Amazon DATA-ENGINEER-ASSOCIATE Online Practice Questions and Exam Preparation

Amazon DATA-ENGINEER-ASSOCIATE Online Questions & Answers

• Question 381:

  A company regularly loads data into an Amazon DynamoDB table. The company must retain the data in DynamoDB for 1 year. After 1 year, the company must archive the data for 5 years. After 5 years, the company must delete the data.

  Which solution will meet these requirements in the MOST operationally efficient way?

  A. Include a timestamp with the data that is loaded into DynamoDB. Create an AWS Lambda function to read the timestamps and move items that are 1 year old to Amazon S3 Glacier Flexible Retrieval. Create an S3 Lifecycle policy to delete the items after 5 years.
  B. Enable Amazon DynamoDB Streams. Configure a TTL rule to delete items from the DynamoDB table after 1 year. Use an AWS Lambda function to consume the stream and send items to Amazon S3 Glacier Flexible Retrieval. Delete the items from S3 Glacier Flexible Retrieval after 5 years.
  C. Include a timestamp with the data that is loaded into DynamoDB. Use an AWS Lambda function to read timestamps and move items that are 1 year old directly to Amazon S3 Glacier Deep Archive. Configure the function to delete items after 5 years.
  D. Enable Amazon DynamoDB Streams. Configure a TTL rule to delete items from the DynamoDB table after 1 year. Use an AWS Lambda function to consume the stream and send items directly to Amazon S3 Glacier Deep Archive. Create an S3 Lifecycle policy to delete the items after 5 years.
  D. Enable Amazon DynamoDB Streams. Configure a TTL rule to delete items from the DynamoDB table after 1 year. Use an AWS Lambda function to consume the stream and send items directly to Amazon S3 Glacier Deep Archive. Create an S3 Lifecycle policy to delete the items after 5 years.

• Question 382:

  A company implements a data mesh that has a central governance account. The company needs to catalog all data in the governance account. The governance account uses AWS Lake Formation to centrally share data and grant access permissions.

  The company has created a new data product that includes a group of Amazon Redshift Serverless tables.

  A data engineer needs to share the data product with a marketing team. The marketing team must have access to only a subset of columns. The data engineer needs to share the same data product with a compliance team. The compliance team must have access to a different subset of columns than the marketing team needs access to.

  Which combination of steps should the data engineer take to meet these requirements? (Choose Two.)

  A. Create views of the tables that need to be shared. Include only the required columns.
  B. Create an Amazon Redshift data than that includes the tables that need to be shared.
  C. Create an Amazon Redshift managed VPC endpoint in the marketing team's account. Grant the marketing team access to the views.
  D. Share the Amazon Redshift data share to the Lake Formation catalog in the governance account.
  E. Share the Amazon Redshift data share to the Amazon Redshift Serverless workgroup in the marketing team's account.
  B. Create an Amazon Redshift data than that includes the tables that need to be shared.
  D. Share the Amazon Redshift data share to the Lake Formation catalog in the governance account.

• Question 383:

  To speed up Amazon Athena queries on uncompressed .

  csv files where most queries select a specific column, which solution is best?

  A. Convert to JSON with Snappy compression
  B. Compress .csv with Snappy
  C. Convert to Apache Parquet with Snappy compression
  D. Compress .csv with gzip
  C. Convert to Apache Parquet with Snappy compression

• Question 384:

  A company currently stores all of its data in Amazon S3 by using the S3 Standard storage class.

  A data engineer examined data access patterns to identify trends. During the first 6 months, most data files are accessed several times each day. Between 6 months and 2 years, most data files are accessed once or twice each month. After 2 years, data files are accessed only once or twice each year.

  The data engineer needs to use an S3 Lifecycle policy to develop new data storage rules. The new storage solution must continue to provide high availability.

  Which solution will meet these requirements in the MOST cost-effective way?

  A. Transition objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. Transfer objects to S3 Glacier Flexible Retrieval after 2 years.
  B. Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. Transfer objects to S3 Glacier Flexible Retrieval after 2 years.
  C. Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. Transfer objects to S3 Glacier Deep Archive after 2 years.
  D. Transition objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. Transfer objects to S3 Glacier Deep Archive after 2 years.
  C. Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. Transfer objects to S3 Glacier Deep Archive after 2 years.

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  Explanation

  To achieve the most cost-effective storage solution, the data engineer needs to use an S3 Lifecycle policy that transitions objects to lower-cost storage classes based on their access patterns, and deletes them when they are no longer needed. The storage classes should also provide high availability, which means they should be resilient to the loss of data in a single Availability Zone. Therefore, the solution must include the following steps: Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. S3 Standard-IA is designed for data that is accessed less frequently, but requires rapid access when needed. It offers the same high durability, throughput, and low latency as S3 Standard, but with a lower storage cost and a retrieval fee. Therefore, it is suitable for data files that are accessed once or twice each month. S3 Standard-IA also provides high availability, as it stores data redundantly across multiple Availability Zones.

  Transfer objects to S3 Glacier Deep Archive after 2 years. S3 Glacier Deep Archive is the lowest-cost storage class that offers secure and durable storage for data that is rarely accessed and can tolerate a 12- hour retrieval time. It is ideal for long-term archiving and digital preservation. Therefore, it is suitable for data files that are accessed only once or twice each year. S3 Glacier Deep Archive also provides high availability, as it stores data across at least three geographically dispersed Availability Zones.

  Delete objects when they are no longer needed. The data engineer can specify an expiration action in the S3 Lifecycle policy to delete objects after a certain period of time. This will reduce the storage cost and comply with any data retention policies.

  Option C is the only solution that includes all these steps. Therefore, option C is the correct answer.

  Option A is incorrect because it transitions objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. S3 One Zone-IA is similar to S3 Standard-IA, but it stores data in a single Availability Zone.

  This means it has a lower availability and durability than S3 Standard-IA, and it is not resilient to the loss of data in a single Availability Zone. Therefore, it does not provide high availability as required.

  Option B is incorrect because it transfers objects to S3 Glacier Flexible Retrieval after 2 years. S3 Glacier Flexible Retrieval is a storage class that offers secure and durable storage for data that is accessed infrequently and can tolerate a retrieval time of minutes to hours. It is more expensive than S3 Glacier Deep Archive, and it is not suitable for data that is accessed only once or twice each year. Therefore, it is not the most cost-effective option.

  Option D is incorrect because it combines the errors of option A and B. It transitions objects to S3 One Zone-IA after 6 months, which does not provide high availability, and it transfers objects to S3 Glacier Flexible Retrieval after 2 years, which is not the most cost-effective option.

  References:

  1: Amazon S3 storage classes - Amazon Simple Storage Service

  2: Amazon S3 Standard-Infrequent Access (S3 Standard-IA) - Amazon Simple Storage Service

  3: Amazon S3 Glacier and S3 Glacier Deep Archive - Amazon Simple Storage Service [4]: Expiring objects - Amazon Simple Storage Service [5]: Managing your storage lifecycle - Amazon Simple Storage Service [6]: Examples of S3 Lifecycle configuration - Amazon Simple Storage Service [7]: Amazon S3 Lifecycle further optimizes storage cost savings with new features - What's New with AWS

• Question 385:

  A company uses Amazon S3 to store data and Amazon QuickSight to create visualizations.

  The company has an S3 bucket in an AWS account named Hub-Account. The S3 bucket is encrypted by an AWS Key Management Service (AWS KMS) key. The company's QuickSight instance is in a separate account named BI-Account

  The company updates the S3 bucket policy to grant access to the QuickSight service role. The company wants to enable cross-account access to allow QuickSight to interact with the S3 bucket.

  Which combination of steps will meet this requirement? (Choose Two.)

  A. Use the existing AWS KMS key to encrypt connections from QuickSight to the S3 bucket.
  B. Add the 53 bucket as a resource that the QuickSight service role can access.
  C. Use AWS Resource Access Manager (AWS RAM) to share the S3 bucket with the Bl-Account account.
  D. Add an IAM policy to the QuickSight service role to give QuickSight access to the KMS key that encrypts the S3 bucket.
  E. Add the KMS key as a resource that the QuickSight service role can access.
  B. Add the 53 bucket as a resource that the QuickSight service role can access.
  E. Add the KMS key as a resource that the QuickSight service role can access.

• Question 386:

  A data lake team needs centralized permissions for Amazon S3 tables queried by Athena and Amazon EMR. The team also needs fine-grained table, column, and row controls without building separate authorization logic in each query engine.

  Which AWS service should the team use?

  A. AWS Lake Formation
  B. AWS Cost Explorer
  C. AWS Transfer Family
  D. Amazon CloudFront
  A. AWS Lake Formation

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  Explanation

  Lake Formation provides centralized data lake permissions and fine-grained access controls for services such as Athena, Amazon EMR, Amazon Redshift, and Amazon S3. Cost Explorer analyzes spending.

  Transfer Family provides managed file transfer endpoints. CloudFront is a content delivery network and does not govern data lake query access.

• Question 387:

  A company stores employee data in Amazon Redshift A table named Employee uses columns named Region ID, Department ID, and Role ID as a compound sort key.

  Which queries will MOST increase the speed of a query by using a compound sort key of the table? (Choose Two.)

  A. Select *from Employee where Region ID='North America';
  B. Select *from Employee where Region ID='North America' and Department ID=20;
  C. Select *from Employee where Department ID=20 and Region ID='North America';
  D. Select *from Employee where Role ID=50;
  E. Select *from Employee where Region ID='North America' and Role ID=50;
  B. Select *from Employee where Region ID='North America' and Department ID=20;
  E. Select *from Employee where Region ID='North America' and Role ID=50;

• Question 388:

  A company receives a data file from a partner each day in an Amazon S3 bucket. The company uses a daily AW5 Glue extract, transform, and load (ETL) pipeline to clean and transform each data file. The output of the ETL pipeline is written to a CSV file named Dairy.csv in a second 53 bucket.

  Occasionally, the daily data file is empty or is missing values for required fields. When the file is missing data, the company can use the previous day's CSV file.

  A data engineer needs to ensure that the previous day's data file is overwritten only if the new daily file is complete and valid.

  Which solution will meet these requirements with the LEAST effort?

  A. Invoke an AWS Lambda function to check the file for missing data and to fill in missing values in required fields.
  B. Configure the AWS Glue ETL pipeline to use AWS Glue Data Quality rules. Develop rules in Data Quality Definition Language (DQDL) to check for missing values in required files and empty files.
  C. Use AWS Glue Studio to change the code in the ETL pipeline to fill in any missing values in the required fields with the most common values for each field.
  D. Run a SQL query in Amazon Athena to read the CSV file and drop missing rows. Copy the corrected CSV file to the second S3 bucket.
  B. Configure the AWS Glue ETL pipeline to use AWS Glue Data Quality rules. Develop rules in Data Quality Definition Language (DQDL) to check for missing values in required files and empty files.

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  Explanation

  Problem Analysis:

  The company runs a daily AWS Glue ETL pipeline to clean and transform files received in an S3 bucket.

  If a file is incomplete or empty , the previous day's file should be retained.

  Need a solution to validate files before overwriting the existing file.

  Key Considerations:

  Automate data validation with minimal human intervention.

  Use built-in AWS Glue capabilities for ease of integration.

  Ensure robust validation for missing or incomplete data.

  Solution Analysis:

  Option A: Lambda Function for Validation Lambda can validate files, but it would require custom code.

  Does not leverage AWS Glue's built-in features, adding operational complexity.

  Option B: AWS Glue Data Quality Rules AWS Glue Data Quality allows defining Data Quality Definition Language (DQDL) rules.

  Rules can validate if required fields are missing or if the file is empty.

  Automatically integrates into the existing ETL pipeline.

  If validation fails, retain the previous day's file.

  Option C: AWS Glue Studio with Filling Missing Values Modifying ETL code to fill missing values with most common values risks introducing inaccuracies.

  Does not handle empty files effectively.

  Option D: Athena Query for Validation Athena can drop rows with missing values, but this is a post-hoc solution.

  Requires manual intervention to copy the corrected file to S3, increasing complexity.

  Final Recommendation:

  Use AWS Glue Data Quality to define validation rules in DQDL for identifying missing or incomplete data.

  This solution integrates seamlessly with the ETL pipeline and minimizes manual effort.

  Implementation Steps:

  Enable AWS Glue Data Quality in the existing ETL pipeline.

  Define DQDL Rules , such as: Check if a file is empty.

  Verify required fields are present and non-null.

  Configure the pipeline to proceed with overwriting only if the file passes validation.

  In case of failure, retain the previous day's file.

  References:

  AWS Glue Data Quality Overview

  Defining DQDL Rules

  AWS Glue Studio Documentation

• Question 389:

  A global retailer stores sales transactions in Amazon S3 and catalogs the tables in AWS Glue Data Catalog. Analysts from each country must query only rows for their own country, and the governance team wants to avoid creating separate copies of the data.

  Which solution will meet these requirements with the LEAST operational effort?

  A. Use AWS Lake Formation data filters to enforce row-level access on the shared catalog tables.
  B. Create a separate S3 bucket for each country and copy each country's rows into the matching bucket every day.
  C. Create one Amazon Redshift view for each country and grant each analyst direct access to the underlying S3 data.
  D. Use S3 Lifecycle rules to transition records for each country to different storage classes.
  A. Use AWS Lake Formation data filters to enforce row-level access on the shared catalog tables.

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  Explanation

  Lake Formation data filters can enforce row-level, column-level, and cell-level access for governed tables without duplicating the dataset. Per-country buckets and copies increase storage and pipeline overhead.

  Redshift views do not govern direct S3 access unless the data lake permissions are also controlled. S3 Lifecycle policies manage retention and storage class transitions, not row-level authorization.

• Question 390:

  A data engineer uses Amazon Redshift to run resource-intensive analytics processes once every month.

  Every month, the data engineer creates a new Redshift provisioned cluster. The data engineer deletes the Redshift provisioned cluster after the analytics processes are complete every month. Before the data engineer deletes the cluster each month, the data engineer unloads backup data from the cluster to an Amazon S3 bucket.

  The data engineer needs a solution to run the monthly analytics processes that does not require the data engineer to manage the infrastructure manually.

  Which solution will meet these requirements with the LEAST operational overhead?

  A. Use Amazon Step Functions to pause the Redshift cluster when the analytics processes are complete and to resume the cluster to run new processes every month.
  B. Use Amazon Redshift Serverless to automatically process the analytics workload.
  C. Use the AWS CLI to automatically process the analytics workload.
  D. Use AWS CloudFormation templates to automatically process the analytics workload.
  B. Use Amazon Redshift Serverless to automatically process the analytics workload.

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  Explanation

  Amazon Redshift Serverless is a new feature of Amazon Redshift that enables you to run SQL queries on data in Amazon S3 without provisioning or managing any clusters. You can use Amazon Redshift Serverless to automatically process the analytics workload,as it scales up and down the compute resources based on the query demand, and charges you only for the resources consumed. This solution will meet the requirements with the least operational overhead, as it does not require the data engineer to create, delete, pause, or resume any Redshift clusters, or to manage any infrastructure manually. You can use the Amazon Redshift Data API to run queries from the AWS CLI, AWS SDK, or AWS Lambda functions.

  The other options are not optimal for the following reasons:

  A. Use Amazon Step Functions to pause the Redshift cluster when the analytics processes are complete and to resume the cluster to run new processes every month. This option is not recommended, as it would still require the data engineer to create and delete a new Redshift provisioned cluster every month, which can incur additional costs and time. Moreover, this option would require the data engineer to use Amazon Step Functions to orchestrate the workflow of pausing and resuming the cluster, which can add complexity and overhead.

  C. Use the AWS CLI to automatically process the analytics workload. This option is vague and does not specify how the AWS CLI is used to process the analytics workload. The AWS CLI can be used to run queries on data in Amazon S3 using Amazon Redshift Serverless, Amazon Athena, or Amazon EMR, but each of these services has different features and benefits. Moreover, this option does not address the requirement of not managing the infrastructure manually, as the data engineer may still need to provision and configure some resources, such as Amazon EMR clusters or Amazon Athena workgroups.

  D. 3. Amazon Step Functions

  4. AWS CLI

  5. AWS CloudFormation