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DAS-C01 Exam Questions

Exam Code:
DAS-C01
Exam Name:
AWS Certified Data Analytics - Specialty
Last Update: Oct 18, 2021
130 Questions
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DAS-C01 Exam Questions

DAS-C01 Exam Details

Format: Multiple choice, Multiple Answer
Exam Type: Specialty
Exam Method: Testing center or online proctored exam
Time: 180 Min
Exam Price: $300 USD
Language: Available in English, Japanese, Korean, and Simplified Chinese

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AWS Certified Data Analytics - Specialty Questions and Answers

Questions 1

A human resources company maintains a 10-node Amazon Redshift cluster to run analytics queries on the company’s data. The Amazon Redshift cluster contains a product table and a transactions table, and both tables have a product_sku column. The tables are over 100 GB in size. The majority of queries run on both tables.

Which distribution style should the company use for the two tables to achieve optimal query performance?

Options:

A.

An EVEN distribution style for both tables

B.

A KEY distribution style for both tables

C.

An ALL distribution style for the product table and an EVEN distribution style for the transactions table

D.

An EVEN distribution style for the product table and an KEY distribution style for the transactions table

Questions 2

A company that monitors weather conditions from remote construction sites is setting up a solution to collect temperature data from the following two weather stations.

  • Station A, which has 10 sensors
  • Station B, which has five sensors

These weather stations were placed by onsite subject-matter experts.

Each sensor has a unique ID. The data collected from each sensor will be collected using Amazon Kinesis Data Streams.

Based on the total incoming and outgoing data throughput, a single Amazon Kinesis data stream with two shards is created. Two partition keys are created based on the station names. During testing, there is a bottleneck on data coming from Station A, but not from Station B. Upon review, it is confirmed that the total stream throughput is still less than the allocated Kinesis Data Streams throughput.

How can this bottleneck be resolved without increasing the overall cost and complexity of the solution, while retaining the data collection quality requirements?

Options:

A.

Increase the number of shards in Kinesis Data Streams to increase the level of parallelism.

B.

Create a separate Kinesis data stream for Station A with two shards, and stream Station A sensor data to the new stream.

C.

Modify the partition key to use the sensor ID instead of the station name.

D.

Reduce the number of sensors in Station A from 10 to 5 sensors.

Questions 3

A mortgage company has a microservice for accepting payments. This microservice uses the Amazon DynamoDB encryption client with AWS KMS managed keys to encrypt the sensitive data before writing the data to DynamoDB. The finance team should be able to load this data into Amazon Redshift and aggregate the values within the sensitive fields. The Amazon Redshift cluster is shared with other data analysts from different business units.

Which steps should a data analyst take to accomplish this task efficiently and securely?

Options:

A.

Create an AWS Lambda function to process the DynamoDB stream. Decrypt the sensitive data using the same KMS key. Save the output to a restricted S3 bucket for the finance team. Create a finance table in Amazon Redshift that is accessible to the finance team only. Use the COPY command to load the data from Amazon S3 to the finance table.

B.

Create an AWS Lambda function to process the DynamoDB stream. Save the output to a restricted S3 bucket for the finance team. Create a finance table in Amazon Redshift that is accessible to the finance team only. Use the COPY command with the IAM role that has access to the KMS key to load the data from S3 to the finance table.

C.

Create an Amazon EMR cluster with an EMR_EC2_DefaultRole role that has access to the KMS key. Create Apache Hive tables that reference the data stored in DynamoDB and the finance table in Amazon Redshift. In Hive, select the data from DynamoDB and then insert the output to the finance table in Amazon Redshift.

D.

Create an Amazon EMR cluster. Create Apache Hive tables that reference the data stored in DynamoDB. Insert the output to the restricted Amazon S3 bucket for the finance team. Use the COPY command with the IAM role that has access to the KMS key to load the data from Amazon S3 to the finance table in Amazon Redshift.

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