AWS Certified Data Engineer - Associate DEA-C01 Practice Test

AWS Certified Data Engineer - Associate DEA-C01 Practice Test practice questions, page 14 of 15, with answer review and explanations.

Question 66

A financial company wants to use Amazon Athena to run on-demand SQL queries on a petabyte-scale dataset to support a business intelligence (BI) application. An AWS Glue job that runs during non-business hours updates the dataset once every day. The BI application has a standard data refresh frequency of 1 hour to comply with company policies. A data engineer wants to cost optimize the company's use of Amazon Athena without adding any additional infrastructure costs. Which solution will meet these requirements with the LEAST operational overhead?

  1. Configure an Amazon S3 Lifecycle policy to move data to the S3 Glacier Deep Archive storage class after 1 day.
  2. Use the query result reuse feature of Amazon Athena for the SQL queries.
  3. Add an Amazon ElastiCache cluster between the BI application and Athena.
  4. Change the format of the files that are in the dataset to Apache Parquet.

Question 67

A company's data engineer needs to optimize the performance of table SQL queries. The company stores data in an Amazon Redshift cluster. The data engineer cannot increase the size of the cluster because of budget constraints. The company stores the data in multiple tables and loads the data by using the EVEN distribution style. Some tables are hundreds of gigabytes in size. Other tables are less than 10 MB in size. Which solution will meet these requirements?

  1. Keep using the EVEN distribution style for all tables. Specify primary and foreign keys for all tables.
  2. Use the ALL distribution style for large tables. Specify primary and foreign keys for all tables.
  3. Use the ALL distribution style for rarely updated small tables. Specify primary and foreign keys for all tables.
  4. Specify a combination of distribution, sort, and partition keys for all tables.

Question 68

A company receives .csv files that contain physical address data. The data is in columns that have the following names: Door_No, Street_Name, City, and Zip_Code. The company wants to create a single column to store these values in the following format: Which solution will meet this requirement with the LEAST coding effort?

  1. Use AWS Glue DataBrew to read the files. Use the NEST_TO_ARRAY transformation to create the new column.
  2. Use AWS Glue DataBrew to read the files. Use the NEST_TO_MAP transformation to create the new column.
  3. Use AWS Glue DataBrew to read the files. Use the PIVOT transformation to create the new column.
  4. Write a Lambda function in Python to read the files. Use the Python data dictionary type to create the new column.

Question 69

A company receives call logs as Amazon S3 objects that contain sensitive customer information. The company must protect the S3 objects by using encryption. The company must also use encryption keys that only specific employees can access. Which solution will meet these requirements with the LEAST effort?

  1. Use an AWS CloudHSM cluster to store the encryption keys. Configure the process that writes to Amazon S3 to make calls to CloudHSM to encrypt and decrypt the objects. Deploy an IAM policy that restricts access to the CloudHSM cluster.
  2. Use server-side encryption with customer-provided keys (SSE-C) to encrypt the objects that contain customer information. Restrict access to the keys that encrypt the objects.
  3. Use server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the objects that contain customer information. Configure an IAM policy that restricts access to the KMS keys that encrypt the objects.
  4. Use server-side encryption with Amazon S3 managed keys (SSE-S3) to encrypt the objects that contain customer information. Configure an IAM policy that restricts access to the Amazon S3 managed keys that encrypt the objects.

Question 70

A company stores petabytes of data in thousands of Amazon S3 buckets in the S3 Standard storage class. The data supports analytics workloads that have unpredictable and variable data access patterns. The company does not access some data for months. However, the company must be able to retrieve all data within milliseconds. The company needs to optimize S3 storage costs. Which solution will meet these requirements with the LEAST operational overhead?

  1. Use S3 Storage Lens standard metrics to determine when to move objects to more cost-optimized storage classes. Create S3 Lifecycle policies for the S3 buckets to move objects to cost-optimized storage classes. Continue to refine the S3 Lifecycle policies in the future to optimize storage costs.
  2. Use S3 Storage Lens activity metrics to identify S3 buckets that the company accesses infrequently. Configure S3 Lifecycle rules to move objects from S3 Standard to the S3 Standard-Infrequent Access (S3 Standard-IA) and S3 Glacier storage classes based on the age of the data.
  3. Use S3 Intelligent-Tiering. Activate the Deep Archive Access tier.
  4. Use S3 Intelligent-Tiering. Use the default access tier.