DEA-C01 Data Ingestion and Transformation Practice Question
A data engineering team is designing a batch processing workflow using AWS Glue. The job reads from an S3 bucket, transforms data, and writes to another S3 bucket. The job runs daily and processes new data incrementally. Which THREE features should they use to optimize performance and cost?
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 autoscaling.
Option B is correct because Glue job autoscaling dynamically adjusts the number of workers (DPUs) based on the workload, which reduces cost during lighter stages and improves throughput during heavier ones for a daily incremental job. Option D is correct because predicate pushdown and column pruning let Glue read only the needed partitions, rows, and columns from Parquet/ORC on S3, cutting I/O, shuffle, and DPU-hours. Option E is correct because Glue job bookmarks persist state so the job processes only new or changed data since the last run, which is exactly the incremental daily pattern described and avoids reprocessing old S3 objects. Option A is not marked correct because converting all input data to Parquet is a broad storage-format change, not a Glue job performance/cost feature, and it may add conversion overhead. Option C is not marked correct because manually increasing DPUs for every run is static over-provisioning that raises cost rather than optimizing it, unlike autoscaling.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Convert all input data to Parquet format before processing.
Why it's wrong here
Parquet conversion is a valid optimisation, but the stem asks for three features; on its own it does not address incremental processing, which relies on Glue job bookmarks to track previously processed S3 objects. Columnar storage would be the right focus when repeatedly scanning wide datasets for analytics.
- ✓
Enable Glue job autoscaling.
Why this is correct
Glue job autoscaling dynamically adds and removes workers based on the stage of the shuffle and partition workload, so the daily incremental job does not over-provision capacity for its peak. This directly cuts DPU-hour cost while meeting the performance requirement for variable-volume runs.
- ✗
Manually increase the number of DPUs for each run.
Why it's wrong here
Fixed DPU counts over-provision every run, so cost stays flat while incremental daily volumes vary. It is tempting because more DPUs do speed individual jobs, and it would be correct where a workload is consistently large and latency-critical rather than variable and cost-sensitive.
- ✓
Use predicate pushdown and column pruning in the script.
Why this is correct
Predicate pushdown filters rows and column pruning selects only needed columns at the S3 and catalog read stage, so Glue reads far less data than a full scan. This satisfies the cost and performance optimisation goal for incremental daily processing without extra infrastructure.
- ✓
Enable job bookmarks to process only new data.
Why this is correct
Job bookmarks persist state from prior runs, so Glue reads only newly arrived S3 objects rather than reprocessing the whole prefix each day. This directly satisfies the incremental-processing requirement and avoids paying to transform already-handled data.
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
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.