DEA-C01 Data Ingestion and Transformation Practice Question

A company uses AWS Glue ETL to transform data from Amazon RDS for MySQL to Amazon S3. The Glue job reads from a JDBC connection. The job runs once daily and processes all records, but the data volume is growing. Which change would improve performance and reduce costs?

⚠ Common exam trap

Test-takers frequently assume increasing parallelism (Option A) is the universal fix for performance, overlooking the fact that reducing the data volume processed (Option D) is a more fundamental and cost-effective optimization.

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 Glue job bookmarking and set the job to process only new data

Enabling Glue job bookmarking allows the job to process only new or changed data since the last run, rather than reprocessing the entire dataset. This reduces both the data volume read from the JDBC source and the transformation time, directly improving performance and lowering costs by minimizing DPU usage.

Answer analysis

Option-by-option breakdown

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

✗
Increase the number of DPUs for the Glue job
Why it's wrong here
Adding DPUs scales compute horizontally, raising cost while the daily full-table JDBC read remains the bottleneck. It would suit CPU-bound transformations, but here the inefficiency is re-reading all records each run, which incremental JDBC bookmarking addresses.
✗
Switch to a Glue Python shell job
Why it's wrong here
A Glue Python shell job lacks the distributed processing engine (Apache Spark) required to handle the growing data volume from the JDBC connection; it runs on a single node and cannot parallelise reads from RDS for MySQL, so it would fail to improve performance and likely increase runtime. This option is tempting because Python shell jobs are ideal for lightweight, non-distributed tasks such as running SQL queries or calling APIs, where minimal overhead and lower cost are priorities.
✗
Use a higher JDBC fetch size
Why it's wrong here
A larger fetch size reduces round trips per query but still reads every record daily, so growing volume keeps costs rising. It would help latency-bound small queries, yet the stem's cost driver is full re-extraction, which job bookmarks eliminate by processing only new rows.
✓
Enable Glue job bookmarking and set the job to process only new data
Why this is correct
Job bookmarks persist state from prior runs, so the JDBC source reads only rows added or changed since the last successful run rather than the full table. This cuts JDBC read volume, shuffle and S3 write costs, satisfying the growing-data-volume constraint while keeping the daily schedule.

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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