- A
Use a Python shell job instead of a Spark job.
Why wrong: Python shell jobs are for light processing and not suitable for large transformations.
- B
Switch from using DynamicFrame to using Spark SQL for transformations.
Why wrong: Spark SQL may have similar performance; the bottleneck is likely resources, not API.
- C
Repartition the input data into more partitions before reading.
Why wrong: Repartitioning adds overhead and does not help when reading a single partition.
- D
Increase the number of workers (DPUs) for the Glue job.
More workers increase parallelism, reducing runtime for the given data size.
Question 1,471 of 1,786
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company uses AWS Glue ETL jobs to transform data in Amazon S3. The data is partitioned by date and hour. The job reads the latest hour's data, performs aggregations, and writes results to a separate S3 bucket. The job runs every hour and processes approximately 500 MB of input data. The team notices that the job takes longer than expected, often exceeding the 1-hour window. Which action would most effectively reduce the job's runtime?
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
✓
Increase the number of workers (DPUs) for the Glue job.
Increasing the number of workers (DPUs) for the Glue job directly addresses the root cause: the job is CPU- or memory-bound due to insufficient parallelism for the 500 MB hourly workload. By allocating more DPUs, AWS Glue can distribute the aggregation and write operations across more executors, reducing wall-clock time and keeping the job within the 1-hour window. This is the most effective action because the job's bottleneck is compute capacity, not data format or processing framework.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a Python shell job instead of a Spark job.
Why it's wrong here
Python shell jobs are for light processing and not suitable for large transformations.
- ✗
Switch from using DynamicFrame to using Spark SQL for transformations.
Why it's wrong here
Spark SQL may have similar performance; the bottleneck is likely resources, not API.
- ✗
Repartition the input data into more partitions before reading.
Why it's wrong here
Repartitioning adds overhead and does not help when reading a single partition.
- ✓
Increase the number of workers (DPUs) for the Glue job.
Why this is correct
More workers increase parallelism, reducing runtime for the given data size.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'repartitioning' (Option C) with 'increasing parallelism' — but without more workers, more partitions simply create scheduling overhead and do not reduce runtime.
Trap categories for this question
Similar concept trap
Spark SQL may have similar performance; the bottleneck is likely resources, not API.
Detailed technical explanation
How to think about this question
AWS Glue ETL jobs run on Apache Spark, where the number of DPUs determines the number of executors (each DPU provides 4 vCPU and 16 GB memory). For a 500 MB hourly aggregation, the default 10 DPUs may be insufficient if the aggregation involves shuffles or skewed keys; increasing to 20 or 30 DPUs can linearly reduce runtime until the job becomes I/O-bound. Under the hood, Glue uses a managed Spark cluster where DPU allocation directly controls the `spark.executor.instances` and `spark.executor.cores` settings, making it the primary lever for scaling compute-bound jobs.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 |
What to study next
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the number of workers (DPUs) for the Glue job. — Increasing the number of workers (DPUs) for the Glue job directly addresses the root cause: the job is CPU- or memory-bound due to insufficient parallelism for the 500 MB hourly workload. By allocating more DPUs, AWS Glue can distribute the aggregation and write operations across more executors, reducing wall-clock time and keeping the job within the 1-hour window. This is the most effective action because the job's bottleneck is compute capacity, not data format or processing framework.
What should I do if I get this DEA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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