DAS-C01 sample questions

Question 1

A company stores its sales and marketing data that includes personally identifiable information (PH) in Amazon S3. The company allows its analysts to launch their own Amazon EMR cluster and run analytics reports with the data. To meet compliance requirements, the company must ensure the data is not publicly accessible throughout this process. A data engineer has secured Amazon S3 but must ensure the individual EMR clusters created by the analysts are not exposed to the public internet.

Which solution should the data engineer to meet this compliance requirement with LEAST amount of effort?

Accepted Answer

Check the security group of the EMR clusters regularly to ensure it does not allow inbound traffic from IPv4 0.0.0.0/0 or IPv6 ::/0.

Answer

Create an EMR security configuration and ensure the security configuration is associated with the EMR clusters when they are created.

Answer

Enable the block public access setting for Amazon EMR at the account level before any EMR cluster is created.

Answer

Use AWS WAF to block public internet access to the EMR clusters across the board.

Question 2

A company analyzes its data in an Amazon Redshift data warehouse, which currently has a cluster of three dense storage nodes. Due to a recent business acquisition, the company needs to load an additional 4 TB of user data into Amazon Redshift. The engineering team will combine all the user data and apply complex calculations that require I/O intensive resources. The company needs to adjust the cluster's capacity to support the change in analytical and storage requirements.

Accepted Answer

Resize the cluster using elastic resize with dense storage nodes.

Answer

Resize the cluster using elastic resize with dense compute nodes.

Answer

Resize the cluster using classic resize with dense compute nodes.

Answer

Resize the cluster using classic resize with dense storage nodes.

Question 3

Once a month, a company receives a 100 MB .csv file compressed with gzip. The file contains 50,000 property listing records and is stored in Amazon S3 Glacier. The company needs its data analyst to query a subset of the data for a specific vendor.

What is the most cost-effective solution?

Accepted Answer

Load the data to Amazon S3 and query it with Amazon Athena.

Answer

Load the data into Amazon S3 and query it with Amazon S3 Select.

Answer

Query the data from Amazon S3 Glacier directly with Amazon Glacier Select.

Answer

Load the data to Amazon S3 and query it with Amazon Redshift Spectrum.

Question 4

A streaming application is reading data from Amazon Kinesis Data Streams and immediately writing the data to an Amazon S3 bucket every 10 seconds. The application is reading data from hundreds of shards. The batch interval cannot be changed due to a separate requirement. The data is being accessed by Amazon Athena. Users are seeing degradation in query performance as time progresses.

Which action can help improve query performance?

Accepted Answer

Add more memory and CPU capacity to the streaming application.

Answer

Merge the files in Amazon S3 to form larger files.

Answer

Increase the number of shards in Kinesis Data Streams.

Answer

Write the files to multiple S3 buckets.

Question 5

A financial company uses Apache Hive on Amazon EMR for ad-hoc queries. Users are complaining of sluggish performance.

A data analyst notes the following:

• Approximately 90% of queries are submitted 1 hour after the market opens
• Hadoop Distributed File System (HDFS) utilization never exceeds 10%.

Which solution would help address the performance issues?

Accepted Answer

Create instance group configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch CapacityRemainingGB metric. Create an automatic scaling policy to scale in the instance groups based on the CloudWatch CapacityRemainingGB metric.

Answer

Create instance fleet configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch CapacityRemainingGB metric. Create an automatic scaling policy to scale in the instance fleet based on the CloudWatch CapacityRemainingGB metric.

Answer

Create instance fleet configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch YARNMemoryAvailablePercentage metric. Create an automatic scaling policy to scale in the instance fleet based on the CloudWatch YARNMemoryAvailablePercentage metric.

Answer

Create instance group configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch YARNMemoryAvailablePercentage metric. Create an automatic scaling policy to scale in the instance groups based on the CloudWatch YARNMemoryAvailablePercentage metric.

Question 6

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?

Accepted Answer

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.

Answer

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.

Answer

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.

Answer

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 1AM role that has access to the KMS key to load the data from Amazon S3 to the finance table in Amazon Redshift.

DAS-C01 Sample Questions & Answers

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DAS-C01 Sample Questions & Answers

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