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

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.

Option C is correct because the required access pattern is: find all completed rides for a specific driver. In DynamoDB, when you need to query data efficiently by attributes that are not part of the base table primary key, you typically create a global secondary index (GSI). AWS documentation states that a GSI can have a key schema that is different from the base table and can use top-level attributes such as DriverID and RideStatus as its partition and sort keys. That makes it possible to query directly for a given driver and then narrow the results to completed rides, without scanning the full table.

Option A is incorrect because a local secondary index (LSI) must use the same partition key as the base table. Since the table partition key is RideID, an LSI cannot be created with DriverID as the partitioning access path.

Option B uses RiderID, which does not satisfy the requirement to retrieve rides by driver. Option D is also wrong because filter expressions are applied after items are read and therefore do not avoid scanning large amounts of data. The study guide emphasizes choosing the correct data model and access pattern for the workload, which places this question in the Data Storage and Management domain.

Problem Analysis:

The company needs near real-time replication of MySQL updates to Amazon Redshift.

Minimal development effort is required for this solution.

Key Considerations:

AWS DMS provides a full load + CDC (Change Data Capture) mode for continuous replication of database changes.

DMS integrates natively with both MySQL and Redshift, simplifying setup.

Solution Analysis:

Option A: AWS Glue Job

Glue is batch-oriented and does not support near real-time replication.

Option B: DMS with Full Load + CDC

Efficiently handles initial database load and continuous updates.

Requires minimal setup and operational overhead.

Option C: AppFlow SDK

AppFlow is not designed for database replication. Custom connectors increase development effort.

Option D: DataSync

DataSync is for file synchronization and not suitable for database updates.

Final Recommendation:

Use AWS DMS in full load + CDC mode for continuous replication.

AWS Database Migration Service Documentation

Setting Up DMS with Redshift