DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is building an AWS Glue ETL job that reads data from Amazon S3 and must write the output partitioned by year, month, and day for efficient downstream querying in Amazon Athena. The engineer wants the job to create the partition folders and register them in the Glue Data Catalog automatically. (Choose two.)
⚠ Common exam trap
The trap here is assuming a post-run crawler is the only way to register partitions, when the Glue write itself can update the catalog if configured to do so.
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
✓
Set the DynamicFrame write option partitionKeys to the year, month, and day columns.
Producing partitioned output and registering it in the Glue Data Catalog from a Glue job is achieved by specifying partitionKeys on the write and enabling the job to update the Data Catalog. Together these create the year/month/day folder structure and add the corresponding catalog partitions during the run. Manual API calls or a separate crawler can work but are not the automatic in-job mechanism the scenario requires.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Call the Glue catalog's create_partition API for each partition after the job writes files.
Why it's wrong here
Manually calling create_partition works but is not the automatic approach the scenario requests and scales poorly when partitions are numerous. It also duplicates what Glue can do natively during the write. The engineer wants the job itself to handle catalog registration, so this manual step is unnecessary and error-prone at scale.
- ✓
Set the DynamicFrame write option partitionKeys to the year, month, and day columns.
Why this is correct
The partitionKeys write option tells Glue how to split output files into directory hierarchies based on the specified columns. Setting it to year, month, and day produces the year=/month=/day= folder structure that Athena and other engines expect. This is the primary mechanism for generating partitioned output from a Glue job.
- ✓
Enable the job's 'Update the Data Catalog' option so new partitions are added during the write.
Why this is correct
When the write to S3 uses a catalog table and the job is configured to update the Data Catalog, Glue adds the newly written partitions to the catalog during the run. Combined with partitionKeys, this delivers both the folder layout and the catalog entries without a separate crawler or manual calls.
- ✗
Run an AWS Glue crawler on the output prefix after every job run.
Why it's wrong here
A crawler would eventually discover partitions, but it runs as a separate step after the job and adds latency and cost. The scenario asks the job to register partitions automatically, which Glue can do during the write. Relying on a post-run crawler is a valid alternative pattern but does not match the requirement for automatic registration within the job.
- ✗
Store the output as a single Parquet file per run to preserve partition metadata.
Why it's wrong here
Writing one file per run prevents a meaningful partition hierarchy because all rows land in a single object. Partitioning requires multiple output files organized under distinct key paths. This approach would hurt Athena performance by eliminating partition pruning and does not register anything in the catalog, so it contradicts the goal.
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 |
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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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