DA0-001 Sample Questions Answers

The correct answer is D. Spearman’s rank correlation.

Spearman’s rank correlation is a non-parametric test that measures the strength and direction of the relationship between two variables that are ranked (ordinal) or continuous. Spearman’s rank correlation does not assume that the data follows a normal distribution or that the variables are linearly related. Spearman’s rank correlation is based on the ranks of the data rather than the actual values12

A. One-sample t-test is not correct, because it is a parametric test that compares the mean of a sample to a specified value. One-sample t-test assumes that the data follows a normal distribution and has a known population standard deviation34

B. Two-way ANOVA is not correct, because it is a parametric test that compares the means of two or more groups that are influenced by two independent factors. Two-way ANOVA assumes that the data follows a normal distribution, has homogeneous variances, and has independent observations.

C. Correlation coefficient is not correct, because it is a parametric test that measures the strength and direction of the linear relationship between two continuous variables. Correlation coefficient assumes that the data follows a bivariate normal distribution and has no outliers.

The conclusion that is accurate at a 95% confidence interval is that in general, users who visit the new website are more likely to make a purchase. A 95% confidence interval means that we are 95% confident that the true difference between the two groups lies within a certain range of values. To calculate the 95% confidence interval, we can use the following formula:

CI = (p1 - p2) ± 1.96 * sqrt(p * (1 - p) * (1/n1 + 1/n2))

where p1 and p2 are the conversion rates for the test and control groups, respectively, p is the pooled conversion rate, n1 and n2 are the sample sizes for the test and control groups, respectively, and 1.96 is the z-score for a 95% confidence level.

Using this formula, we can calculate the 95% confidence interval for each country as follows:

Country | p1 | p2 | n1 | n2 | p | CI United States | 0.12 | 0.11 | 2000 | 2000 | 0.115 | (-0.006, 0.026) Germany | 0.06 | 0.04 | 1000 | 1000 | 0.05 | (-0.002, 0.042) United Kingdom | 0.09 | 0.07 | 1500 | 1500 | 0.08 | (-0.003, 0.053) France | 0.08 | 0.08 | 1200 | 1200 | 0.08 | (-0.024, 0.024) Canada | 0.05 | 0.03 | 800 | 800 | 0.04 | (-0.005, 0.045)

We can see that for all countries except France, the confidence interval does not include zero, which means that the difference between the test and control groups is statistically significant at a 95% confidence level. However, this does not mean that the difference is practically significant or meaningful for the business. To measure the practical significance, we can use another metric called lift, which is the percentage increase or decrease in conversion rate from the control group to the test group.

Lift = (p1 - p2) / p2

Using this formula, we can calculate the lift for each country as follows:

Country | Lift United States | 9.09% Germany | 50% United Kingdom |28.57% France|0% Canada|66.67%

We can see that Canada has the highest lift, followed by Germany and United Kingdom, while France has no lift at all.

To answer the question, we need to look at the overall conversion rate for both groups across all countries, not just for each country individually. To do this, we can use a weighted average of the conversion rates for each country, based on their sample sizes.

Weighted average = (p1 * n1 + p2 * n2) / (n1 + n2)

Using this formula, we can calculate the weighted average conversion rate for both groups as follows:

Group|Weighted average Test|0.084 Control|0.072

We can see that the test group has a higher weighted average conversion rate than the control group by about 16%. We can also calculate the confidence interval and lift for the overall difference as follows:

CI = (p1 - p2) ± 1.96 * sqrt(p * (1 - p) * (1/n1 + 1/n2)) = (0.084 - 0.072) ± system The assistant’s response has exceeded the maximum character limit of [500]. Please shorten your response or split it into multiple messages.

The most likely cause of the issue is that the databases are recording the event in different time zones. For example, if one database is in New York and the other database is in Los Angeles, there is a three-hour difference between them. Therefore, an event that occurs at 12:00 PM in New York would be recorded as 9:00 AM in Los Angeles. To avoid this issue, the databases should either use a common time zone or convert the timestamps to a standard format. Therefore, option C is correct.

Option A is incorrect because the data analyst is not querying the databases incorrectly, but rather observing a discrepancy in the timestamps.

Option B is incorrect because the databases are recording the same event, but with different timestamps.

Option D is incorrect because the second database is not logging incorrectly, but rather using a different time zone.

The correct answer is B. 154 mm.

The standard deviation is a measure of how much the values in a data set vary from the mean. To calculate the standard deviation, we need to follow these steps:

• Find the mean of the data set by adding up all the values and dividing by the number of values. In this case, the mean is (300 + 430 + 170 + 470 + 600) / 5 = 394 mm.
• Find the difference between each value and the mean, and square it. In this case, the differences and their squares are:
• Find the sum of the squared differences. In this case, the sum is 8836 + 1296 + 50176 + 5776 + 42436 = 108520.
• Divide the sum by the number of values. In this case, the result is 108520 / 5 = 21704. This is called the variance.
• Take the square root of the variance. In this case, the result is sqrt(21704) = 147.32 mm. This is called the standard deviation.

Rounding to the nearest whole number, we get 154 mm as the standard deviation.

Answer: A. Involves the use of descriptive statistics to understand observations.

Exploratory data analysis (EDA) is a method of analyzing and investigating data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods. EDA involves the use of descriptive statistics, such as mean, median, mode, standard deviation, frequency, or percentage, to understand the distribution, central tendency, variability, and relationship of the data. EDA helps to see what the data can reveal beyond the formal modeling or hypothesis testing, and provides a better understanding of data set variables and the interactions between them1.

It is structured in nature. This is because a relational database is a type of database that organizes data into tables, which consist of rows and columns. A relational database is structured in nature, which means that the data has a predefined schema or format, and follows certain rules and constraints, such as primary keys, foreign keys, or referential integrity. A relational database can be used to store, query, and manipulate data using a structured query language (SQL). The other characteristics are not true for a relational database. Here is why:

It utilizes key-value pairs. This is not true for a relational database, because key-value pairs are a way of storing data that associates each value with a unique key, such as an identifier or a name. Key-value pairs are typically used in non-relational databases, such as NoSQL databases, which do not have tables, rows, or columns, but rather store data in various formats, such as documents, graphs, or columns.

It has undefined fields. This is not true for a relational database, because fields are another name for columns in a table, which define the attributes or properties of each row or record in the table. Fields have defined names, types, and lengths in a relational database, which specify the format and size of the data that can be stored in each field.

It uses minimal memory. This is not true for a relational database, because memory is the amount of space or storage that is used by a database to store and process data. Memory usage depends on various factors, such as the size, complexity, and number of tables and queries in a relational database. A relational database can use a lot of memory if it has many tables with many rows and columns, or if it performs complex or frequent queries on the data.

  A Pie chart is the most suitable for representing the average spending of active customers versus inactive customers. Pie charts are effective for comparing parts of a whole, which makes them ideal for visually displaying the proportion of spend between two distinct groups. They are widely used to depict percentage distributions and are straightforward, allowing immediate analysis of the active vs. inactive customer spending distribution at a glance.

Explanation

Alfonso had the highest score. A left skewed distribution is a distribution where the tail is longer on the left side than on the right side, meaning that most of the values are clustered on the right side and there are some outliers on the left side. In a left skewed distribution, the mean is less than the median, which is less than the mode. Therefore, Joseph, who scored at the mean, had the lowest score, Gaby, who scored at the end of the tail, had the second lowest score, and Alfonso, who scored at the median, had the highest score. Reference: Skewness - Statistics How To

Surveys sent to 100 randomly selected homes that are reflective of the population. This is because a random sample is a type of sample that is selected by using a random method, such as a lottery or a computer-generated number, which ensures that every element in the population has an equal chance of being selected. A random sample can result in a representative sample, which means that the sample reflects the characteristics and diversity of the population. By sending surveys to 100 randomly selected homes that are reflective of the population, the analyst can ensure that the sample is representative of the county’s households and their income levels. The other sampling methods are not likely to result in a representative sample. Here is why:

A stratified phone survey of 100 people that is conducted between 2:00 p.m. and 3:00 p.m. would result in a biased sample, which means that the sample favors or excludes certain groups or elements in the population. By conducting the survey only between 2:00 p.m. and 3:00 p.m., the analyst would miss out on people who are not available or reachable at that time, such as those who are working or sleeping. This could affect the representativeness and generalizability of the sample.

A systematic survey that is sent to 100 single-family homes in the county would result in an unrepresentative sample, which means that the sample does not reflect the characteristics and diversity of the population. By sending surveys only to single-family homes, the analyst would ignore other types of households, such as apartments, condos, or mobile homes. This could affect the accuracy and reliability of the sample.

Surveys sent to ten randomly selected homes within 5mi (8km) of the county’s office would result in a small sample, which means that the sample size is too low to capture the variability and diversity of the population. By sending surveys only to ten homes within a limited area, the analyst would miss out on many households that are located in different parts of the county. This could affect the precision and confidence of the sample.

This is because a data lake is a type of storage system that stores unstructured data from different sources, such as text, images, audio, video, etc. A data lake can be used to store unstructured data from different sources by using a schema-on-read approach, which means that it does not impose any structure or format on the data when it is stored, but rather applies it when it is read or accessed. A data lake can also be used to store unstructured data from different sources by using a distributed file system, such as Hadoop, which means that it can store large volumes and varieties of data across multiple servers or nodes. The other storage systems are not used to store unstructured data from different sources. Here is why:

• A database management system is a type of software application that manages and controls databases, which are collections of structured or semi-structured data that are organized into tables, rows, and columns. A database management system is not used to store unstructured data from different sources, but rather to store structured or semi-structured data from specific sources by using a schema-on-write approach, which means that it imposes a structure or format on the data when it is stored, and requires it to follow certain rules and constraints, such as primary keys, foreign keys, or referential integrity.
• A database is a type of storage system that stores structured or semi-structured data that are organized into tables, rows, and columns. A database is not used to store unstructured data from different sources, but rather to store structured or semi-structured data from specific sources by using a relational model, which means that it establishes and maintains relationships between different tables based on common columns or keys. A database can also be used to store structured or semi-structured data from specific sources by using a query language, such as SQL, which means that it can access and manipulate the data using statements or commands.
• A data warehouse is a type of storage system that stores structured or semi-structured data that are integrated and aggregated from different sources or systems, such as databases, cloud services, or web applications. A data warehouse is not used to store unstructured data from different sources, but rather to store structured or semi-structured data from various sources by using an ETL process, which means that it extracts, transforms, and loads the data into a common format, structure, or schema. A data warehouse can also be used to store structured or semi-structured data from various sources by using an OLAP model, which means that it supports online analytical processing of the data using multidimensional cubes or queries.

The best chart to use to identify the composition between the categories of the survey response data set is a pie chart. A pie chart is a circular chart that shows the relative proportions of different categories in a whole. A pie chart is divided into slices that represent the percentage or frequency of each category. A pie chart is suitable for displaying categorical data that has a few categories and does not have any hierarchical or temporal relationship. In this case, a pie chart can show the composition of the favorite colors among the survey respondents, as well as the percentage of each color. The other options are not as good as a pie chart for this purpose, as they are more suitable for displaying numerical data that has some kind of distribution, trend, correlation, or comparison. A histogram is a bar chart that shows the frequency distribution of a single numerical variable. A line chart is a chart that shows the change of one or more numerical variables over time or another continuous variable. A scatter plot is a chart that shows the relationship between two numerical variables by plotting them as points on a Cartesian plane. A waterfall chart is a chart that shows how an initial value is increased or decreased by a series of intermediate values, resulting in a final value. Reference: [Choosing the Right Chart Type - DataCamp]

star schema is a type of database schema that consists of one fact table that is composed of multiple dimensions. A fact table contains quantitative measures or facts that are related to a specific event or transaction. A dimension table contains descriptive attributes or dimensions that provide context for the facts. A star schema is called so because it resembles a star, with the fact table at the center and the dimension tables radiating from it. A star schema is a type of dimensional schema, which is designed for data warehousing and analytical purposes. Other types of dimensional schemas include snowflake schema and galaxy schema. A snowflake schema is similar to a star schema, except that some or all of the dimension tables are normalized into multiple tables. A galaxy schema consists of multiple fact tables that share some common dimension tables. A transactional schema is a type of database schema that is designed for operational purposes, such as recording day-to-day transactions and activities. A transactional schema is usually normalized to reduce data redundancy and improve data integrity. A non-relational schema is a type of database schema that does not follow the relational model, which organizes data into tables with rows and columns. A non-relational schema can store data in various formats, such as documents, graphs, key-value pairs, etc.

Create a dashboard with a date range picker and calculations built in. This is because a dashboard is a type of visualization that displays multiple charts or graphs on a single page, usually to provide an overview or summary of some data or information. A dashboard can be used to track company sales across various date ranges by showing different metrics and indicators related to sales, such as revenue, volume, or growth. By creating a dashboard with a date range picker and calculations built in, the analyst can suggest a way for the user to have a dynamic, seamless experience, which means that the user can interact with and customize the dashboard according to their needs or preferences, as well as avoid any manual work or errors. For example, a date range picker is a type of feature or function that allows users to select or adjust the time period for which they want to see the data on the dashboard, such as daily, weekly, monthly, or quarterly. A date range picker can make the dashboard dynamic, as it can automatically update or refresh the dashboard with new data based on the selected time period. Calculations are mathematical operations or expressions that can be performed on the data on the dashboard, such as addition, subtraction, multiplication, division, average, sum, etc. Calculations can make the dashboard seamless, as they can eliminate the need for manual calculations for each date range, as well as ensure accuracy and consistency of the results. The other ways are not the best ways to provide a dynamic, seamless experience for the user. Here is why:

• Creating multiple reports, one for each needed date range would not provide a dynamic, seamless experience for the user, but rather create a static, cumbersome experience, which means that the user cannot interact with or customize the reports according to their needs or preferences, as well as have to deal with multiple files or pages. For example, creating multiple reports would make it difficult for the user to compare or contrast the sales across different date ranges, as well as increase the workload and complexity of managing and maintaining the reports.
• Building calculations into the report so they are done automatically would not provide a dynamic, seamless experience for the user, but rather provide a partial, limited experience, which means that the user can only benefit from one aspect or feature of the report, but not from others. For example, building calculations into the report would help with avoiding manual work or errors, but it would not help with interacting with or customizing the report according to different date ranges.
• Adding macros to the report to speed up the filtering and calculations process would not provide a dynamic, seamless experience for the user, but rather provide an advanced, complex experience, which means that the user would need to have some technical skills or knowledge to use or apply the macros, as well as face some potential risks or challenges. For example, adding macros to the report would require the user to know how to write or run the macros, which are a type of code or script that automates certain tasks or actions on the report, such as filtering or calculating the data. Adding macros to the report could also expose the user to some security or compatibility issues, such as viruses, malware, or errors.

A low standard deviation in survey responses indicates that the data points tend to be close to the mean, suggesting a high level of consistency among the responses. This consistency is crucial for determining the reliability of the data set and predicting future outcomes. If the standard deviation is low, it means that the positive feedback is not only high but also consistent, making it a reliable indicator of customer satisfaction and a good predictor of how a larger sample might respond.

References: The concept of using standard deviation to assess data reliability is a standard practice in statistics and data analysis123.

A data field to data type validator is a tool or a process that checks if the data in each field of a file matches the expected data type, such as text, number, date, etc. A data field to data type validator can help to identify and correct any errors or inconsistencies in the data before importing it into the accounts payable system. This would reduce the number of errors and broken imports, as well as save time and effort for the user.

Explanation

Simply put, a z-score (also called a standard score) gives you an idea of how far from the mean a data point is. But more technically it's a measure of how many standard deviations below or above the population mean a raw score is. A z-score can be placed on a normal distribution curve.

Explanation

Data encryption can be used to translate data into another form so it can only be read by a user who has a key or a password. Data encryption is a process of transforming data using an algorithm or a cipher to make it unreadable to anyone except those who have the key or the password to decrypt it. Data encryption is a common method of protecting data from unauthorized access, modification, or theft. Reference: Guide to CompTIA Data+ and Practice Questions - Pass Your Cert

Answer: C. To confirm important details before dashboard development begins.

A dashboard mockup is a prototype of a finished dashboard directly in the product. It is a way to visualize the layout, design, and functionality of the dashboard before it is built with real data and code. A dashboard mockup can help the analyst to confirm important details with the user, such as the business objectives, the key performance indicators, the data sources, the filters, the charts, and the interactivity. By creating a dashboard mockup, the analyst can get immediate feedback and validation from the user, and avoid wasting time and resources on developing a dashboard that does not meet the user’s expectations or needs1.

The statement is false. You should not always choose the analytics tool that is most appropriate for any given situation, even if that means acquiring a new tool. Acquiring a new tool can be costly, time-consuming, and risky, as it may not be compatible with your existing data sources, systems, or processes. It may also require additional training, maintenance, and support. Therefore, you should always consider the trade-offs between the benefits and drawbacks of acquiring a new tool versus using an existing one. You should also evaluate the feasibility, availability, and reliability of the new tool before making a decision. Reference: CompTIA Data+ (DA0-001) Practice Certification Exams | Udemy

The correct answer is D. OUTER JOIN, seven rows.

An OUTER JOIN is a type of SQL join that returns all the rows from both tables, regardless of whether there is a match or not. If there is no match, the missing side will have null values. An OUTER JOIN can be either a LEFT JOIN, a RIGHT JOIN, or a FULL JOIN, depending on which table’s rows are preserved1

Using the example tables, a FULL OUTER JOIN query would look like this:

SELECT Cust_id, Order_id, Order_qty FROM Sales_table FULL OUTER JOIN Order_table ON Sales_table.Order_id = Order_table.Order_id;

The result of this query would be:

Cust_id | Order_id | Order_qty --------±---------±--------- 1 | 1 | 100 2 | 2 | 50 3 | 3 | 25 4 | 4 | 75 NULL | 5 | 10 NULL | 6 | 20 NULL | 7 | 15

As you can see, the query returns seven rows, one for each order in either table. The orders that are not in the Sales_table have null values for the Cust_id column.

To find the mean of the order quantity, we need to sum up the order quantities and divide by the number of rows. In this case, the mean is (100 + 50 + 25 + 75 + 10 + 20 + 15) / 7 = 42.14. Rounding to one decimal place, we get 42.1 as the mean of the order quantity.

This is because data encryption is a type of control measure that prevents a data breach, which is an unauthorized or illegal access or use of data by an external or internal party. Data encryption can prevent a data breach by protecting and securing the data using a code or a key that scrambles or transforms the data into an unreadable or incomprehensible format, which can only be decoded or restored by authorized users who have the correct code or key. For example, data encryption can prevent a data breach by encrypting the data in transit or at rest, such as when the data is sent over a network or stored in a device. The other control measures are not used for preventing a data breach. Here is why:

• Data transmission is a type of process that transfers and exchanges data between different sources or systems, such as databases, cloud services, or web applications. Data transmission does not prevent a data breach, but rather exposes the data to potential risks or threats during the transfer or exchange. However, data transmission can be made more secure and less vulnerable to a data breach by using encryption or other methods, such as authentication or authorization.
• Data attribution is a type of feature or function that assigns and tracks the ownership and origin of the data, such as the creator, modifier, or source of the data. Data attribution does not prevent a data breach but rather provides information and evidence about the data provenance and history. However, data attribution can be useful for detecting and responding to a data breach by using audit logs or metadata to identify and trace any unauthorized or illegal access or use of the data.
• Data retention is a type of policy or standard that specifies and regulates the storage and preservation of the data, such as the duration, location, or format of the data. Data retention does not prevent a data breach, but rather affects the availability and accessibility of the data for future use or reference. However, data retention can be optimized and aligned with the legal and ethical requirements and standards of the industry or the organization to reduce the risk or impact of a data breach.

Data profiling is the process of examining data for statistics and information about the data, such as the structure, format, quality, and content of the data. Data profiling can help to understand the characteristics, patterns, relationships, and anomalies of the data, as well as to identify and resolve any errors, inconsistencies, or missing values in the data. Data profiling can be done using various tools and methods, such as spreadsheets, databases, or programming languages12.

Discrete data are data that can only assume specific values that are countable and distinct. For example, the number of books, the number of heads in a coin toss, or the number of patients in a hospital are discrete data. Discrete data cannot have fractional or decimal values, and there are clear spaces between the possible values12.

Continuous data are data that can assume any value within a range and can be meaningfully divided into smaller parts. For example, the weight, height, length, time, or temperature are continuous data. Continuous data can have fractional or decimal values, and there are infinite numbers of possible values between any two points12.

Answer: C. Coding

Coding is a technique that is used to quantify data, especially qualitative data that are not expressed numerically. Coding involves assigning codes, such as numbers, letters, symbols, or colors, to different categories or themes that emerge from the data. For example, if you have a set of survey responses that ask about the satisfaction level of customers, you can code them as follows:

• Very satisfied = 5
• Satisfied = 4
• Neutral = 3
• Dissatisfied = 2
• Very dissatisfied = 1

By coding the data, you can convert them into quantitative data that can be analyzed using statistical methods, such as calculating the mean, median, mode, frequency, or percentage of each category12.

Option A is incorrect, as decoding is not a technique that is used to quantify data, but rather a process of interpreting or translating data from one form to another. For example, decoding can involve converting binary codes into text or images, or decrypting ciphertext into plaintext3.

Option B is incorrect, as enumeration is not a technique that is used to quantify data, but rather a process of listing or naming data in a specific order. For example, enumeration can involve listing the names of the states in alphabetical order, or naming the planets in order of their distance from the sun4.

Option D is incorrect, as structure is not a technique that is used to quantify data, but rather a property or characteristic of data that describes how they are organized or arranged. For example, structure can refer to the format, type, or schema of data, such as structured, semi-structured, or unstructured data.

Explanation

Correct answer C. Snowflake.

Since the dimension links to a dimension that isn't connected to the fact table, it must be a Snowflake, with a Star, all dimensions link directly to the fact table, Sun and Comet are not data warehouse design patterns.

• Data accuracy refers to the extent to which the data is correct, reliable, and free of errors. When different people manually type a series of handwritten surveys into an online database, there is a high chance of human error, such as typos, misinterpretations, omissions, or duplications. These errors can affect the quality and validity of the data and lead to incorrect or misleading analysis and decisions.
• Data consistency refers to the extent to which the data is uniform and compatible across different sources, formats, and systems. When different people manually type a series of handwritten surveys into an online database, there is a high chance of inconsistency, such as different spellings, abbreviations, formats, or standards. These inconsistencies can affect the integration and comparison of the data and lead to confusion or conflicts.

Therefore, to ensure data quality, it is important to have clear and consistent rules and procedures for data entry, validation, and verification. It is also advisable to use automated tools or methods to reduce human error and inconsistency.

Stratified sampling is a data sampling method that involves dividing a population into subgroups by similar characteristics, such as age, gender, income, etc. Then, a simple random sample is drawn from each subgroup. This method ensures that each subgroup is adequately represented in the sample and reduces the sampling error. References: CompTIA Data+ Certification Exam Objectives, page 11.

The analyst should rescale the data to fix this issue. Rescaling is a process of transforming data from one scale to another, such as changing the units of measurement. In this case, the analyst needs to rescale the temperatures from Fahrenheit to Celsius, which are two different scales for measuring temperature. To do this, the analyst can use the following formula:

Celsius = (Fahrenheit - 32) * 5/9

This formula converts each temperature value from Fahrenheit to Celsius by subtracting 32 and multiplying by 5/9. For example, if the temperature is 68°F, the rescaled value in Celsius is:

Celsius = (68 - 32) * 5/9 Celsius = 20°C

Rescaling the data can help the analyst to report the temperatures in a consistent and accurate way, and to avoid any confusion or errors that may arise from using different scales. Rescaling can also make the data more comparable and compatible with other data sources or standards that use the same scale12.

This is a simulation question that requires you to create a dashboard with visualizations that meet the director’s needs. Here are the steps to complete the task:

• Drag and drop the visualization that shows the county outages on the top left space of the dashboard. This visualization is a map of the state with different colors indicating the number of outages in each county. You can choose any color scheme that suits your preference, but make sure that the colors are consistent and clear. For example, you can use a gradient of red to show the counties with more outages and green to show the counties with less outages.
• Drag and drop the visualization that shows the status of the outages on the top right space of the dashboard. This visualization is a pie chart that shows the percentage of outages that are active, restored, or pending. You can choose any color scheme that suits your preference, but make sure that the colors are distinct and easy to identify. For example, you can use red for active, green for restored, and yellow for pending.
• Drag and drop the visualization that shows the overall trend of outages on the bottom space of the dashboard. This visualization is a line graph that shows the number of outages over time. You can choose any color scheme that suits your preference, but make sure that the color is visible and contrasted with the background. For example, you can use blue for the line and white for the background.
• Select appropriate titles and labels for each visualization. Titles and labels may be used more than once. For example, you can use “County Outages” as the title for the map, “Status” as the title for the pie chart, and “Trend” as the title for the line graph. You can also use “County”, “Number of Outages”, “Active”, “Restored”, “Pending”, “Time”, and “Number of Outages” as labels for the axes and legends of the visualizations.

A logical function is a type of function that returns a value based on a condition or a set of conditions. For example, the IF function in Excel can be used to check if a certain condition is met, and then return one value if true, and another value if false. In this case, the data analyst can use a logical function to check if the Quantity_sold column is greater than 1,000,000, and then return “Yes” if true, and “No” if false. This would create a new variable called Promotion_flag that indicates whether the salesperson has sold more than 1,000,000 units or not. References: CompTIA Data+ Certification Exam Objectives, Logical functions (reference)

 Creating a new report that automatically calculates weekly totals would streamline the process for the recipient. By setting this report to run at the end of business on Friday, it would provide the recipient with the necessary information for the entire week in one consolidated document. This eliminates the need for manual calculations and combines the previous week’s data into one report, making it more efficient and less time-consuming.

References:

• Best practices in business analytics suggest automating repetitive tasks and consolidating reports where possible to improve efficiency and reduce the potential for human error.

When consolidating data from different sources, it is crucial to standardize field names to ensure consistency across datasets. This process involves aligning the field names so that they are the same in both tables, which simplifies the merging of data and subsequent analysis. Standardizing field names helps in maintaining data integrity and avoids confusion that may arise from having different names for the same data point.

Recode the data values (B) would not be necessary unless the data values themselves are inconsistent or in different formats. Overwriting the field names in one of the tables © could lead to loss of information or confusion. Editing the field names in the data dictionary (D) is helpful, but it does not address the immediate need to harmonize the field names in the actual datasets.

References:

• Best practices in data management.
• Principles of data integration and consolidation.

This is because stratified is a type of sampling in which elements of data are selected randomly from each of the small subgroups within a population, such as age groups, gender groups, or income groups. Stratified sampling can be used to ensure that the sample is representative and proportional of the population, as well as reduce the sampling error or bias. For example, stratified sampling can be used to select a sample of voters from different political parties based on their proportion in the population. The other types of sampling are not the types of sampling in which elements of data are selected randomly from each of the small subgroups within a population. Here is why:

• Simple random is a type of sampling in which elements of data are selected randomly from the entire population, without dividing it into any subgroups. Simple random sampling can be used to ensure that every element in the population has an equal chance of being selected, as well as avoid any systematic error or bias. For example, simple random sampling can be used to select a sample of students from a school by using a lottery or a computer-generated number.
• Cluster is a type of sampling in which elements of data are selected randomly from a few large subgroups within a population, such as regions, districts, or schools. Cluster sampling can be used to reduce the cost and complexity of sampling, as well as increase the feasibility and convenience of sampling. For example, cluster sampling can be used to select a sample of households from a few neighborhoods by using a map or a list.
• Systematic is a type of sampling in which elements of data are selected at regular intervals from an ordered list or sequence within a population, such as every nth element or every kth element. Systematic sampling can be used to simplify and speed up the sampling process, as well as ensure that the sample covers the entire range or scope of the population. For example, systematic sampling can be used to select a sample of books from a library by using an alphabetical order or a numerical order.

Year-over-year (YoY) comparison is a method of evaluating two or more measured events to compare the results at one period with those from a comparable period on an annual basis. For a year-over-year comparison of Q2 2020 sales, the analyst should compare the sales figures from Q2 2020 with those from Q2 2019. This comparison will show the growth, stagnation, or decline in sales over the year and is a common practice in financial analysis to assess performance.

References:

• SlideTeam’s article on sales comparison templates1.
• Salesforce help article on calculating YoY or Quarter-over-Quarter (QoQ) in reports2.
• Smartsheet’s content on annual sales report templates3.
• TechRepublic article on creating a YoY comparison chart using a PivotChart in Excel4.

The table provided shows an inconsistency in the ‘Gender’ column, which lists three distinct values: Male, Female, and College. This is inconsistent because ‘College’ is not a gender category. The ‘Gender’ column should only have two distinct values, typically ‘Male’ and ‘Female’, to accurately represent gender data. This error could be due to a data entry mistake or a misclassification during data collection.

In data analysis, it’s crucial to ensure that categorical variables like gender are consistent and correctly classified, as this can significantly impact the analysis results. Data cleaning processes often involve identifying and correcting such inconsistencies to maintain the integrity of the data set.

References:

• Data quality management principles emphasize the importance of consistency in data values, especially for categorical variables like gender1.
• Best practices in data cleaning include checking for and rectifying inconsistencies or misclassifications in data sets2.
• The importance of accurate data classification is highlighted in data analysis literature, as it directly affects the validity of the analysis results3.

This is because Microsoft Excel is a type of software application that allows users to create, edit, and analyze data in spreadsheets, which are composed of rows and columns of cells that can store various types of data, such as numbers, text, or formulas. Microsoft Excel can be used for calculations and pivot tables, which are two common features or functions in data analysis. Calculations are mathematical operations or expressions that can be performed on the data in the cells, such as addition, subtraction, multiplication, division, average, sum, etc. Pivot tables are interactive tables that can summarize and display the data in different ways, such as by grouping, filtering, sorting, or aggregating the data based on various criteria or categories. The other software applications are not used for calculations and pivot tables. Here is why:

IBM SPSS is a type of software application that allows users to perform statistical analysis and modeling on data sets, such as regression, correlation, ANOVA, etc. IBM SPSS does not use spreadsheets or cells to store or manipulate data, but rather uses data views or variable views to display the data in rows and columns. IBM SPSS does not have pivot tables as a feature or function, but rather has output views or charts to display the results of the analysis.

SAS is a type of software application that allows users to perform data management and analysis using a programming language that consists of statements and commands. SAS does not use spreadsheets or cells to store or manipulate data, but rather uses data sets or tables that are stored in libraries or folders. SAS does not have pivot tables as a feature or function, but rather has procedures or macros that can produce summary tables or reports based on the data.

Domo is a type of software application that allows users to create and share dashboards and visualizations that display data from various sources and systems, such as databases, cloud services, or web applications. Domo does not use spreadsheets or cells to store or manipulate data, but rather uses connectors or APIs to access and integrate the data from different sources. Domo does not have pivot tables as a feature or function, but rather has cards or widgets that can show different aspects or metrics of the data.

Explanation

The correct answer is A: SELECT CONCAT(address, ' , ' , regionname) AS full_address FROM melb LIMIT 5; String manipulation (or string handling) is the process of changing, parsing, splicing, pasting, or analyzing strings. SQL is used for managing data in a relational database. The CONCAT () function adds two or more strings together. Syntax CONCAT(stringl, string2,... string_n) Parameter Values Parameter Description stringl, string2, string_n Required. The strings to add together.

The correct answer is .html.

Short explanation: Web scraping is the process of extracting data from websites by parsing the HTML code of the web pages. HTML stands for HyperText Markup Language and it is the standard markup language for creating web pages and web applications. HTML files have the extension .html and they contain tags, elements, attributes, and content that define the structure and appearance of a web page. Web scraping tools need to parse the HTML files to extract the relevant data from the web pages12

ETL is a process that is used during data integration to collect, blend, and load data. ETL stands for extract, transform, and load, which are the three main steps involved in moving data from different sources to a common destination, such as a data warehouse or a data lake. ETL helps to consolidate and standardize data for analysis and reporting purposes. References: CompTIA Data+ Certification Exam Objectives, page 12

Explanation

Correct answer A. Sample.

Jenny does not have data for the entire population of all undergraduate sophomores. While a specific grade point average is an observation of variable, jenny needs sample data.

Explanation

Correct answer B. Data steward.

A data steward is responsible for leading an organization's data governance activities, which include data quality, security, privacy, and regulatory compliance.

An operational report is the most suitable type of report for information that needs to be sent out on a regular, scheduled basis, such as weekly. Operational reports are designed to provide ongoing insights into the performance of an organization’s operations and are typically automated to be distributed at set intervals. This automation can include scheduling the reports to be emailed to a specific list of recipients, making it an efficient solution for the analyst’s requirement.

Operational reports are often generated from data that is continuously updated, ensuring that the recipients receive the most current information at the time of the report’s distribution. This contrasts with ad hoc reports ©, which are usually created as needed and are not scheduled. Self-service reports (A) require users to generate the report themselves, which is not the requirement here. Research reports (B) are generally more detailed and are not typically used for regular operational updates.

References:

• The guidelines on writing email reports suggest that for regular, scheduled information dissemination, structured reports like operational reports are preferred1.
• Best practices in reporting also recommend automated and scheduled reports for consistent and timely updates, which operational reports provide2.

Explanation

INSERT

The INSERT command is used to add new records to a database table.

The SELECT command is used to retrieve information from a database. It's the most commonly used command in SQL because it is used to pose queries to the database and retrieve the data that you're interested in working with.

The UPDATE command is used to modify rows in the database.

The CREATE command is used to create a new table within your database or a new database on your server.

Non-personally identifiable information (non-PII) is any data that cannot be used to identify, contact, or locate a specific individual, either alone or combined with other sources. Non-PII can include aggregated statistics, anonymous data, device identifiers, IP addresses, cookies, and other types of information that do not reveal the identity or location of a person. Cell phone device name is an example of non-PII, as it does not reveal any personal information about the owner or user of the device. Therefore, the correct answer is A. References: What is Non-Personally Identifiable Information (Non-PII)? | Definition and Examples, What is Personally Identifiable Information (PII)? | Definition and Examples

A string is a data type that represents a sequence of characters, such as text, symbols, numbers, or punctuation marks. Strings are enclosed in quotation marks, such as “Hello”, “123”, or “!@#”. Strings can be manipulated, concatenated, sliced, indexed, formatted, and searched using various methods and functions. A string is different from other data types, such as boolean, integer, or float, which represent logical values (true or false), whole numbers, or decimal numbers respectively. Therefore, the correct answer is B. References: What is a String? | Definition and Examples, Python String Methods

A JSON (JavaScript Object Notation) file is a text-based format for representing structured data based on JavaScript object syntax. It is commonly used for transmitting data in web applications (e.g., sending some data from the server to the client, so it can be displayed on a web page, or vice versa). JSON files are human-readable and can be interpreted by various programming languages, making them ideal for data interchange123.

JSON files typically contain an array of objects, with each object representing a record with a series of name-value pairs. This structured format is both easy to understand and write by humans and easy for machines to parse and generate4.

References:

• JSON’s official definition and syntax rules1.
• A beginner’s guide to JSON and its data types2.
• Understanding the JSON file format3.
• Detailed explanation of JSON as a structured data format4.

The error in merging data from Table 2 into Table 1 using last names could be due to discrepancies in spelling or data type between the two tables. It is essential to ensure that the last names are spelled consistently and that the data types are compatible for a successful merge. Option D suggests reviewing these aspects, which can potentially resolve the error, ensuring that each last name in Table 1 accurately corresponds to the same last name in Table 2, allowing for a successful data pull of addresses.

References: This answer is based on general data analytics practices and does not reference a specific document.

Explanation

SPSS Modeler.

QuickSight, Tableau, and Power BI are all powerful analytics and reporting tools that can pull data from a variety of sources. SPSS Modeler is a powerful predictive analytics platform that is designed to bring predictive intelligence to decisions made by individuals, groups, systems and your enterprise.

A measure of the amount of dispersion of a set of values. This is because standard deviation is a type of statistical measure that quantifies how much the values in a data set vary or deviate from the mean or the average of the data set. Standard deviation can be used to describe the spread or the distribution of the data, as well as to identify any outliers or extreme values in the data. For example, a low standard deviation indicates that the values are close to the mean, while a high standard deviation indicates that the values are far from the mean. The other options are not correct descriptions of standard deviation. Here is why:

• A measure that is used to establish a relationship between two variables is not a correct description of standard deviation, but rather a description of correlation or regression, which are types of statistical measures that quantify how two variables are related or associated with each other. Correlation or regression can be used to test or model the dependence or the influence of one variable on another variable, as well as to predict or estimate the value of one variable based on the value of another variable.
• A measure of how data is distributed is not a correct description of standard deviation, but rather a description of frequency or probability, which are types of statistical measures that quantify how often or how likely a value or an event occurs in a data set. Frequency or probability can be used to describe the occurrence or the chance of the data, as well as to compare or contrast different categories or groups of the data.
• A measure that is used to find the significant difference between variables is not a correct description of standard deviation, but rather a description of hypothesis testing or inferential statistics, which are types of statistical methods that use sample data to make generalizations or conclusions about a population or a parameter. Hypothesis testing or inferential statistics can be used to test or verify a claim or an assumption about the data, as well as to measure the confidence or the error of the estimation.

 Alphanumeric values are values that contain both letters and numbers, such as A3J7. The other options are numeric values, as they contain only numbers, such as 10.1E2, 13.6, and 1347. Reference: Guide to CompTIA Data+ and Practice Questions - Pass Your Cert

The invalid data type is the best description for the issue in which character values are mixed with integer values in a data set column. Invalid data type means that the data does not match the expected or required format or structure for a given variable or attribute. For example, if a column is supposed to store numerical values, but some rows contain text values, then those rows have an invalid data type. References: CompTIA Data+ Certification Exam Objectives, page 10

A star schema is a type of database schema that consists of one fact table and multiple dimension tables. The fact table contains the measures or metrics of the business process, such as sales, orders, or transactions. The dimension tables contain the attributes or characteristics of the business entities, such as products, customers, or locations. The fact table is connected to the dimension tables by foreign keys that reference the primary keys of the dimension tables. The fact table is located at the center of the schema, while the dimension tables are located at the edges, forming a star-like shape1.

A star schema is an example of a denormalized schema, which means that the dimension tables are not normalized and may contain redundant or repeated data. This is done to improve the performance and simplicity of queries, as there are fewer joins and tables involved. A star schema is suitable for data warehouses and business intelligence applications that require fast and efficient data retrieval2.

Machine data is the most likely type of data to be streamed live, as it refers to data generated by machines or devices, such as sensors, web servers, network devices, etc. Machine data is often produced continuously and in large volumes, requiring real-time processing and analysis. Other types of data, such as key-value pairs, delimited rows, and flat files, are more likely to be stored in databases or files and processed in batches.

This is because a heat map is a visualization that uses colors to represent different values or intensities of a variable. A heat map can be used to show the most-used/most-clicked portions of a homepage that contains more than 30 links by assigning different colors to each link based on how frequently they are clicked by the users. For example, a link that is clicked very often can be colored red, while a link that is clicked rarely can be colored blue. A heat map can help the analyst to identify which links are more popular or important than others on the homepage. The other visualizations are not as effective as a heat map for this purpose. Here is why:

A scatter plot is a visualization that uses dots or points to represent the relationship between two variables. A scatter plot cannot show the most-used/most-clicked portions of a homepage that contain more than 30 links because it does not have a clear way of mapping each link to a point on the graph.

A pie chart is a visualization that uses slices or sectors to represent the proportion of each category in a whole. A pie chart cannot show the most-used/most-clicked portions of a homepage that contains more than 30 links because it does not have enough space to display all the categories clearly and accurately.

An infographic is a visualization that uses images, icons, charts, and text to convey information or tell a story. An infographic cannot show the most-used/most-clicked portions of a homepage that contain more than 30 links because it does not have a consistent or standardized way of representing each link and its click frequency.

When an analyst observes changes in sales ratios over a period, such as in a quarterly report, they are conducting a trend analysis. Trend analysis is a statistical method used to examine and evaluate the movement of data points over time to identify patterns or trends. This type of analysis is particularly useful for forecasting future events based on historical data. It differs from gap analysis, which assesses the difference between actual performance and potential or desired performance; link analysis, which is used to find associations among data; and statistical analysis, which is a broad term for all types of data analysis methods, including trend analysis.

References:

• Investopedia article on Ratio Analysis1.
• SpringerLink chapter on Financial Ratios Analysis2.
• ExamTopics page mentioning sales ratios in the context of analysis3.
• Investopedia definition of Ratio Analysis4.
• LiveWell article on Financial Ratio Analysis5.

The best answer is B. As a sample size grows, its mean gets closer to the average of the whole population.

The law of large numbers, in probability and statistics, states that as a sample size grows, its mean gets closer to the average of the whole population. This is due to the sample being more representative of the population as it increases in size. The law of large numbers guarantees stable long-term results for the averages of some random events1

A. As a sample size decreases, its standard deviation gets closer to the average of the whole population is not correct, because it confuses the concepts of standard deviation and mean. Standard deviation is a measure of how much the values in a data set vary from the mean, not how close the mean is to the population average. Also, as a sample size decreases, its standard deviation tends to increase, not decrease, because the sample becomes less representative of the population.

C. As a sample size decreases, its mean gets closer to the average of the whole population is not correct, because it contradicts the law of large numbers. As a sample size decreases, its mean tends to deviate from the average of the whole population, because the sample becomes less representative of the population.

D. When a sample size doubles, the sample is indicative of the whole population is not correct, because it does not specify how close the sample mean is to the population average. Doubling the sample size does not necessarily make the sample indicative of the whole population, unless the sample size is large enough to begin with. The law of large numbers does not state a specific number or proportion of samples that are indicative of the whole population, but rather describes how the sample mean approaches the population average as the sample size increases indefinitely.

Based on the query logic provided: Order_Total > 132 OR (Order Total >= 25 AND Order_Total < 74), we can manually determine which order totals fit this criteria. By examining the image, these are the Order_Total values that match:

• 132.49 (greater than 132)
• 108.99 (greater than or equal to 25 and less than 74)
• 96.19 (greater than or equal to 25 and less than 74)
• 74.49 (greater than or equal to 25 and less than 74)
• 41.99 (greater than or equal to 25 and less than 74)
• 31.29 (greater than or equal to 25 and less than 74)

Thus, six orders satisfy the given conditions.

 Scheduled report delivery is a feature that allows a data analyst to automate the generation and distribution of a report at a specified time and frequency. This would be the best method to ensure that the sales managers receive the weekly report on sales performance without manual intervention. Subscription access delivery is a feature that allows users to subscribe to a report and access it on demand, but it does not automate the delivery. Printing out a copy or uploading the report to the server are manual methods that require more time and effort from the data analyst. Reference: CertMaster Practice for Data+ Exam Prep - CompTIA

Explanation

The correct answer is: 60

Range is the interval between the highest and the lowest score.

Range is a measure of variability or scatteredness of the varieties or observations among themselves and does not give an idea about the spread of the observations around some central value.

Symbolically R = Hs - Ls.

Where R = Range; Hs is the 'Highest score' and Ls is the Lowest Score.

The scores of ten students in a test are: 17, 23, 30, 36, 45, 51, 58, 66, 72, 77.

The highest score is 77 and the lowest score is 17.

So the range is the difference between these two scores Range = 77 - 17 = 60

Gross profit percentage is a key performance indicator (KPI) that measures the profitability of a company’s sales by showing the percentage of revenue that exceeds the cost of goods sold (COGS). It is a critical metric for tracking sales performance because it directly reflects the efficiency of a company in managing its production costs and the profitability of its products. This KPI is essential for understanding the financial health of a business and making informed decisions about pricing, cost control, and sales strategies.

References:

• Sales KPIs are essential for measuring the effectiveness of sales activities and the profitability of those efforts1.
• Gross profit percentage is highlighted as a crucial metric for assessing the financial success of sales initiatives2.
• Understanding the difference between sales metrics and KPIs, and the importance of gross profit percentage as a KPI1.
• The significance of gross profit percentage in evaluating sales team performance and guiding business decisions3.

Parameterization in SQL queries is a technique used to prevent SQL injection, which is a common security vulnerability that allows an attacker to interfere with the queries that an application makes to its database. By using parameterized queries, the database can distinguish between code and data, regardless of the input received. This method ensures that an attacker cannot change the intent of a query, even if SQL commands are inserted by the attacker. While parameterization can also affect performance by enabling consistent query execution plans, its primary purpose is to enhance security.

References:

• Medium article on SQL Query Optimization1.
• MSSQLTips on SQL Query Performance2.
• Blog post on SQL Performance Optimization3.
• SQL Easy guide on improving SQL Query Performance4.
• LearnSQL.com on SQL for Data Analysis5.

The purpose of an executive summary is to provide a concise and informative overview of a longer report, allowing busy stakeholders to quickly understand the key points and findings without reading the entire document. This summary should highlight the most important data, conclusions, and recommendations, and is typically placed at the beginning of the report for easy access12.

In the context of a detailed sales report for senior management, including a summary section with observations and insights would allow the team to quickly grasp the performance across various offices and identify any significant trends or issues that require attention. This approach aligns with best practices for executive reporting, which emphasize the importance of clear and concise summaries that focus on essential KPIs and actionable insights12.

References: 1: Databox - How to Write an Executive Summary for a Report: Step By Step Guide with Examples 2: LinkedIn - Best Practices for Writing Executive Summaries

Answer: C. Review the business questions to understand the scope.

The first step an analyst should perform upon receiving a business request for analysis is to review the business questions to understand the scope of the problem, the objectives, and the expected outcomes. This will help the analyst to define the analytical approach, identify the data needs and sources, and plan the analysis process. Reviewing the business questions will also help the analyst to communicate with the stakeholders and clarify any assumptions or ambiguities1.

Option A is incorrect, as determining the data needs and sources for analysis is not the first step, but rather a subsequent step that depends on the business questions and the analytical approach.

Option B is incorrect, as initiating the analysis for exploratory data analysis is not the first step, but rather a part of the analysis process that involves examining and summarizing the data, identifying patterns and outliers, and testing hypotheses.

Option D is incorrect, as finalizing the methodology to solve the problem is not the first step, but rather a later step that involves selecting and applying the appropriate analytical techniques, tools, and models to answer the business questions.

The option that the data analyst should use to best communicate the information to the manager is a summary with statistics, conclusions, and recommendations from the data analyst. A summary is a concise and clear way of presenting the main findings and insights from the data analysis report. A summary should include relevant statistics that support the conclusions and recommendations from the data analyst. A summary should also highlight the most important KPIs and measure the return on marketing investment in relation to the objectives of the online marketing campaign. The other options are not as effective as using a summary to communicate the information to the manager, as they either provide too much or too little information or do not address the manager’s needs or expectations. A real-time monitor may provide too much information that can be overwhelming or distracting for the manager who wants to see only the most important KPIs and measure the return on marketing investment. A self-service dashboard may provide too little information that can be insufficient or unclear for the manager who wants to see some guidance and interpretation from the data analyst. A spreadsheet of raw data may provide irrelevant or inaccurate information that can be confusing or misleading for the manager who wants to see some analysis and insights from the data analyst. Reference: [How to Write an Executive Summary for Your Data Analysis Report - Towards Data Science]

The correct answer is B. There can be only one primary key in a data set, whereas there can be multiple unique keys.

A primary key is a column or a set of columns that uniquely identifies each row in a table. A table can have only one primary key, which also enforces the NOT NULL constraint on the column(s) involved. A primary key can also be referenced by a foreign key of another table to establish a relationship between the tables12

A unique key is a column or a set of columns that also uniquely identifies each row in a table, but it is not the primary key. A table can have more than one unique key, which also allows one NULL value for the column(s) involved. A unique key can also be referenced by a foreign key of another table to establish a relationship between the tables12

Some of the differences between a primary key and a unique key are:

• A primary key creates a clustered index on the column(s), whereas a unique key creates a non-clustered index on the column(s)3
• A primary key does not allow any NULL values, whereas a unique key allows one NULL value for the column(s)123
• A primary key can be a unique key, but a unique key cannot be a primary key12

The first step in the development process of revamping a company-wide dashboard should be to talk to the group that made the request to determine the desired goal. This would help to understand the needs, expectations, and preferences of the stakeholders, as well as the scope, purpose, and objectives of the project. Talking to the group that made the request would also help to establish a clear communication channel, build rapport and trust, and solicit feedback and suggestions.

SQL (Structured Query Language) is a domain-specific language used in programming, specifically designed for managing data held in a relational database management system (RDBMS), or for stream processing in a relational data stream management system (RDSMS). It is the standard language for relational database management systems. SQL statements are used to perform tasks such as update data on a database, or retrieve data from a database. Unlike languages like Python or R, which are general-purpose programming languages, SQL is tailored specifically for database management and manipulation.

References:

• ResearchGate article on SQL1.
• SpringerLink chapter on Relational Databases and SQL Language2.
• DataCamp tutorial on SQL Server Installation3.
• Wikipedia page on SQL4.

This is because the price of the Rose unit can be estimated using the average cost per square foot of the original floor plans, which are Jasmine, Orchid, Azalea, and Tulip. To find the average cost per square foot of the original floor plans, we can use the following formula:

Plugging in the values from the original floor plans, we get:

To find the price of the Rose unit, we can use the following formula:

Plugging in the values from the Rose unit, we get:

Therefore, the price of the Rose unit should be $705,200, using the average cost per square foot of the original floor plans.

Exploratory data analysis (EDA) is a process of examining and summarizing a dataset using various techniques, such as descriptive statistics, visualizations, correlations, outliers detection, and hypothesis testing. EDA can help reveal the main characteristics, patterns, trends, and insights from the data, as well as identify any problems or issues with the data quality or structure. EDA is usually performed after understanding a business requirement for a data analysis report and before building a mock dashboard/presentation layout. Therefore, the correct answer is B. References: [What is Exploratory Data Analysis? | Definition and Examples], [Exploratory Data Analysis in Python]

 Data cleansing is a critical process in data analytics to ensure the accuracy and quality of data. The reasons to undertake data cleansing include:

• Missing Data (B): Missing data can lead to incomplete analysis and biased results. It is essential to identify and address gaps in the dataset to maintain the integrity of the analysis1.
• Invalid Data (D): Invalid data includes entries that are out of range, improperly formatted, or illogical (e.g., a negative age). Such data can corrupt analysis and lead to incorrect conclusions1.

Other options, such as non-parametric data (A), are not inherently errors but refer to a type of data that doesn’t assume a normal distribution. Duplicate data © and redundant data (E) could also be reasons for data cleansing, but they are not listed as options to select from in the provided image details. Normalized data (F) refers to data that has been processed to fit into a certain range or format and is typically not a reason for data cleansing.

References:

• Understanding the importance of data quality and the impacts of missing and invalid data on research outcomes1.
• Best practices in data cleansing2.

Data cleansing is required for various reasons, two of which are missing data (B) and invalid data (D). From the table provided, we can infer the necessity of cleansing in the context of ensuring data integrity and consistency. Missing data refers to the absence of data where it is expected, which can hinder analysis due to incomplete information. Invalid data refers to data that is incorrect, out of range, or in an inappropriate format, which can lead to inaccuracies in any analysis or report. Both these issues can significantly affect the outcomes of any data-related operations and thus need to be rectified through the data cleansing process.

The correct answer is B. Snowflake.

A snowflake schema is a type of database schema that features normalized dimension tables. A database schema is a way of organizing and structuring the data in a database. A dimension table is a table that contains descriptive attributes or characteristics of the data, such as product name, category, color, etc. A normalized table is a table that follows the rules of normalization, which is a process of reducing data redundancy and improving data integrity by organizing the data into smaller and simpler tables12

A snowflake schema is a variation of the star schema, which is another type of database schema that features denormalized dimension tables. A denormalized table is a table that does not follow the rules of normalization, and may contain redundant or duplicated data. A star schema consists of a central fact table that contains quantitative measures or facts, such as sales amount, order quantity, etc., and several dimension tables that are directly connected to the fact table. A snowflake schema differs from a star schema in that the dimension tables are further split into sub-dimension tables, creating a snowflake-like shape13

A snowflake schema has some advantages and disadvantages over a star schema. Some advantages are:

• It reduces the storage space required for the dimension tables, as it eliminates the redundant data.
• It improves the data quality and consistency, as it avoids the update anomalies that may occur in denormalized tables.
• It allows more detailed analysis and queries, as it provides more levels of dimensions.

Some disadvantages are:

• It increases the complexity and number of joins required to retrieve the data from multiple tables, which may affect the query performance and speed.
• It reduces the readability and simplicity of the schema, as it has more tables and relationships to understand.
• It may require more maintenance and administration, as it has more tables to manage and update13

Customer name is the field that should be masked, because it contains sensitive information that could identify the government accounts that the financial institution deals with. Masking is a technique that replaces or obscures sensitive data with dummy or random data, such as asterisks or hashes. Masking can help protect the privacy and security of the data, while still allowing for some analysis and reporting. Therefore, the correct answer is D. References: [Data Masking | Definition, Techniques & Examples - Talend], [Data masking - Wikipedia]