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Data Ingestion and TransformationhardMultiple ChoiceObjective-mapped

DEA-C01 Data Ingestion and Transformation Practice Question

A data engineer runs a weekly AWS Glue ETL job that processes data from Amazon DynamoDB to Amazon S3. The job reads the entire table every time, which is slow and expensive. The job needs to process only items that changed since the last run. Which solution should the engineer implement?

⚠ Common exam trap

Many candidates think a GSI on a timestamp (Option C) is sufficient for incremental processing, but it fails to capture updates to existing items that do not change the timestamp, and it still requires a full scan of the index to find new records.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Enable DynamoDB Streams and process change events with AWS Lambda to write to S3

DynamoDB Streams captures item-level changes (inserts, updates, deletes) in near real-time. An AWS Lambda function can process these change events and write only the incremental data to Amazon S3, eliminating the need to scan the entire DynamoDB table. This approach is both cost-effective and efficient for incremental data ingestion.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Use DynamoDB Scan with a LastEvaluatedKey to paginate and store the last scanned key to resume next time

    Why it's wrong here

    Still scans all items; doesn't capture only changes.

  • Enable DynamoDB Streams and process change events with AWS Lambda to write to S3

    Why this is correct

    Streams capture item-level changes, enabling incremental loads.

  • Add a Global Secondary Index (GSI) on a timestamp attribute and query only new records

    Why it's wrong here

    Requires application to update timestamp and still may scan if timestamp is not monotonically increasing.

  • Use AWS Database Migration Service (DMS) with ongoing replication from DynamoDB to S3

    Why it's wrong here

    AWS DMS ongoing replication captures continuous CDC events from DynamoDB streams, but the requirement is for a weekly batch job that processes only changed items since the last run, not a continuous replication pipeline. DMS would introduce persistent infrastructure costs and schema mapping overhead for a scheduled, incremental read pattern. It is tempting because DMS is designed for continuous data synchronisation between databases and S3, which would be correct if the goal were real-time replication rather than a periodic batch job.

Quick reference

AWS S3 Storage Class Comparison

Storage ClassMin DurationRetrievalUse Case
S3 StandardNoneImmediateFrequently accessed data
S3 Standard-IA30 daysImmediateInfrequent access, rapid retrieval
S3 One Zone-IA30 daysImmediateNon-critical infrequent data
S3 Intelligent-TieringNoneImmediate–hoursUnknown or changing access patterns
S3 Glacier Instant90 daysMillisecondsArchive with instant retrieval
S3 Glacier Flexible90 daysMinutes–hoursArchive, flexible retrieval
S3 Glacier Deep Archive180 daysHoursLong-term compliance archive

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.