DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is designing an ingestion pipeline that uses AWS Glue to read from an Amazon RDS for PostgreSQL database. The job must read only rows changed since the previous run and must not scan the entire table each night. The source table has a last_updated timestamp column that is updated on every write. (Choose two.)
⚠ Common exam trap
The trap here is assuming that enabling job bookmarks alone is enough, when a pushdown predicate is also required to restrict the SQL read on the source.
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
✓
Configure the Glue JDBC connection with a pushdown predicate that filters on last_updated greater than a stored high-water mark.
Incremental JDBC ingestion in Glue relies on a pushdown predicate that filters on a high-water mark column like last_updated, combined with job bookmarks that persist state between runs. The predicate pushes work to RDS and reduces transfer, while bookmarks ensure the job resumes from the previous position without reprocessing. Transformations and worker-tuning options do not affect which rows are read.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Configure the Glue JDBC connection with a pushdown predicate that filters on last_updated greater than a stored high-water mark.
Why this is correct
A pushdown predicate is sent to the source database as part of the SQL query, so RDS filters rows before returning them to Glue. Filtering on last_updated greater than a persisted high-water mark reads only changed rows, minimizes network transfer, and satisfies the incremental requirement without full-table scans.
- ✗
Set the Glue job's '--enable-auto-scaling' argument so the job adds workers as the source table grows.
Why it's wrong here
Auto scaling adjusts the number of workers based on workload, improving throughput and cost efficiency for large jobs. It does not change which rows are read, so the job would still scan the entire table. Auto scaling addresses compute sizing, not incremental read logic.
- ✗
Create the DynamicFrame with the 'additional_options' parameter set to {'hashfield': 'id'} to enable hash-based change detection.
Why it's wrong here
The hashfield option is used with the Glue S3 connector and the 'hashpartitions' setting to distribute reads across workers when listing many objects. It is not a change-data-capture mechanism and does not apply to JDBC sources, so it would not restrict the read to changed rows.
- ✓
Enable job bookmarks on the Glue job so it tracks the last processed state of the JDBC source between runs.
Why this is correct
Job bookmarks persist state about which data has already been processed for supported sources, including JDBC. When enabled, Glue can skip rows already handled in prior runs based on the bookmark's stored position, complementing the predicate and preventing duplicate ingestion across nightly executions.
- ✗
Use the Glue ResolveChoice transformation with the 'cast:double' option on the timestamp column.
Why it's wrong here
ResolveChoice resolves data-type conflicts in the DynamicFrame and can cast columns to a chosen type. It does not filter rows or track incremental state, so applying it would not reduce the volume of data read from RDS and would not prevent full-table scans on subsequent runs.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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