DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer needs to run a PySpark transformation on a 2 TB dataset stored in Amazon S3 and write the output back to S3 in Parquet. The team wants to use AWS Glue but does not want to manage clusters or tune Spark configuration manually. They also want to pay only for the time the job runs. Which AWS Glue component should the engineer use?
⚠ Common exam trap
The trap here is equating 'AWS Glue' generically with any Glue job type, when Python shell jobs cannot run distributed PySpark and DataBrew targets visual preparation rather than custom code.
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
✓
AWS Glue for Apache Spark job
AWS Glue for Apache Spark provides a serverless, distributed Spark runtime. The engineer submits a PySpark script, Glue handles cluster provisioning and Spark tuning, and billing is based on DPU-hours consumed during the run. This fits the need for custom PySpark on a 2 TB S3 dataset with no cluster management and pay-per-run cost, unlike single-node shell jobs or self-managed EMR clusters.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AWS Glue DataBrew recipe job
Why it's wrong here
DataBrew is a visual data preparation service intended for analysts to clean and normalize data through recipes. It is not designed for large-scale PySpark transformations on multi-terabyte datasets, and it abstracts away Spark code rather than executing custom PySpark. It would not satisfy the requirement to run a custom PySpark transformation over 2 TB of S3 data.
- ✗
Amazon EMR on EC2 cluster with a bootstrap action
Why it's wrong here
EMR on EC2 provides managed Spark, but it requires provisioning and managing a cluster of EC2 instances, including sizing, scaling, and termination policies. That conflicts with the requirement to avoid cluster management. While EMR can run PySpark at scale, it does not match the serverless, pay-only-for-runtime model the team requested.
- ✗
AWS Glue Python shell job
Why it's wrong here
Python shell jobs run single-node Python scripts with limited compute and no distributed Spark engine. They are suited to lightweight tasks such as invoking APIs or small data manipulation, not 2 TB distributed transformations. Using a Python shell job for this workload would run out of memory or take impractically long, so it does not meet the requirement.
- ✓
AWS Glue for Apache Spark job
Why this is correct
AWS Glue for Apache Spark is a serverless Spark environment. You supply a PySpark script, and Glue provisions and manages the Spark cluster, applies tuning defaults, and bills per DPU-hour for the job's runtime. This matches the requirement for custom PySpark on a 2 TB S3 dataset without cluster management and with pay-per-run pricing.
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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