This DEA-C01 practice question tests your understanding of data ingestion and transformation. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 from Amazon S3 to Amazon Redshift. The job reads JSON files, applies schema mapping, and writes to a Redshift table. Recently, the job started failing with memory errors. The data volume has increased tenfold. Which approach should a data engineer take to resolve this issue with minimal code changes?
Switch from Spark to Python Shell job type.
Why wrong: Python Shell has limited memory.
Implement batch processing with smaller file sizes.
Why wrong: Requires code changes and may not be sufficient.
Increase the number of DPUs allocated to the Glue job.
Provides more resources for processing.
Use Redshift Spectrum to query data directly from S3.
Why wrong: Does not help Glue job memory.
Answer choices
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 DPUs allocated to the Glue job.
Option C is correct because increasing the number of DPUs (Data Processing Units) allocated to the AWS Glue job directly addresses the memory constraint caused by a tenfold increase in data volume. Glue ETL jobs run on Apache Spark, which distributes data processing across executors; more DPUs provide more memory and compute capacity, allowing the job to handle larger datasets without code changes.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Switch from Spark to Python Shell job type.
Why it's wrong here
Python Shell has limited memory.
Implement batch processing with smaller file sizes.
Why it's wrong here
Requires code changes and may not be sufficient.
Increase the number of DPUs allocated to the Glue job.
Why this is correct
Provides more resources for processing.
Related concept
Read the scenario before looking for a memorised answer.
Use Redshift Spectrum to query data directly from S3.
Why it's wrong here
Does not help Glue job memory.
Common exam traps
The trap here is that candidates may assume memory errors always require code optimization (e.g., batching or partitioning), but the question explicitly asks for minimal code changes, making resource scaling the correct answer.
Detailed technical explanation
AWS Glue ETL jobs allocate DPUs in increments of 1 DPU (4 vCPU, 16 GB memory) for Spark jobs, with a default of 10 DPUs. When data volume increases, the Spark executors may run out of memory during shuffle or aggregation phases, causing OutOfMemory errors. Increasing DPUs scales the cluster horizontally, distributing partitions across more executors and reducing memory pressure per node. In practice, a tenfold increase might require doubling or tripling the DPU count, but the exact number depends on data skew and transformation complexity.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..
The correct answer is: Increase the number of DPUs allocated to the Glue job. — Option C is correct because increasing the number of DPUs (Data Processing Units) allocated to the AWS Glue job directly addresses the memory constraint caused by a tenfold increase in data volume. Glue ETL jobs run on Apache Spark, which distributes data processing across executors; more DPUs provide more memory and compute capacity, allowing the job to handle larger datasets without code changes.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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.
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