DEA-C01 Data Ingestion and Transformation Practice Question

A data engineer runs an AWS Glue job that reads Parquet files from Amazon S3 partitioned by year/month/day and writes to another S3 prefix. The job currently processes all historical partitions on every run, causing long runtimes and high cost. The engineer wants subsequent runs to process only new data. Which configuration should the engineer apply?

⚠ Common exam trap

It's easy for candidates to confuse concurrency or compute scaling controls with incremental processing, when only job bookmarks track previously processed partitions.

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 AWS Glue job bookmarks and ensure the job's source and target paths are stable across runs.

Job bookmarks maintain persistent state across runs, allowing Glue to skip files and partitions that were already processed in prior executions. This is the native mechanism for incremental processing in Glue ETL, directly reducing scanned data, runtime, and cost without changing the transformation logic or adding external state tracking.

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 an S3 Lifecycle rule to transition old partitions to S3 Glacier Instant Retrieval so the Glue job reads them faster.
Why it's wrong here
Lifecycle transitions change storage class, not which objects a Glue job selects. The job would still list and attempt to read all partitions, and Glacier Instant Retrieval does not reduce listing or scan scope. This does not achieve incremental processing and may add retrieval considerations.
✗
Increase the number of AWS Glue DPUs allocated to the job so it can process all partitions faster.
Why it's wrong here
Adding DPUs increases parallel capacity but still reads every historical partition on each run, so total data scanned and cost remain high. It addresses speed, not the redundant reprocessing of old data, and can actually raise cost because more workers run for the same oversized input.
✓
Enable AWS Glue job bookmarks and ensure the job's source and target paths are stable across runs.
Why this is correct
Job bookmarks persist state about which partitions and files have already been processed, so reruns skip previously handled data. This directly reduces runtime and cost by limiting each run to new partitions. Bookmarks require stable source and target paths and a consistent transformation script to remain effective.
✗
Set the Glue job's MaxConcurrentRuns parameter to 1 to prevent overlapping executions from reprocessing data.
Why it's wrong here
MaxConcurrentRuns controls how many instances of the job can run simultaneously; it does not track which partitions were already processed. Even with a single concurrent run, each execution still scans the entire dataset, so runtime and cost are unchanged and incremental behavior is not achieved.

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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