DEA-C01 Data Ingestion and Transformation Practice Question

A data engineer needs to load data from an Amazon DynamoDB table into an Amazon S3 bucket for analytics. The table is approximately 500 GB and has a high volume of write traffic. The engineer must minimize the impact on the table's read capacity and avoid consuming provisioned throughput. What is the MOST appropriate method to export the data?

⚠ Common exam trap

The trap here is assuming that any export method must scan the table and consume read capacity, overlooking the zero-RCU native export feature.

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

✓

Use the DynamoDB export to Amazon S3 feature to export the table to an S3 bucket.

The DynamoDB export to Amazon S3 feature creates a point-in-time export without consuming any read capacity, which is critical for a high-traffic 500 GB table. Other methods such as Glue or DMS perform scans that consume RCUs, and Streams only provides incremental changes. Therefore, the native export is the only option that fully satisfies the requirement.

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 the DynamoDB export to Amazon S3 feature to export the table to an S3 bucket.
Why this is correct
DynamoDB's native export to Amazon S3 uses a consistent point-in-time snapshot and does not consume any read capacity units on the table. It is designed for large tables and high-traffic workloads, making it the ideal choice to avoid impacting provisioned throughput while exporting 500 GB.
✗
Use AWS Database Migration Service (AWS DMS) with a DynamoDB source and an S3 target.
Why it's wrong here
AWS DMS can read from DynamoDB, but it performs table scans that consume RCUs unless you use a specific configuration. Even then, DMS is more suited for ongoing replication and may not avoid throughput consumption entirely, failing the requirement to minimize read capacity impact.
✗
Use AWS Glue to read the DynamoDB table with the dynamodb connector and write to Amazon S3.
Why it's wrong here
AWS Glue's DynamoDB connector performs scan or parallel scan operations against the table, which consumes read capacity units (RCUs). With a high-write-traffic table, this can throttle writes and degrade application performance. It does not meet the requirement to avoid consuming provisioned throughput.
✗
Enable DynamoDB Streams and use an AWS Lambda function to write changed records to Amazon S3.
Why it's wrong here
DynamoDB Streams captures item-level changes in near real time, but it only provides incremental changes, not a full 500 GB baseline export. Using Lambda to process the stream for a full export would require a full table scan to seed the data, which again consumes RCUs.

Quick reference

AWS S3 Storage Class Comparison

Storage Class	Min Duration	Retrieval	Use Case
S3 Standard	None	Immediate	Frequently accessed data
S3 Standard-IA	30 days	Immediate	Infrequent access, rapid retrieval
S3 One Zone-IA	30 days	Immediate	Non-critical infrequent data
S3 Intelligent-Tiering	None	Immediate–hours	Unknown or changing access patterns
S3 Glacier Instant	90 days	Milliseconds	Archive with instant retrieval
S3 Glacier Flexible	90 days	Minutes–hours	Archive, flexible retrieval
S3 Glacier Deep Archive	180 days	Hours	Long-term compliance archive

Go deeper

DEA-C01 Data Ingestion and Transformation Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

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