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Data-Engineer-Associate Exam Results

Page: 4 / 16
Total 218 questions
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AWS Certified Data Engineer - Associate (DEA-C01) Questions and Answers

Question 13

A data engineer is launching an Amazon EMR duster. The data that the data engineer needs to load into the new cluster is currently in an Amazon S3 bucket. The data engineer needs to ensure that data is encrypted both at rest and in transit.

The data that is in the S3 bucket is encrypted by an AWS Key Management Service (AWS KMS) key. The data engineer has an Amazon S3 path that has a Privacy Enhanced Mail (PEM) file.

Which solution will meet these requirements?

Options:

A.

Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Create a second security configuration. Specify the Amazon S3 path of the PEM file for in-transit encryption. Create the EMR cluster, and attach both security configurations to the cluster.

B.

Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for local disk encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption. Use the security configuration during EMR cluster creation.

C.

Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption. Use the security configuration during EMR cluster creation.

D.

Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption. Create the EMR cluster, and attach the security configuration to the cluster.

Question 14

A company has a data lake in Amazon 53. The company uses AWS Glue to catalog data and AWS Glue Studio to implement data extract, transform, and load (ETL) pipelines.

The company needs to ensure that data quality issues are checked every time the pipelines run. A data engineer must enhance the existing pipelines to evaluate data quality rules based on predefined thresholds.

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

Options:

A.

Add a new transform that is defined by a SQL query to each Glue ETL job. Use the SQL query to implement a ruleset that includes the data quality rules that need to be evaluated.

B.

Add a new Evaluate Data Quality transform to each Glue ETL job. Use Data Quality Definition Language (DQDL) to implement a ruleset that includes the data quality rules that need to be evaluated.

C.

Add a new custom transform to each Glue ETL job. Use the PyDeequ library to implement a ruleset that includes the data quality rules that need to be evaluated.

D.

Add a new custom transform to each Glue ETL job. Use the Great Expectations library to implement a ruleset that includes the data quality rules that need to be evaluated.

Question 15

A company uses AWS Glue Apache Spark jobs to handle extract, transform, and load (ETL) workloads. The company has enabled logging and monitoring for all AWS Glue jobs. One of the AWS Glue jobs begins to fail. A data engineer investigates the error and wants to examine metrics for all individual stages within the job. How can the data engineer access the stage metrics?

Options:

A.

Examine the AWS Glue job and stage details in the Spark UI.

B.

Examine the AWS Glue job and stage metrics in Amazon CloudWatch.

C.

Examine the AWS Glue job and stage logs in AWS CloudTrail logs.

D.

Examine the AWS Glue job and stage details by using the run insights feature on the job.

Question 16

A company wants to combine data from multiple software as a service (SaaS) applications for analysis.

A data engineering team needs to use Amazon QuickSight to perform the analysis and build dashboards. A data engineer needs to extract the data from the SaaS applications and make the data available for QuickSight queries.

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

Options:

A.

Create AWS Lambda functions that call the required APIs to extract the data from the applications. Store the data in an Amazon S3 bucket. Use AWS Glue to catalog the data in the S3 bucket. Create a data source and a dataset in QuickSight

B.

Use AWS Lambda functions as Amazon Athena data source connectors to run federated queries against the SaaS applications. Create an Athena data source and a dataset in QuickSight.

C.

Use Amazon AppFlow to create a Row for each SaaS application. Set an Amazon S3 bucket as the destination. Schedule the flows to extract the data to the bucket. Use AWS Glue to catalog the data in the S3 bucket. Create a data source and a dataset in QuickSight.

D.

Export data the from the SaaS applications as Microsoft Excel files. Create a data source and a dataset in QuickSight by uploading the Excel files.

Page: 4 / 16
Total 218 questions
Get Data-Engineer-Associate Full Access Download Data-Engineer-Associate PDF