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

1. 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?

A) 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.
B) 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.
C) Create an Athena workgroup for each use case. Apply tags to the workgroup. Create an 1AM policy that uses the tags to apply appropriate permissions to the workgroup.
D) Create an JAM role for each use case. Assign appropriate permissions to the role for each use case.
Associate the role with Athena.

2. 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?

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 details by using the run insights feature on the job.
D) Examine the AWS Glue job and stage logs in AWS CloudTrail logs.

3. A gaming company uses AWS Glue to perform read and write operations on Apache Iceberg tables for real- time streaming data. The data in the Iceberg tables is stored in Apache Parquet format. The company is experiencing slow query performance.
Which solutions will improve query performance? (Select TWO)

A) Use AWS Glue Data Catalog to automatically optimize indexes for the Iceberg tables.
B) Use AWS Glue Data Catalog to generate column-level statistics for the Iceberg tables on a schedule.
C) Use AWS Glue Data Catalog to enable copy-on-write for the Iceberg tables.
D) Use AWS Glue Data Catalog to generate views for the Iceberg tables.
E) Use AWS Glue Data Catalog to automatically compact the Iceberg tables.

4. A company stores Apache Parquet files in an Amazon S3 data lake. The data lake receives thousands of files from multiple sources every hour. The files range in size from 50 KB to 100 KB.
The company is evaluating the implementation of Apache Iceberg tables for the data lake. The company is using AWS Glue Data Catalog as part of the evaluation. The company needs a solution to optimize query performance in Iceberg. The solution must ensure that Iceberg table performance does not degrade when more files are added over time.
Which solution will meet these requirements?

A) Configure Iceberg table properties to enable automatic compaction based on thresholds for file size and the number of files.
B) Implement a partition strategy in Amazon S3. Run an AWS Glue crawler to update the Data Catalog every 5 minutes.
C) Use an AWS Glue job to compact the files into a standard size of 512 MB at the end of each day. Run an AWS Glue crawler to update the Data Catalog.
D) Configure the Data Catalog to automatically compact the files every minute.

5. A company ingests data from multiple data sources and stores the data in an Amazon S3 bucket. An AWS Glue extract, transform, and load (ETL) job transforms the data and writes the transformed data to an Amazon S3 based data lake. The company uses Amazon Athena to query the data that is in the data lake.
The company needs to identify matching records even when the records do not have a common unique identifier.
Which solution will meet this requirement?

A) Train and use the AWS Lake Formation FindMatches transform in the ETL job.
B) Partition tables and use the ETL job to partition the data on a unique identifier.
C) Use Amazon Made pattern matching as part of the ETL job.
D) Train and use the AWS Glue PySpark Filter class in the ETL job.

Solutions: