DAS-C01 Free Practice Test

A financial company hosts a data lake in Amazon S3 and a data warehouse on an Amazon Redshift cluster. The company uses Amazon QuickSight to build dashboards and wants to secure access from its on-premises Active Directory to Amazon QuickSight.
How should the data be secured?

1. A. Use an Active Directory connector and single sign-on (SSO) in a corporate network environment.
2. B. Use a VPC endpoint to connect to Amazon S3 from Amazon QuickSight and an IAM role to authenticate Amazon Redshift.
3. C. Establish a secure connection by creating an S3 endpoint to connect Amazon QuickSight and a VPC endpoint to connect to Amazon Redshift.
4. D. Place Amazon QuickSight and Amazon Redshift in the security group and use an Amazon S3 endpoint to connect Amazon QuickSight to Amazon S3.

Correct Answer: A
https://docs.aws.amazon.com/quicksight/latest/user/directory-integration.html

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?

1. A. Merge the files in Amazon S3 to form larger files.
2. B. Increase the number of shards in Kinesis Data Streams.
3. C. Add more memory and CPU capacity to the streaming application.
4. D. Write the files to multiple S3 buckets.

Correct Answer: A
https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/

A university intends to use Amazon Kinesis Data Firehose to collect JSON-formatted batches of water quality readings in Amazon S3. The readings are from 50 sensors scattered across a local lake. Students will query the stored data using Amazon Athena to observe changes in a captured metric over time, such as water temperature or acidity. Interest has grown in the study, prompting the university to reconsider how data will be stored.
Which data format and partitioning choices will MOST significantly reduce costs? (Choose two.)

1. A. Store the data in Apache Avro format using Snappy compression.
2. B. Partition the data by year, month, and day.
3. C. Store the data in Apache ORC format using no compression.
4. D. Store the data in Apache Parquet format using Snappy compression.
5. E. Partition the data by sensor, year, month, and day.

Correct Answer: CD

A company leverages Amazon Athena for ad-hoc queries against data stored in Amazon S3. The company wants to implement additional controls to separate query execution and query history among users, teams, or applications running in the same AWS account to comply with internal security policies.
Which solution meets these requirements?

1. A. Create an S3 bucket for each given use case, create an S3 bucket policy that grants permissions to appropriate individual IAM user
2. B. and apply the S3 bucket policy to the S3 bucket.
3. C. Create an Athena workgroup for each given use case, apply tags to the workgroup, and create an IAM policy using the tags to apply appropriate permissions to the workgroup.
4. D. Create an IAM role for each given use case, assign appropriate permissions to the role for the given use case, and add the role to associate the role with Athena.
5. E. Create an AWS Glue Data Catalog resource policy for each given use case that grants permissions to appropriate individual IAM users, and apply the resource policy to the specific tables used by Athena.

Correct Answer: B
https://docs.aws.amazon.com/athena/latest/ug/user-created-workgroups.html
Amazon Athena Workgroups - A new resource type that can be used to separate query execution and query history between Users, Teams, or Applications running under the same AWS account https://aws.amazon.com/about-aws/whats-new/2019/02/athena_workgroups/

A company has developed several AWS Glue jobs to validate and transform its data from Amazon S3 and load it into Amazon RDS for MySQL in batches once every day. The ETL jobs read the S3 data using a DynamicFrame. Currently, the ETL developers are experiencing challenges in processing only the incremental data on every run, as the AWS Glue job processes all the S3 input data on each run.
Which approach would allow the developers to solve the issue with minimal coding effort?

1. A. Have the ETL jobs read the data from Amazon S3 using a DataFrame.
2. B. Enable job bookmarks on the AWS Glue jobs.
3. C. Create custom logic on the ETL jobs to track the processed S3 objects.
4. D. Have the ETL jobs delete the processed objects or data from Amazon S3 after each run.

Correct Answer: B