Sample Questions from AWS Certified Data Engineer – Associate (DEA-C01)

Preview a few questions below — answers are revealed when you take the exam.

  1. A team needs to migrate a large-scale data warehouse from on-premises to AWS. They want to ensure minimal downtime during the migration. What approach should they take?

    • Utilize AWS Database Migration Service (DMS) to perform a homogeneous migration, ensuring data consistency and minimal downtime.
    • Perform a big-bang migration using AWS Snowball for data transfer, ensuring all data is migrated at once with minimal downtime.
    • Use AWS Glue to extract, transform, and load (ETL) data incrementally, minimizing downtime during the migration process.
    • Migrate the data warehouse to Amazon Redshift using a combination of AWS DMS and AWS Data Pipeline, ensuring a seamless transition with minimal downtime.

  2. Consider the situation where a data engineering team is tasked with optimizing the performance of an Amazon Redshift cluster. Which method solves it best?

    • Implementing columnar storage and distributing data across nodes to enhance query performance.
    • Increasing the number of compute nodes without optimizing the data distribution and query patterns.
    • Using Amazon Redshift Spectrum to query data directly from Amazon S3 without optimizing the underlying Redshift cluster.
    • Relying solely on Redshift's automatic table optimization features without manual intervention.

  3. Identify the AWS service that allows for serverless data processing with Apache Spark and Apache Hive.

    • AWS Glue
    • Amazon EMR
    • AWS Lambda
    • Amazon SageMaker

  4. What command should be used to query data stored in Amazon S3 using Amazon Athena?

    • SELECT * FROM s3_bucket.table_name;
    • Athena.query('SELECT * FROM s3_bucket.table_name');
    • athena.query('SELECT * FROM s3_bucket.table_name');
    • SELECT * FROM s3_bucket.table_name LIMIT 10;

  5. A data engineer needs to implement a data pipeline that processes streaming data in real-time. What configuration is most appropriate for this requirement?

    • Use Amazon Kinesis Data Streams to capture and process streaming data, combined with Amazon Kinesis Data Analytics for real-time processing.
    • Deploy a batch processing pipeline using AWS Glue and Amazon S3 for storing processed data.
    • Utilize Amazon SQS for message queuing and AWS Lambda for processing messages in near real-time.
    • Implement a data lake architecture using Amazon S3 and AWS Glue for ETL processing.