Data-Engineer-Associate Exam Dumps - AWS Certified Data Engineer - Associate (DEA-C01)

The company needs to ingest tables from an on-premises Oracle database into a data lake on Amazon S3 in Apache Parquet format. The most efficient solution, requiring the least manual effort, would be to use AWS Database Migration Service (DMS) for continuous data replication.

Option C: Create an AWS Database Migration Service (AWS DMS) task for ongoing replication. Set the Oracle database as the source. Set Amazon S3 as the target. Configure the task to write the data in Parquet format.AWS DMS can continuously replicate data from the Oracle database into Amazon S3, transforming it into Parquet format as it ingests the data. DMS simplifies the process by providing ongoing replication with minimal setup, and it automatically handles the conversion to Parquet format without requiring manual transformations or separate jobs. This option is the least effort solution since it automates both the ingestion and transformation processes.

Other options:

Option A (Apache Sqoop on EMR) involves more manual configuration and management, including setting up EMR clusters and writing Sqoop jobs.

Option B (AWS Glue bookmark job) involves configuring Glue jobs, which adds complexity. While Glue supports data transformations, DMS offers a more seamless solution for database replication.

Option D (RDS and Lambda triggers) introduces unnecessary complexity by involving RDS and Lambda for a task that DMS can handle more efficiently.

Lake Formation requires that S3 locations be registered before they can be accessed by AWS Glue crawlers, jobs, or queries. Registering the location associates it with the Lake Formation permissions framework.

“Before AWS Glue crawlers can access an S3 path managed by Lake Formation, the S3 location must be registered in Lake Formation.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

The Amazon Redshift Data API enables you to interact with your Amazon Redshift data warehouse in an easy and secure way. You can use the Data API to run SQL commands, such as loading data into tables, without requiring a persistent connection to the cluster. The Data API also integrates with Amazon EventBridge, which allows you to monitor the execution status of your SQL commands and trigger actions based on events. By using the Data API to publish an event to EventBridge, the data engineer can invoke the Lambda function that writes the load statuses to the DynamoDB table. This solution is scalable, reliable, and cost-effective. The other options are either not possible or not optimal. You cannot use a second Lambda function to invoke the first Lambda function based on CloudWatch or CloudTrail events, as these services do not capture the load status of Redshift tables. You can use the Data API to publish a message to an SQS queue, but this would require additional configuration and polling logic to invoke the Lambda function from the queue. This would also introduce additional latency and cost. References:

Using the Amazon Redshift Data API

Using Amazon EventBridge with Amazon Redshift

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 2: Data Store Management, Section 2.2: Amazon Redshift

Option D is correct because Aurora zero-ETL integration with Amazon Redshift is the AWS-managed feature built specifically to make transactional Aurora data available in Amazon Redshift Serverless in near real time. AWS documents that Aurora zero-ETL is a fully managed solution that makes transactional data available in the analytics destination after it is written to the Aurora cluster, eliminating the need to build and maintain complex ETL pipelines. AWS also states that Redshift zero-ETL supports a target data warehouse that can be a Redshift Serverless workgroup, which directly matches the question.

Option A and C add more operational components, such as DMS tasks, replication design, schema handling, and additional streaming infrastructure. Option B is also more operationally heavy because MSK Connect with Debezium requires Kafka-based CDC infrastructure and connector management. Since the requirement emphasizes serverless, near real-time analytics, and least operational overhead, the native zero-ETL integration is the best fit. This also aligns with the study guide’s focus on choosing AWS services that minimize management effort while supporting ingestion and analytics workflows.

Amazon Redshift query performance for large joins depends heavily on data distribution and query optimization. Using the KEY distribution style on both tables ensures that rows with the same join key are co-located on the same compute nodes, minimizing costly data redistribution during joins.

Selecting a high-cardinality column as the distribution key is critical to evenly distribute data across nodes and prevent data skew. Low-cardinality keys can cause hotspots where too much data resides on a small number of nodes, degrading performance.

Using EVEN distribution on the 10-billion-row table and ALL distribution on the 900-million-row table is inefficient because ALL distribution replicates the entire table to every node, which is not appropriate for large datasets. The Amazon Redshift query optimizer operates automatically and does not require manual selection.

Amazon Redshift Advisor analyzes workload patterns and provides actionable recommendations for distribution styles, sort keys, and query optimizations. Using Redshift Advisor allows the data engineer to apply proven optimizations based on actual cluster usage.

Therefore, combining KEY distribution with a high-cardinality join column and leveraging Amazon Redshift Advisor recommendations is the most effective solution to improve query performance.