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

1. A data engineer is designing a new data lake architecture for a company. The data engineer plans to use Apache Iceberg tables and AWS Glue Data Catalog to achieve fast query performance and enhanced metadata handling. The data engineer needs to query historical data for trend analysis and optimize storage costs for a large volume of event data.
Which solution will meet these requirements with the LEAST development effort?

A) Run a custom AWS Glue job to compact Iceberg table data files.
B) Define partitioning schemes based on event type and event date.
C) Store Iceberg table data files in Amazon S3 Intelligent-Tiering.
D) Use AWS Glue Data Catalog to automatically optimize Iceberg storage.

2. A company has a data processing pipeline that runs multiple SQL queries in sequence against an Amazon Redshift cluster. The company merges with a second company. The original company modifies a query that aggregates sales revenue data to join sales tables from both companies.
The sales table for the first company is named Table S1 and contains 10 billion records. The sales table for the second company is named Table S2 and contains 900 million records. The query becomes slow after the modification.
A data engineer must improve the query performance.
Which solutions will meet these requirements? (Select TWO)

A) Use the KEY distribution style for both sales tables. Select a high-cardinality column to use for the join.
B) Use the KEY distribution style for both sales tables. Select a low-cardinality column to use for the join.
C) Use Amazon Redshift Advisor to review and select optimizations to implement.
D) Use the EVEN distribution style for Table S1. Use the ALL distribution style for Table S2.
E) Use the Amazon Redshift query optimizer to review and select optimizations to implement.

3. A data engineer needs to securely transfer 5 TB of data from an on-premises data center to an Amazon S3 bucket. Approximately 5% of the data changes every day. Updates to the data need to be regularly proliferated to the S3 bucket. The data includes files that are in multiple formats. The data engineer needs to automate the transfer process and must schedule the process to run periodically.
Which AWS service should the data engineer use to transfer the data in the MOST operationally efficient way?

A) AWS Glue
B) AWS Direct Connect
C) Amazon S3 Transfer Acceleration
D) AWS DataSync

4. A data engineer is building a serverless, multi-step extract, transform, and load (ETL) pipeline. The pipeline extracts data from an Amazon S3 data lake and transforms the data by using AWS Glue ETL jobs. The pipeline then loads the results into an Amazon Redshift database. The data engineer needs to orchestrate the serverless ETL workflow.
Which solutions will meet these requirements? (Select TWO.)

A) Build an orchestration solution by using AWS CodePipeline to coordinate the ETL pipeline and infrastructure changes based on the dependencies.
B) Use Amazon EventBridge rules to invoke the AWS Glue ETL jobs based on S3 object creation events.
Configure the rules to chain the AWS Glue ETL jobs in sequence and handle complex job dependencies.
C) Use AWS Glue workflows to create a graph of the ETL tasks that visually represents the dependencies between jobs and the job triggers.
D) Provision an always-on Amazon EC2 instance. Create a cron job that invokes the AWS Glue ETL jobs in sequence based on a predefined schedule.
E) Implement the workflow by using AWS Step Functions. Configure Step Functions to coordinate the AWS Glue ETL jobs and handle error conditions with automatic retries.

5. A company uses Amazon RDS for MySQL as the database for a critical application. The database workload is mostly writes, with a small number of reads.
A data engineer notices that the CPU utilization of the DB instance is very high. The high CPU utilization is slowing down the application. The data engineer must reduce the CPU utilization of the DB Instance.
Which actions should the data engineer take to meet this requirement? (Choose two.)

A) Upgrade to a larger instance size.
B) Modify the database schema to include additional tables and indexes.
C) Reboot the RDS DB instance once each week.
D) Use the Performance Insights feature of Amazon RDS to identify queries that have high CPU utilization. Optimize the problematic queries.
E) Implement caching to reduce the database query load.

Solutions: