DEA-C01 Data Ingestion and Transformation Practice Question
A company uses AWS Glue ETL jobs to process data from an S3 data lake. The job reads data in CSV format, transforms it, and writes to Parquet. The job runs daily and takes 2 hours to complete. The data volume is increasing by 20% each month. The engineer wants to reduce the job runtime. Which action is most effective?
⚠ Common exam trap
A common mix-up: candidates assume increasing DPUs or enabling compression is the universal fix, but they fail to recognize that reducing the data scanned via partition pruning is the most impactful optimization for growing datasets in S3-based Glue jobs.
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
✓
Partition the input data in S3 by date and use partition pruning in the job
Most effective because partitioning the input data by date and using partition pruning allows the Glue ETL job to read only the relevant partitions instead of scanning the entire S3 data lake. This drastically reduces the amount of data processed, which directly addresses the growing data volume and shortens job runtime. Partition pruning is a core optimization for Spark-based Glue jobs, as it leverages Hive-style partitioning to skip unnecessary files.
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 workers horizontally, but a CSV-to-Parquet job bottlenecked on row-oriented parsing and shuffle gains little from extra executors. It is tempting because DPU count is the obvious tuning knob, and it would be correct when the job is genuinely compute-bound with parallelisable partitions.
- ✗
Enable compression on the input CSV files
Why it's wrong here
Compressing input CSV reduces bytes read but leaves the row-oriented parsing and shuffle cost intact, so the dominant runtime driver remains. It is tempting because compression is cheap and effective for storage-bound scans, and it would be correct when network transfer or S3 read volume is the bottleneck.
- ✗
Switch from Python Shell to Spark ETL
Why it's wrong here
Python Shell jobs operate on a single instance without distributed processing, making them incapable of handling the increasing data volume through parallelisation. This option is tempting because Python Shell is ideal for lightweight tasks like triggering API calls or managing small metadata files where Spark's overhead is unnecessary. However, it fails here because the job requires horizontal scaling across multiple workers to reduce execution time as datasets grow.
- ✓
Partition the input data in S3 by date and use partition pruning in the job
Why this is correct
Partitioning S3 input by date lets Glue read only the relevant partitions, cutting the bytes scanned each run. This addresses the growing data volume constraint far more effectively than scaling workers or changing formats alone.
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.