PASS-GUARANTEED DATA-ENGINEER-ASSOCIATE GUIDE MATERIALS: AWS CERTIFIED DATA ENGINEER - ASSOCIATE (DEA-C01) ARE THE MOST AUTHENTIC EXAM DUMPS - PREPAWAYETE

Pass-guaranteed Data-Engineer-Associate Guide Materials: AWS Certified Data Engineer - Associate (DEA-C01) are the most authentic Exam Dumps - PrepAwayETE

Pass-guaranteed Data-Engineer-Associate Guide Materials: AWS Certified Data Engineer - Associate (DEA-C01) are the most authentic Exam Dumps - PrepAwayETE

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q164-Q169):

NEW QUESTION # 164
A data engineer is configuring an AWS Glue job to read data from an Amazon S3 bucket. The data engineer has set up the necessary AWS Glue connection details and an associated IAM role. However, when the data engineer attempts to run the AWS Glue job, the data engineer receives an error message that indicates that there are problems with the Amazon S3 VPC gateway endpoint.
The data engineer must resolve the error and connect the AWS Glue job to the S3 bucket.
Which solution will meet this requirement?

  • A. Review the AWS Glue job code to ensure that the AWS Glue connection details include a fully qualified domain name.
  • B. Configure an S3 bucket policy to explicitly grant the AWS Glue job permissions to access the S3 bucket.
  • C. Update the AWS Glue security group to allow inbound traffic from the Amazon S3 VPC gateway endpoint.
  • D. Verify that the VPC's route table includes inbound and outbound routes for the Amazon S3 VPC gateway endpoint.

Answer: D

Explanation:
The error message indicates that the AWS Glue job cannot access the Amazon S3 bucket through the VPC endpoint. This could be because the VPC's route table does not have the necessary routes to direct the traffic to the endpoint. To fix this, the data engineer must verify that the route table has an entry for the Amazon S3 service prefix (com.amazonaws.region.s3) with the target as the VPC endpoint ID. This will allow the AWS Glue job to use the VPC endpoint to access the S3 bucket without going through the internet or a NAT gateway. For more information, see Gateway endpoints. References:
Troubleshoot the AWS Glue error "VPC S3 endpoint validation failed"
Amazon VPC endpoints for Amazon S3
[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]


NEW QUESTION # 165
A company stores CSV files in an Amazon S3 bucket. A data engineer needs to process the data in the CSV files and store the processed data in a new S3 bucket.
The process needs to rename a column, remove specific columns, ignore the second row of each file, create a new column based on the values of the first row of the data, and filter the results by a numeric value of a column.
Which solution will meet these requirements with the LEAST development effort?

  • A. Use AWS Glue Python jobs to read and transform the CSV files.
  • B. Use an AWS Glue workflow to build a set of jobs to crawl and transform the CSV files.
  • C. Use AWS Glue DataBrew recipes to read and transform the CSV files.
  • D. Use an AWS Glue custom crawler to read and transform the CSV files.

Answer: C

Explanation:
The requirement involves transforming CSV files by renaming columns, removing rows, and other operations with minimal development effort. AWS Glue DataBrew is the best solution here because it allows you to visually create transformation recipes without writing extensive code.
* Option D: Use AWS Glue DataBrew recipes to read and transform the CSV files.DataBrew provides a visual interface where you can build transformation steps (e.g., renaming columns, filtering rows, creating new columns, etc.) as a "recipe" that can be applied to datasets, making it easy to handle complex transformations on CSV files with minimal coding.
Other options (A, B, C) involve more manual development and configuration effort (e.g., writing Python jobs or creating custom workflows in Glue) compared to the low-code/no-code approach of DataBrew.
References:
* AWS Glue DataBrew Documentation


NEW QUESTION # 166
A data engineer needs to build an enterprise data catalog based on the company's Amazon S3 buckets and Amazon RDS databases. The data catalog must include storage format metadata for the data in the catalog.
Which solution will meet these requirements with the LEAST effort?

  • A. Use an AWS Glue crawler to build a data catalog. Use AWS Glue crawler classifiers to recognize the format of data and store the format in the catalog.
  • B. Use an AWS Glue crawler to scan the S3 buckets and RDS databases and build a data catalog. Use data stewards to inspect the data and update the data catalog with the data format.
  • C. Use Amazon Macie to build a data catalog and to identify sensitive data elements. Collect the data format information from Macie.
  • D. Use scripts to scan data elements and to assign data classifications based on the format of the data.

Answer: A

Explanation:
To build an enterprise data catalog with metadata for storage formats, the easiest and most efficient solution is using an AWS Glue crawler. The Glue crawler can scan Amazon S3 buckets and Amazon RDS databases to automatically create a data catalog that includes metadata such as the schema and storage format (e.g., CSV, Parquet, etc.). By using AWS Glue crawler classifiers, you can configure the crawler to recognize the format of the data and store this information directly in the catalog.
Option B: Use an AWS Glue crawler to build a data catalog. Use AWS Glue crawler classifiers to recognize the format of data and store the format in the catalog.
This option meets the requirements with the least effort because Glue crawlers automate the discovery and cataloging of data from multiple sources, including S3 and RDS, while recognizing various file formats via classifiers.
Other options (A, C, D) involve additional manual steps, like having data stewards inspect the data, or using services like Amazon Macie that focus more on sensitive data detection rather than format cataloging.
Reference:
AWS Glue Crawler Documentation
AWS Glue Classifiers


NEW QUESTION # 167
A data engineer uses Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to run data pipelines in an AWS account. A workflow recently failed to run. The data engineer needs to use Apache Airflow logs to diagnose the failure of the workflow. Which log type should the data engineer use to diagnose the cause of the failure?

  • A. YourEnvironmentName-WebServer
  • B. YourEnvironmentName-DAGProcessing
  • C. YourEnvironmentName-Scheduler
  • D. YourEnvironmentName-Task

Answer: D

Explanation:
In Amazon Managed Workflows for Apache Airflow (MWAA), the type of log that is most useful for diagnosing workflow (DAG) failures is the Task logs. These logs provide detailed information on the execution of each task within the DAG, including error messages, exceptions, and other critical details necessary for diagnosing failures.
* Option D: YourEnvironmentName-TaskTask logs capture the output from the execution of each task within a workflow (DAG), which is crucial for understanding what went wrong when a DAG fails.
These logs contain detailed execution information, including errors and stack traces, making them the best source for debugging.
Other options (WebServer, Scheduler, and DAGProcessing logs) provide general environment-level logs or logs related to scheduling and DAG parsing, but they do not provide the granular task-level execution details needed for diagnosing workflow failures.
References:
* Amazon MWAA Logging and Monitoring
* Apache Airflow Task Logs


NEW QUESTION # 168
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? (Select TWO.)

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

Answer: A,E

Explanation:
The company is using a data mesh architecture with AWS Lake Formation for governance and needs to share specific subsets of data with different teams (marketing and compliance) using Amazon Redshift Serverless.
* Option A: Create views of the tables that need to be shared. Include only the required columns.
Creating views in Amazon Redshift that include only the necessary columns allows for fine-grained access control. This method ensures that each team has access to only the data they are authorized to view.
* Option E: Share the Amazon Redshift data share to the Amazon Redshift Serverless workgroup in the marketing team's account.Amazon Redshift data sharing enables live access to data across Redshift clusters or Serverless workgroups. By sharing data with specific workgroups, you can ensure that the marketing team and compliance team each access the relevant subset of data based on the views created.
* Option B (creating a Redshift data share) is close but does not address the fine-grained column-level access.
* Option C (creating a managed VPC endpoint) is unnecessary for sharing data with specific teams.
* Option D (sharing with the Lake Formation catalog) is incorrect because Redshift data shares do not integrate directly with Lake Formation catalogs; they are specific to Redshift workgroups.
References:
* Amazon Redshift Data Sharing
* AWS Lake Formation Documentation


NEW QUESTION # 169
......

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