The answer is to increase the number of Glue worker nodes (DPUs) for the job. This is correct because scaling AWS Glue ETL jobs with DPUs directly boosts the distributed processing power, allowing the job to partition and process larger volumes of Parquet data in parallel across multiple workers, which reduces execution time as data volume grows. On the AWS Certified Data Engineer Associate DEA-C01 exam, this concept tests your understanding of horizontal scaling in serverless ETL—a common scenario where candidates mistakenly try to optimize code or change file formats first, when simply adding DPUs is the most immediate fix for increasing data loads. A key trap is assuming more memory or a different engine type is needed, but for Parquet-to-Redshift aggregations, more workers is the primary lever. Memory tip: think "DPU = Distributed Parallel Units"—when data grows, add units, not complexity.
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.
An e-commerce company uses AWS Glue to run ETL jobs that transform clickstream data from Amazon S3. The job reads Parquet files, performs aggregations, and writes the results to Amazon Redshift. The job runs successfully but takes longer than expected. The data volume is increasing. Which design change would MOST improve the job's performance?
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 Glue worker nodes (DPUs) for the job.
Increasing the number of Glue worker nodes (DPUs) directly scales the distributed processing capacity of the ETL job, allowing it to process larger volumes of Parquet data in parallel. This is the most straightforward way to reduce execution time when data volume is growing, as AWS Glue automatically partitions the workload across the additional workers.
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.
✗
Write the aggregated results to a single large file instead of multiple partitions.
Why it's wrong here
Single file reduces parallelism and increases shuffle overhead.
✗
Convert the Parquet files to CSV to simplify the schema.
Why it's wrong here
CSV is less efficient than Parquet for columnar storage and compression.
✗
Replace the Redshift target with Amazon Redshift Spectrum.
Why it's wrong here
Spectrum is for querying S3, not for loading transformed data into Redshift.
✓
Increase the number of Glue worker nodes (DPUs) for the job.
Why this is correct
More workers parallelize tasks and reduce runtime.
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 assume increasing DPUs always increases cost without considering that the job's runtime reduction often lowers total cost, and they mistakenly choose a data format or target change that does not address the core parallelism issue.
Detailed technical explanation
How to think about this question
AWS Glue uses Apache Spark under the hood, where each worker (DPU) provides a fixed amount of memory and vCPUs. Increasing DPUs from, say, 10 to 50 can yield near-linear speedup for embarrassingly parallel tasks like reading partitioned Parquet files, but only up to the point where shuffle or skew becomes the bottleneck. In practice, monitoring the job's 'DPU utilization' metric in CloudWatch helps identify whether the job is CPU-bound or I/O-bound before scaling.
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.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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 Glue worker nodes (DPUs) for the job. — Increasing the number of Glue worker nodes (DPUs) directly scales the distributed processing capacity of the ETL job, allowing it to process larger volumes of Parquet data in parallel. This is the most straightforward way to reduce execution time when data volume is growing, as AWS Glue automatically partitions the workload across the additional workers.
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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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.
Question Discussion
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