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

The correct answer is A because the requirement specifically says that traffic between Lambda and the Amazon Redshift Data API must remain on the AWS network. AWS documentation states that you can call the Redshift Data API with Amazon VPC endpoints and that this keeps traffic between applications in the VPC and the Data API in the AWS network without public IP addresses. The Data API also removes the need to manage database drivers, persistent connections, and network configuration from Lambda. Creating a VPC endpoint for Lambda is not how Lambda privately reaches Redshift Data API. ODBC and JDBC drivers introduce connection management and do not address the stated Data API traffic requirement. Therefore, Data API plus the Redshift Data API VPC endpoint is correct.

 This solution meets the requirements of optimizing the performance of table SQL queries without increasing the size of the cluster. By using the ALL distribution style for rarely updated small tables, you can ensure that the entire table is copied to every node in the cluster, which eliminates the need for data redistribution during joins. This can improve query performance significantly, especially for frequently joined dimension tables. However, using the ALL distribution style also increases the storage space and the load time, so it is only suitable for small tables that are not updated frequently or extensively. By specifying primary and foreign keys for all tables, you can help the query optimizer to generate better query plans and avoid unnecessary scans or joins. You can also use the AUTO distribution style to let Amazon Redshift choose the optimal distribution style based on the table size and the query patterns. References:

Choose the best distribution style

Distribution styles

Working with data distribution styles

Option B is correct because CloudWatch Logs subscription filters are the native way to send only matching log events to a downstream destination such as Amazon Data Firehose. AWS states that a subscription filter defines the filter pattern for which log events get delivered and where they are sent. AWS also documents that CloudWatch Logs events can be sent to Firehose by using subscription filters. This directly satisfies the requirement to deliver only relevant logs. Once the logs are in Firehose, AWS documents that Firehose can invoke an AWS Lambda function to transform incoming source data before delivering it to the destination, which satisfies the requirement to deliver the logs in a compatible format for analysis.

Option A is incorrect because S3 bucket rules do not filter CloudWatch log events by timestamp for ingestion control, and using S3 triggers adds unnecessary post-delivery processing. Option C is invalid because the subscription filter applies at the CloudWatch Logs log group, not by “monitoring the Firehose stream.” Option D is incorrect because Firehose does not natively ingest only CloudWatch log groups based on tags or automatically reformat arbitrary logs without a transformation step. The most reliable and AWS-native design is subscription filtering + Firehose + Lambda transformation.

Problem Analysis:

The goal is to create a new empty table in Athena with the same schema as an existing table (old_table).

The solution must avoid copying any data.

Key Considerations:

CREATE TABLE AS (CTAS) is commonly used in Athena for creating new tables based on an existing table.

Adding the WITH NO DATA clause ensures only the schema is copied, without transferring any data.

Solution Analysis:

Option A: Copies both schema and data. Does not meet the requirement for an empty table.

Option B: Inserts data into an existing table, which does not create a new table.

Option C: Creates an empty table but does not copy the schema.

Option D: Creates a new table with the same schema and ensures it is empty by using WITH NO DATA.

Final Recommendation:

Use D. CREATE TABLE new_table AS (SELECT * FROM old_table) WITH NO DATA to create an empty table with the same schema.

Athena CTAS Queries

CREATE TABLE Statement in Athena

AWS Step Functions allows you to orchestrate ETL jobs and workflows by integrating with AWS Glue and EventBridge. It supports both scheduled and event-driven executions with minimal operational overhead, making it ideal for hybrid triggering.

AWS Glue workflows are limited in orchestration logic.

MWAA (option B) is powerful but requires more infrastructure management.

EMR Serverless + Lambda (option D) is possible but involves more components.

“Use Step Functions to coordinate multiple services like Glue and EventBridge for event-driven or scheduled workflows.”

Option B is correct because Amazon Managed Service for Apache Flink is the AWS managed service built for real-time stream processing, including stateful computations over streaming data. AWS documentation describes Apache Flink as a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. AWS also states that Managed Service for Apache Flink can read from a Kinesis data stream, apply transformations such as filtering, aggregation, or enrichment, and write the transformed output to a sink. This maps exactly to the requirement for complex, stateful, near-real-time transformations on data coming from Amazon Kinesis Data Streams before storing it in S3.

Option A and D are less suitable because scheduled Glue or EMR Spark jobs are not the least-operational or best-fit solution for continuous near-real-time stateful streaming transformations. Option C can work for lightweight event processing, but Lambda is not the best fit for more complex and stateful streaming logic compared with Flink’s native state management and streaming semantics. Therefore, Managed Service for Apache Flink is the correct managed AWS answer.

According to the guide:

“For integrating streaming data from Amazon MSK into Amazon Redshift efficiently and cost-effectively, AWS Glue streaming jobs can process and transform the data, storing it in Amazon S3. Amazon Redshift Spectrum can then directly query the data from S3, minimizing operational overhead and reducing storage costs.”

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

This setup offers:

Low latency via Glue streaming.

Low storage cost by using Parquet/ORC on S3.

Minimal operational overhead by avoiding complex pipelines or constantly updated materialized views.

The Choice state type in AWS Step Functions is designed to perform branching logic, i.e., routing execution to different paths based on conditions in the input data.

“The Step Functions Choice state lets you branch the execution flow depending on values in the state’s input. This allows you to run different processing logic based on dynamic conditions like values in the input JSON.”

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

This makes Choice the correct answer for content-driven conditional workflows.