Amazon Web Services Data-Engineer-Associate Exam With Confidence Using Practice Dumps

Problem Analysis:

The company uses DynamoDB for gaming data storage and needs to ingest data into Amazon OpenSearch Service in near real time.

Data updates must propagate quickly to OpenSearch for analytics or search purposes.

Key Considerations:

DynamoDB Streams provide near-real-time capture of table changes (inserts, updates, and deletes).

Integration with AWS Lambda allows seamless processing of these changes.

OpenSearch offers APIs for indexing and updating documents, which Lambda can invoke.

Solution Analysis:

Option A: Step Functions with Periodic Export

Not suitable for near-real-time updates; introduces significant latency.

Operationally complex to manage periodic exports and S3 data ingestion.

Option B: AWS Glue Job

AWS Glue is designed for ETL workloads but lacks real-time processing capabilities.

Option C: DynamoDB Streams + Lambda

DynamoDB Streams capture changes in near real time.

Lambda can process these streams and use the OpenSearch API to update the index.

This approach provides low latency and seamless integration with minimal operational overhead.

Option D: Custom OpenSearch Plugin

Writing a custom plugin adds complexity and is unnecessary with existing AWS integrations.

Implementation Steps:

Enable DynamoDB Streams for the relevant DynamoDB tables.

Create a Lambda function to process stream records:

Parse insert, update, and delete events.

Use OpenSearch APIs to index or update documents based on the event type.

Set up a trigger to invoke the Lambda function whenever there are changes in the DynamoDB Stream.

Monitor and log errors for debugging and operational health.

Amazon DynamoDB Streams Documentation

AWS Lambda and DynamoDB Integration

Amazon OpenSearch Service APIs

Option A is correct. The MERGE INTO statement is used to conditionally update, insert, or delete rows based on a match condition between a target table and a source table. In this statement, the target table is accounts and the source table is monthly_accounts_update. The join condition is t.customer = s.customer. Because the statement uses WHEN MATCHED THEN DELETE, every row in accounts that has a matching customer value in monthly_accounts_update will be deleted from the target table.

AWS documentation for MERGE INTO states that it conditionally updates, deletes, or inserts rows into an Apache Iceberg table, and the syntax explicitly includes WHEN MATCHED THEN DELETE. AWS Glue guidance and examples for Iceberg also show MERGE INTO as a supported pattern for row-level changes in Glue ETL workflows. This means the syntax is valid for supported Glue-Iceberg use cases, so option D is incorrect. Options B and C are also incorrect because the statement does not retain only matching rows and does not delete the entire table. It deletes only the rows in the target table that satisfy the match condition.

AWS Glue Data Quality transforms allow you to define built-in rules like IsComplete for null validation and ReferentialIntegrity for relationship validation—all with minimal code and operational overhead.

“Use AWS Glue Data Quality rules such as IsComplete and ReferentialIntegrity within ETL jobs to automatically validate incoming data.”