COF-C02 Sample Questions Answers

UTF-8 is the only supported character set for loading and unloading data from all supported file formats in Snowflake. UTF-8 is a widely used encoding that supports a large range of characters from various languages, making it suitable for internationalization and ensuring data compatibility across different systems and platforms.

References:

• Snowflake Documentation: Data Loading and Unloading

To easily transfer data from a production account to a non-production account in Snowflake within the same cloud provider region, creating a data share is the most efficient approach. Data sharing allows for live, read-only access to selected data objects from the production account to the non-production account without the need to duplicate or move the actual data. This method facilitates seamless access to the data for development, testing, or analytics purposes in the non-production environment.

References:

• Snowflake Documentation: Data Sharing

The Virtual Private Snowflake (VPS) edition offers the highest level of security for organizations with the strictest security requirements. This edition provides a dedicated and isolated instance of Snowflake, including enhanced security features and compliance certifications to meet the needs of highly regulated industries or any organization requiring the utmost in data protection and privacy.

References:

• Snowflake Documentation: Snowflake Editions

Snowflake bills virtual warehouse usage in one-minute increments, rounding up to the nearest minute for any partial minute of compute time used. If a virtual warehouse runs for 61 seconds and then, after being shut down, restarts and runs for an additional 30 seconds, the total time billed would be 120 seconds or 2 minutes. The first 61 seconds are rounded up to 2 minutes, and the subsequent 30 seconds are within a new minute, which is also rounded up to the nearest minute.

References:

• Snowflake Documentation: Virtual Warehouses Billing

To extract the URL of a directory table on an external stage for further transformation in Snowflake, the GET_ABSOLUTE_PATH function can be used. This function returns the full path of a file or directory within a specified stage, enabling users to dynamically construct URLs for accessing or processing data stored in external stages.

References:

• Snowflake Documentation: Working with Stages

When unloading floating-point number columns to CSV or JSON files, Snowflake truncates the values to approximately 15 significant digits with 9 digits following the decimal point, which can be represented as (15,9). This ensures a balance between accuracy and efficiency in representing floating-point numbers in text-based formats, which is essential for data interchange and processing applications that consume these files.

References:

• Snowflake Documentation: Data Unloading Considerations

When creating a CSV file format in Snowflake and the requirement is to wrap columns by single quotes or double quotes, the FIELD_OPTIONALLY_ENCLOSED_BY parameter should be used in the file format specification. This parameter allows you to define a character (either a single quote or a double quote) that can optionally enclose each field in the CSV file, providing flexibility in handling fields that contain special characters or delimiters as part of their data.

References:

• Snowflake Documentation: CSV File Format

The REVOKE ROLE command is used to remove a role from another role or a user in Snowflake. This command is part of Snowflake's role-based access control system, allowing administrators to manage permissions and access to database objects efficiently by adding or removing roles from users or other roles.

References:

• Snowflake Documentation: REVOKE ROLE

The Fail-safe retention period for transient and temporary tables in Snowflake is 0 days. Fail-safe is a feature designed to protect data against accidental loss or deletion by retaining historical data for a period after its Time Travel retention period expires. However, transient and temporary tables, which are designed for temporary or short-term storage and operations, do not have a Fail-safe period. Once the data is deleted or the table is dropped, it cannot be recovered.

References:

• Snowflake Documentation: Understanding Fail-safe

Transient tables in Snowflake are designed for temporary or intermediate workloads with the following characteristics:

• B. Transient tables can be cloned to permanent tables: This feature allows users to create copies of transient tables for permanent use, providing flexibility in managing data lifecycles.
• C. Transient tables persist until they are explicitly dropped: Unlike temporary tables that exist for the duration of a session, transient tables remain in the database until explicitly removed by a user, offering more durability for short-term data storage needs.

References:

• Snowflake Documentation: Transient Tables

Query pruning in Snowflake is the mechanism used to limit the number of micro-partitions scanned by a query. By analyzing the filters and conditions applied in a query, Snowflake can skip over micro-partitions that do not contain relevant data, thereby reducing the amount of data processed and improving query performance. This technique is particularly effective for large datasets and is a key component of Snowflake's performance optimization features.

References:

• Snowflake Documentation: Query Performance Optimization

A materialized view in Snowflake is a database object derived from a query specification, stored for later use, and can significantly speed up expensive aggregations on large data sets. Materialized views store the result of their underlying query, reducing the need to recompute the result each time the view is accessed. This makes them ideal for improving the performance of read-heavy, aggregate-intensive queries.

References:

• Snowflake Documentation: Using Materialized Views

To insert JSON formatted string data into a VARIANT field in Snowflake, the correct function to use is PARSE_JSON. The PARSE_JSON function is specifically designed to interpret a JSON formatted string and convert it into a VARIANT type, which is Snowflake's flexible format for handling semi-structured data like JSON, XML, and Avro. This function is essential for loading and querying JSON data within Snowflake, allowing users to store and manage JSON data efficiently while preserving its structure for querying purposes. This function's usage and capabilities are detailed in the Snowflake documentation, providing users with guidance on how to handle semi-structured data effectively within their Snowflake environments.

References:

• Snowflake Documentation: PARSE_JSON

For directory tables, a named external stage allows for the automatic refreshing of metadata. This capability is particularly useful when dealing with files stored on external storage services (like Amazon S3, Google Cloud Storage, or Azure Blob Storage) and accessed through Snowflake. The external stage references these files, and the directory table's metadata can be automatically updated to reflect changes in the underlying files.

References:

• Snowflake Documentation: External Stages

When unloading data, the Parquet file format is known for its efficiency in preserving the data values for floating-point number columns. Parquet is a columnar storage file format that offers high compression ratios and efficient data encoding schemes. It is especially effective for floating-point data, as it maintains high precision and supports efficient querying and analysis.

References:

• Snowflake Documentation: Using the Parquet File Format for Unloading Data

A materialized view in Snowflake denotes a pre-computed data set derived from a SELECT query specification and stored for later use. Unlike standard views, which dynamically compute the data each time the view is accessed, materialized views store the result of the query at the time it is executed, thereby speeding up access to the data, especially for expensive aggregations on large datasets.

References:

• Snowflake Documentation: Materialized Views

In Snowflake, overloading refers to the creation of multiple User-Defined Functions (UDFs) with the same name but differing in the number or types of their arguments. This feature allows for more flexible function usage, as Snowflake can differentiate between functions based on the context of their invocation, such as the types or the number of arguments passed. Overloading helps to create more adaptable and readable code, as the same function name can be used for similar operations on different types of data.

References:

• Snowflake Documentation: User-Defined Functions

Query pruning in Snowflake refers to the optimization technique where the system reduces the amount of data scanned by a query based on the query conditions. This typically involves skipping unnecessary data partitions that do not contribute to the query result. The effectiveness of this technique can be observed through:

• Option A: Partitions scanned. This statistic indicates how many data partitions were actually scanned as a result of query pruning, showing the optimization in action.
• Option C: Bytes scanned. This measures the volume of data physically read during query execution, and a reduction in this number indicates effective query pruning, as fewer bytes are read when unnecessary partitions are skipped.

Options B, D, and E do not directly relate to observing the effects of query pruning. "Partitions total" shows the total available, not the impact of pruning, while "Bytes read from result" and "Bytes written" relate to output rather than the efficiency of data scanning.References: Snowflake documentation on performance tuning and query optimization techniques, specifically how query pruning affects data access.

The TABLE_STORAGE_METRICS view can be used to determine if a table has frequent row updates or deletes. This view provides detailed metrics on the storage utilization of tables within Snowflake, including metrics that reflect the impact of DML operations such as updates and deletes on table storage. For example, metrics related to the number of active and deleted rows can help identify tables that experience high levels of row modifications, indicating frequent updates or deletions.

References:

• Snowflake Documentation: TABLE_STORAGE_METRICS View

When querying semi-structured data in Snowflake, the behavior towards case sensitivity is distinct between column names and the names of elements within the semi-structured data. Column names follow the general SQL norm of being case-insensitive, meaning you can reference them in any case without affecting the query. However, element names within JSON, XML, or other semi-structured data are case-sensitive. This distinction is crucial for accurate data retrieval and manipulation in Snowflake, especially when working with JSON objects where the case of keys can significantly alter the outcome of queries.

References:

• Snowflake Documentation: Querying Semi-structured Data

Snowflake's architecture is unique in that it combines elements of both traditional shared-disk and shared-nothing database architectures. This hybrid approach allows Snowflake to offer the scalability and performance benefits of a shared-nothing architecture (with compute and storage separated) while maintaining the simplicity and flexibility of a shared-disk architecture in managing data across all nodes in the system. This results in an architecture that provides on-demand scalability, both vertically and horizontally, without sacrificing performance or data cohesion.

References:

• Snowflake Documentation: Snowflake Architecture

Scalar functions in Snowflake (and SQL in general) are designed to return a single value for each invocation. They operate on a single value and return a single result, making them suitable for a wide range of data transformations and calculations within queries.

References:

• Snowflake Documentation: Functions

The command to list all the file formats for which a user has access privileges in Snowflake is SHOW FILE FORMATS. This command provides a list of all file formats defined in the user's current session or specified database/schema, along with details such as the name, type, and creation time of each file format. It is a valuable tool for users to understand and manage the file formats available for data loading and unloading operations.

References:

• Snowflake Documentation: SHOW FILE FORMATS

A data consumer who has a pre-signed URL can access data files using Snowflake until the expiration time is exceeded. The expiration time is set when the pre-signed URL is generated and determines how long the URL remains valid3.

The VARIANT data type in Snowflake can store multiple types of data structures, as it is designed to hold semi-structured data. It can contain any other data type, including OBJECT and ARRAY, which allows it to represent various data structures

 Snowsight automatically detects if a target account is in a different region and enables cross-cloud auto-fulfillment when using a paid listing on the Snowflake Marketplace. This feature allows Snowflake to manage the replication of data products to consumer regions as needed, without manual intervention1.

A scoped URL expires when the persisted query result period ends, which is typically after the results cache expires. This is currently set to 24 hours

Snowflake supports storing unstructured data and provides native support for semi-structured file formats such as JSON, Avro, Parquet, ORC, and XML1. GeoJSON, being a type of JSON, and XML are among the formats that can be stored in Snowflake. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The Kafka connector stores formatted information in a single column of type VARIANT. This column type is used to store semi-structured data like JSON or Avro, which allows for flexibility in the data structure

 Snowflake recommends creating separate warehouses for each workload to resolve performance issues caused by shared virtual warehouses. This ensures that the resources are not being overutilized by one user’s activities, thereby affecting the performance of another user’s activities4.

 Snowflake supports several semi-structured data formats for loading data. The supported formats include JSON, Avro, and Parquet12. These formats allow for efficient storage and querying of data that does not conform to a traditional relational database schema.

 The command used to unload files from an internal or external stage to a local file system in Snowflake is the GET command. This command allows users to download data files that have been staged, making them available on the local file system for further use23.

 To monitor data storage for individual tables, the commands and objects that can be used are ‘SHOW STORAGE BY TABLE;’ and the Information Schema view ‘TABLE_STORAGE_METRICS’. These tools provide detailed information about the storage utilization for tables. References: Snowflake Documentation

Snowflake integrates Duo Security to support Multi-Factor Authentication (MFA). This feature provides increased login security for users connecting to Snowflake, and it is managed completely by Snowflake without the need for users to sign up separately with Duo4.

 To change permissions for new objects in a managed access schema, a user should be granted the MANAGE GRANTS global privilege. This privilege allows the user to manage access control through grants on all securable objects within Snowflake2. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The Snowflake Account_Usage.Metering_History view is used to gather the hourly credit usage for an account. This view provides details on the credits consumed by various services within Snowflake for the last 365 days1.

Resource monitors in Snowflake can be configured at the account and virtual warehouse levels. They are used to track credit usage and control costs associated with running virtual warehouses. When certain thresholds are reached, resource monitors can trigger actions such as sending alerts or suspending warehouses to prevent excessive credit consumption. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 In Snowflake, the OPERATE privilege is required for a role to suspend or resume a task. This privilege allows the role to perform operational tasks such as starting and stopping tasks, which includes suspending and resuming them6

 In a standard multi-cluster warehouse with auto-scaling, a cluster will shut down when, after 2-3 consecutive checks, the system determines that the load on the least-loaded cluster could be redistributed to other clusters. This ensures efficient resource utilization and cost management. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 Once a database has been created in a consumer account from a share, the shared objects become accessible to users in that account. The shared objects are not transferred or copied; they remain in the provider’s account and are accessible to the consumer account

To change the existing file format type from CSV to JSON, the recommended way is to use the ALTER FILE FORMAT command with the SET TYPE=JSON clause. This alters the file format specification to use JSON instead of CSV. References: Based on my internal knowledge as of 2021.

The user property ‘MINS TO BYPASS MFA’ in Snowflake allows temporary bypass of MFA for a user, which can be set by an account administrator without contacting Snowflake Support2.

The cloud services layer of Snowflake architecture handles various aspects including security functions, authentication of user sessions, and access control, ensuring that only authorized users can access the data and services23.

 SnowCD (Snowflake Connectivity Diagnostic Tool) is the best tool provided by Snowflake for troubleshooting network connectivity issues. It helps diagnose and resolve issues related to connecting to Snowflake services

https://docs.snowflake.com/en/user-guide/snowcd.html#:~:text=SnowCD%20(i.e.%20Snowflake%20Connectivity%20Diagnostic,their%20network%20connection%20to%20Snowflake .

 The stream type that can be used for tracking the records in external tables is ‘External’. This type of stream is specifically designed to track changes in external tables

 The pre-signed URL type allows users or applications to download or access files directly from a Snowflake stage without authentication. This URL type is open and can be used without needing to authenticate into Snowflake or pass an authorization token.

To find out which numeric values are present in both tables, the INTERSECT set operator should be used. This operator returns rows from one query’s result set which also appear in another query’s result set, effectively finding the common elements between the two tables45.

In Snowflake, to change the columns referenced in a view, the view must be recreated with the required changes. The ALTER VIEW command does not allow changing the definition of a view; it can only be used to rename a view, convert it to or from a secure view, or add, overwrite, or remove a comment for a view. Therefore, the correct approach is to drop the existing view and create a new one with the desired column references. 

 The OBJECT_CONSTRUCT function is used in Snowflake to create a JSON object from relational data, which is useful when unloading semi-structured data. The SINGLE = TRUE parameter is used when unloading data to ensure that the data is exported as a single file, which can be up to 5 GB in size. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 Stored procedures in Snowflake can perform a variety of database operations, including DDL and DML, whereas UDFs are designed to return values and cannot execute database operations1.

To ensure a data consumer has access to the required objects, a data provider can enable the data sharing feature and validate that the consumer can access the views or tables shared with them. References: Based on general data sharing practices in cloud services as of 2021.

Disabling auto-suspend for a virtual warehouse is recommended when there is a steady workload, which ensures that compute resources are always available. Additionally, it is advisable to disable auto-suspend when immediate availability of compute resources is critical, eliminating any startup delay

 Snowflake allows for only one resource monitor to be assigned at the account level. This monitor oversees the credit usage of all the warehouses in the account. References: Snowflake Documentation

 External stages can be altered and dropped in Snowflake. An external stage points to an external location, such as an S3 bucket, where data files are stored. Users can modify the stage’s definition or drop it entirely if it’s no longer needed. This is in contrast to table stages, which are tied to specific tables and cannot be altered or dropped independently.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Stages1

When a CLUSTER BY clause is added to a Snowflake table, it specifies one or more columns to organize the data within the table’s micro-partitions. This clustering aims to colocate data with similar values in the same or adjacent micro-partitions. By doing so, it enhances the efficiency of query pruning, where the Snowflake query optimizer can skip over irrelevant micro-partitions that do not contain the data relevant to the query, thereby improving performance.

References:

• Snowflake Documentation on Clustering Keys & Clustered Tables1.
• Community discussions on how source data’s ordering affects a table with a cluster key

https://docs.snowflake.com/en/user-guide/intro-summary-loading.html#:~:text=For%20delimited%20files%20(CSV%2C%20TSV,encoding%20to%20use%20for%20loading .

For delimited files (CSV, TSV, etc.), the default character set is UTF-8. To use any other characters sets, you must explicitly specify the encoding to use for loading. For the list of supported character sets, see Supported Character Sets for Delimited Files (in this topic).

The COPY command in Snowflake allows for the reordering of columns as they are loaded into a table, and it also permits the omission of columns from the source file during the load process. This provides flexibility in handling the schema of the data being ingested. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 Snowflake manages various compute resources and features, including Snowpipe and the ability to scale up a warehouse. Snowpipe is Snowflake’s continuous data ingestion service that allows users to load data as soon as it becomes available. Scaling up a warehouse refers to increasing the compute resources allocated to a virtual warehouse to handle larger workloads or improve performance.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Snowpipe and Virtual Warehouses1

Snowflake’s Time Travel feature is used for both querying historical data in tables and restoring and cloning historical data in databases, schemas, and tables3. It allows users to access historical data within a defined period (1 day by default, up to 90 days for Snowflake Enterprise Edition) and is a key feature for data recovery and management. References: [COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake supports two geospatial data types: GEOGRAPHY and GEOMETRY. The GEOGRAPHY data type is used to store geospatial data that models the Earth as a perfect sphere, which is suitable for global geospatial data. This data type follows the WGS 84 standard and is used for storing points, lines, and polygons on the Earth’s surface. The GEOMETRY data type, on the other hand, represents features in a planar (Euclidean, Cartesian) coordinate system and is typically used for local spatial reference systems. Since the question specifically asks about geospatial data, which commonly refers to Earth-related spatial data, the correct answer is GEOGRAPHY3. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The purpose of an External Function in Snowflake is to call code that executes outside of the Snowflake environment. This allows Snowflake to interact with external services and leverage functionalities that are not natively available within Snowflake, such as calling APIs or running custom code hosted on cloud services3.

https://docs.snowflake.com/en/sql-reference/external-functions.html

 When unloading data to a stage, it is recommended to define an individual file format. This ensures that the data is unloaded in a consistent and expected format, which can be crucial for downstream processing and analysis2

Provisioning time can vary based on the size of the warehouse. A 4X-Large Warehouse typically has more resources and may take longer to provision compared to a X-Small Warehouse, which has fewer resources and can generally be provisioned more quickly.References: Understanding and viewing Fail-safe | Snowflake Documentation

In Snowflake, Cloud Services costs are incurred when the Cloud Services usage exceeds 10% of the compute usage (measured in credits). Therefore, scenarios A and E would result in Cloud Services charges because the Cloud Services usage is more than 10% of the compute credits used.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake’s official documentation on billing and usage1

When the COPY INTO command is executed in Snowflake, it processes all files present in the specified stage that have not been ingested before or marked as already loaded. Since the user did not remove the original 10 files after the first load, running the same COPY INTO command again will result in all 20 files being processed. This means that the data from the original 10 files will be appended to the table again, along with the data from the new 10 files, potentially leading to duplicate records for the original data set.

References:

• Snowflake Documentation on Data Loading
• SnowPro® Core Certification Study Guide

 The correct statement to accommodate the request for a marketing co-worker to change the size of their medium virtual warehouse called mktg__WH is to grant the MODIFY privilege on the warehouse to the ROLE MARKETING. This privilege allows the role to change the warehouse size among other properties.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Access Control Privileges1

 The Result cache is used in Snowflake to cache the data output from SQL queries. This feature is designed to improve performance by storing the results of queries for a period of time. When the same or similar query is executed again, Snowflake can retrieve the result from this cache instead of re-computing the result, which saves time and computational resources.

References:

• Snowflake Documentation on Query Results Cache
• SnowPro® Core Certification Study Guide

 To address a high volume of queries queuing in a warehouse, Snowflake recommends two best practices:

• A. Use multi-clustered warehousing to scale out warehouse capacity: This approach allows for the distribution of queries across multiple clusters within a warehouse, effectively managing the load and reducing the queue volume.
• B. Scale up the warehouse size to allow Queries to execute faster: Increasing the size of the warehouse provides more compute resources, which can reduce the time it takes for queries to execute and thus decrease the number of queries waiting in the queue.

These strategies help to optimize the performance of the warehouse by ensuring that resources are scaled appropriately to meet demand.

References:

• Snowflake Documentation on Multi-Cluster Warehousing
• SnowPro Core Certification best practices

 Clustering keys in Snowflake work by sorting the designated columns over time. This process is done in the background and does not block data manipulation language (DML) operations, allowing for normal database operations to continue without interruption. The purpose of clustering keys is to organize the data within micro-partitions to optimize query performance1.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Clustering1

When a virtual warehouse in Snowflake is resized, specifically when it is increased in size, the additional compute resources become immediately available to all running and queued queries. This means that the performance of these queries can improve due to the increased resources. Conversely, when the size of a warehouse is reduced, the compute resources are not removed until they are no longer being used by any current operations1.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Virtual Warehouses2

 Virtual Warehouses in Snowflake can indeed be resized while they are suspended. Resizing a warehouse involves changing the number of compute resources (servers) allocated to it, which can be done to adjust performance and cost. When a warehouse is suspended, it is not currently running any queries, but its definition and metadata remain intact, allowing for modifications like resizing.

The result set cache in Snowflake is invalidated and no longer available when the underlying data of the query results has changed, ensuring that queries return the most current data. Additionally, the cache expires after 24 hours to maintain the efficiency and accuracy of data retrieval1.

The primary responsibility of Snowflake’s virtual warehouses is to execute queries. Virtual warehouses are one of the key components of Snowflake’s architecture, providing the compute power required to perform data processing tasks such as running SQL queries, performing joins, aggregations, and other data manipulations.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Virtual Warehouses1

 Snowflake applies egress charges in the case of database replication when data is transferred out of a Snowflake region to another region or cloud provider. This is because the data transfer incurs costs associated with moving data across different networks. Egress charges are not applied for data sharing within the same region, query result retrieval, or loading data into Snowflake, as these actions do not involve data transfer across regions.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Data Replication and Egress Charges1

 Snowflake is designed to optimize query performance by utilizing metadata for certain types of queries. When executing a COUNT query, Snowflake can often fulfill the request by accessing metadata about the table’s row count, rather than scanning the entire table or micro-partitions. This is particularly efficient for large tables like FCT_SALES with a significant number of records. The metadata layer maintains statistics about the table, including the row count, which enables Snowflake to quickly return the result of a COUNT query without the need to perform a full scan.

References:

• Snowflake Documentation on Metadata Management
• SnowPro® Core Certification Study Guide

Data Robot is recognized as a machine learning and data science partner within the Snowflake Partner Ecosystem. It provides an enterprise AI platform that enables users to build and deploy accurate predictive models quickly. As a partner, Data Robot integrates with Snowflake to enhance data science capabilities2.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Machine Learning & Data Science Partners

https://docs.snowflake.com/en/user-guide/ecosystem-analytics.html

For data loading using Snowpipe, the recommended file size is a compressed file greater than 10 MB and up to 100 MB. This size range is optimal for Snowpipe’s continuous, micro-batch loading process, allowing for efficient and timely data ingestion without overwhelming the system with files that are too large or too small.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Snowpipe1

 To evaluate the details of dynamic data masking, the POLICY_REFERENCES and ACCESS_HISTORY views in the account_usage schema are used. The POLICY_REFERENCES view provides information about the objects to which a masking policy is applied, and the ACCESS_HISTORY view contains details about access to the masked data, which can be used to audit and verify the application of dynamic data masking policies.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Dynamic Data Masking1

The ACCOUNTADMIN role is required for a user to create and manage accounts in Snowflake. This role has the highest level of privileges and is responsible for managing all aspects of the Snowflake account, including the ability to create and manage other user accounts1.

https://docs.snowflake.com/en/user-guide/security-access-control-considerations.html

 In Snowflake, when the account is located on Microsoft Azure, a valid source for an external stage can be an Azure container or a folder path within an Azure container. This includes Azure Blob storage which is accessible via the azure:// endpoint. A Windows server file share on Azure, if configured properly, can be a valid source for staging data files for Snowflake. Options A, B, and C are not supported as direct sources for an external stage in Snowflake on Azure12. References: [COF-C02] SnowPro Core Certification Exam Study Guide

Secure data sharing in Snowflake allows users to share various objects between Snowflake accounts without physically copying the data, thus not consuming additional storage. Among the options provided, external tables can be shared through secure data sharing. External tables are used to query data directly from files in a stage without loading the data into Snowflake tables, making them suitable for sharing across different Snowflake accounts.

References:

• Snowflake Documentation on Secure Data Sharing
• SnowPro™ Core Certification Companion: Hands-on Preparation and Practice

To return results from the results cache in Snowflake, certain conditions must be met:

• Privileges: The user must have the appropriate privileges on the objects associated with the query. This ensures that only authorized users can access cached data.
• Time Frame: The query must have been run within 24 hours of the previously-run query. Snowflake’s results cache is designed to store the results of queries for a short period, typically 24 hours, to improve performance for repeated queries.

 In a CREATE PIPE ... AS COPY ... FROM (....) statement, the supported transformations include filtering data using an optional WHERE clause and omitting columns. The WHERE clause allows for the specification of conditions to filter the data that is being loaded, ensuring only relevant data is inserted into the table. Omitting columns enables the exclusion of certain columns from the data load, which can be useful when the incoming data contains more columns than are needed for the target table.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Simple Transformations During a Load1

Snowflake’s Continuous Data Protection (CDP) encompasses a set of features that help protect data stored in Snowflake against human error, malicious acts, and software failure. Time Travel allows users to access historical data (i.e., data that has been changed or deleted) for a defined period, enabling querying and restoring of data. Fail-safe is an additional layer of data protection that provides a recovery option in the event of significant data loss or corruption, which can only be performed by Snowflake.

References:

• Continuous Data Protection | Snowflake Documentation1
• Data Storage Considerations | Snowflake Documentation2
• Snowflake SnowPro Core Certification Study Guide3
• Snowflake Data Cloud Glossary

https://docs.snowflake.com/en/user-guide/data-availability.html

 Protegrity is listed as a data tokenization integration partner for Snowflake. This partnership allows Snowflake users to utilize Protegrity’s tokenization solutions within the Snowflake environment3.

References = [COF-C02] SnowPro Core Certification Exam Study Guide, Snowflake Documentation

Snowflake supports the use of external stages with Amazon Web Services (AWS), Microsoft Azure Cloud, and Google Cloud Platform (GCP). These platforms allow users to stage data externally and integrate with Snowflake for data loading operations

The Snowflake Enterprise edition includes database replication and failover for business continuity and disaster recovery, as well as extended time travel capabilities for longer data retention periods1.

Shares in Snowflake are named objects that encapsulate all the information required to share databases, schemas, tables, secure views, and secure UDFs. These objects can be added to a share by granting privileges on them to the share via a database role

The SQL command used to delete files from a Snowflake internal stage is REMOVE. This command can be used to remove files from either an internal or external stage within Snowflake

 In Snowflake, a schema is a logical grouping of database objects, which can include streams and external tables. A stream is an object that allows users to query data that has changed in specified tables or views, and an external table is a table that references data stored outside of Snowflake. Roles, warehouses, users, and shares are not contained within a schema. References: SHOW OBJECTS, Database, Schema, & Share DDL

The LOGIN_HISTORY view in the ACCOUNT_USAGE schema provides information about login attempts, including both successful and failed logins. This view can be used to review the failed login attempts of a specific user for the past 30 days. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 Snowpipe retains load history for 14 days. This allows users to view and audit the data that has been loaded into Snowflake using Snowpipe within this time frame3.

The LATERAL FLATTEN command in Snowflake produces a result set that includes several columns, among which PATH and INDEX are includedPATH indicates the path to the element within a data structure that needs to be flattened, and INDEX represents the index of the element if it is an array2.

The SQL command Select * from table(validate(t1, job_id => '_last')); is used to return errors from the last executed COPY command into table t1 in the current session. It checks the results of the most recent data load operation and provides details on any errors that occurred during that process1.

 The GET command is used to download files from a Snowflake stage to a local folder on a client’s machine2.

 Temporary and transient tables in Snowflake do not have a Fail-safe period. Once the session ends or the tables are dropped, the data is purged and not recoverable1.

To manage compliance with Personal Identifiable Information (PII) requirements, the Virtual Private Snowflake and Business Critical Editions of Snowflake should be used. These editions provide advanced security features necessary for handling sensitive data

 Best practices for loading data into Snowflake include aiming for data file sizes between 100 MB and 250 MB when compressed, as this size is optimal for parallel processing and minimizes overhead. Enclosing fields with delimiter characters in quotes ensures proper field recognition during the load process. Splitting large files into smaller ones allows for better distribution of the load across compute resources, enhancing performance and efficiency.

 In the Snowflake Data Marketplace, the types of data listings available include ‘Vendor’, which refers to the providers of data, and ‘Personalized’, which indicates customized data offerings tailored to specific consumer needs45.

 The supported file formats for unloading data from Snowflake include JSON, Parquet, and CSV. These formats are commonly used for their flexibility and compatibility with various data processing tools

 The credit consumption for maintaining a materialized view is impacted by how often the base table changes © and whether the materialized view has a cluster key defined (D). Changes to the base table can trigger a refresh of the materialized view, consuming credits. Additionally, having a cluster key defined can optimize the performance and credit usage during the materialized view’s maintenance. References: SnowPro Core Certification materialized view credit consumption

When cloning a database containing stored procedures and regular views with fully qualified table references, the cloned views and stored procedures will reference the cloned tables in the cloned database (A). This ensures that the cloned database is a self-contained copy of the original, with all references pointing to objects within the same cloned database. References: SnowPro Core Certification cloning database stored procedures views

The responsibilities of Snowflake’s Cloud Service layer include authentication (A), which ensures secure access to the platform; resource management (B), which involves allocating and managing compute resources; and query parsing and optimization (D), which improves the efficiency and performance of SQL query execution3.

When loading data into Snowflake, it is recommended to organize the data into single files with 100-250 MB of compressed data per file. This size range is optimal for parallel processing and can help in achieving better performance during data loading operations. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 To ensure that additional multi-clusters are resumed with no delay, a virtual warehouse should be configured to a size larger than generally required. This configuration allows for immediate availability of additional resources when needed, without waiting for new clusters to start up

 Snowflake supports unloading data in JSON and delimited file formats such as CSV and TSV. These formats are commonly used for data interchange and are supported by Snowflake for unloading operations

Worksheets in Snowsight can be shared directly with other Snowflake users within the same account. This feature allows for collaboration and sharing of SQL queries or Python code, as well as other data manipulation tasks1.

 Users are responsible for data storage costs in Snowflake until the data expires from the Fail-safe period. Fail-safe is the final stage in the data lifecycle, following Time Travel, and provides additional protection against accidental data loss. Once data exits the Fail-safe state, users are no longer billed for its storage

The feature in Snowflake that allows a user to substitute a randomly generated identifier for sensitive data before loading it into Snowflake is known as External Tokenization. This process helps to secure sensitive data by ensuring that it is not exposed in its original form, thus preventing unauthorized access3.

Network policies in Snowflake can be set at the user level and at the account level2.

The list @my_stage command in Snowflake can be used to verify if data has been uploaded to an external stage named my_stage. This command provides a list of files that are present in the specified stage2.

 In Snowflake, both materialized views and transient tables will incur storage charges because they store data. They will also incur compute charges when queries are run against them, as compute resources are used to process the queries. References: [COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake supports the VARIANT and ARRAY data types for semi-structured data. VARIANT can store values of any other type, including OBJECT and ARRAY, making it suitable for semi-structured data formats like JSON. ARRAY is used to store an ordered list of elements

 When cloning a database in Snowflake, the clone includes all privileges on the database as well as existing child objects within the database, such as schemas, tables, views, etc. However, it does not include future child objects or privileges on schemas within the database2.

References = [COF-C02] SnowPro Core Certification Exam Study Guide, Snowflake Documentation

 In Snowflake, if a temporary table and a permanent table have the same name within the same schema, the temporary table takes precedence over the permanent table within the session where the temporary table was created4.

The graphical representation of warehouse utilization indicates periods of significant queuing, suggesting that the current single cluster cannot efficiently handle all incoming queries. Configuring the warehouse to a multi-cluster warehouse will distribute the load among multiple clusters, reducing queuing times and improving overall performance1.

References = Snowflake Documentation on Multi-cluster Warehouses1