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Associate-DEA-C01_38

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Questions and Answers

Question GkmZ6p9vxzHjbFdz4D2Z

Question

A company uses Amazon Athena for one-time queries against data that is in Amazon S3. The company has several use cases. The company must implement permission controls to separate query processes and access to query history among users, teams, and applications that are in the same AWS account. Which solution will meet these requirements?

Choices

  • A: Create an S3 bucket for each use case. Create an S3 bucket policy that grants permissions to appropriate individual IAM users. Apply the S3 bucket policy to the S3 bucket.
  • B: Create an Athena workgroup for each use case. Apply tags to the workgroup. Create an IAM policy that uses the tags to apply appropriate permissions to the workgroup.
  • C: Create an IAM role for each use case. Assign appropriate permissions to the role for each use case. Associate the role with Athena.
  • D: Create an AWS Glue Data Catalog resource policy that grants permissions to appropriate individual IAM users for each use case. Apply the resource policy to the specific tables that Athena uses.

Question 9RGcb52pOGtimcTYEV9O

Question

A data engineer needs to build an extract, transform, and load (ETL) job. The ETL job will process daily incoming .csv files that users upload to an Amazon S3 bucket. The size of each S3 object is less than 100 MB. Which solution will meet these requirements MOST cost-effectively?

Choices

  • A: Write a custom Python application. Host the application on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster.
  • B: Write a PySpark ETL script. Host the script on an Amazon EMR cluster.
  • C: Write an AWS Glue PySpark job. Use Apache Spark to transform the data.
  • D: Write an AWS Glue Python shell job. Use pandas to transform the data.

Question u5mWVT3watK8Ki7sFWun

Question

A data engineer creates an AWS Glue Data Catalog table by using an AWS Glue crawler that is named Orders. The data engineer wants to add the following new partitions:

s3://transactions/orders/order_date=2023-01-01 s3://transactions/orders/order_date=2023-01-02

The data engineer must edit the metadata to include the new partitions in the table without scanning all the folders and files in the location of the table.

Which data definition language (DDL) statement should the data engineer use in Amazon Athena?

Choices

  • A: ALTER TABLE Orders ADD PARTITION(order_date=’2023-01-01’) LOCATION ‘s3://transactions/orders/order_date=2023-01-01’; ALTER TABLE Orders ADD PARTITION(order_date=’2023-01-02’) LOCATION ‘s3://transactions/orders/order_date=2023-01-02’;
  • B: MSCK REPAIR TABLE Orders;
  • C: REPAIR TABLE Orders;
  • D: ALTER TABLE Orders MODIFY PARTITION(order_date=’2023-01-01’) LOCATION ‘s3://transactions/orders/2023-01-01’; ALTER TABLE Orders MODIFY PARTITION(order_date=’2023-01-02’) LOCATION ‘s3://transactions/orders/2023-01-02’;

Question JN0MELepQLjJs6tvi69T

Question

A company stores 10 to 15 TB of uncompressed .csv files in Amazon S3. The company is evaluating Amazon Athena as a one-time query engine.

The company wants to transform the data to optimize query runtime and storage costs.

Which file format and compression solution will meet these requirements for Athena queries?

Choices

  • A: .csv format compressed with zip
  • B: JSON format compressed with bzip2
  • C: Apache Parquet format compressed with Snappy
  • D: Apache Avro format compressed with LZO

Question gDxEtvdwCnNwQ96GwkvW

Question

A company uses Apache Airflow to orchestrate the company’s current on-premises data pipelines. The company runs SQL data quality check tasks as part of the pipelines. The company wants to migrate the pipelines to AWS and to use AWS managed services.

Which solution will meet these requirements with the LEAST amount of refactoring?

Choices

  • A: Setup AWS Outposts in the AWS Region that is nearest to the location where the company uses Airflow. Migrate the servers into Outposts hosted Amazon EC2 instances. Update the pipelines to interact with the Outposts hosted EC2 instances instead of the on-premises pipelines.
  • B: Create a custom Amazon Machine Image (AMI) that contains the Airflow application and the code that the company needs to migrate. Use the custom AMI to deploy Amazon EC2 instances. Update the network connections to interact with the newly deployed EC2 instances.
  • C: Migrate the existing Airflow orchestration configuration into Amazon Managed Workflows for Apache Airflow (Amazon MWAA). Create the data quality checks during the ingestion to validate the data quality by using SQL tasks in Airflow.
  • D: Convert the pipelines to AWS Step Functions workflows. Recreate the data quality checks in SQL as Python based AWS Lambda functions.

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