The correct answer involves filtering and projecting data early, increasing the number of DPUs, and optimizing join keys or using skew join handling. Filtering and projecting early, using pushdown predicates and selecting only necessary columns, directly reduces the volume of data shuffled during complex joins and aggregations, which is the primary cause of memory pressure in AWS Glue ETL. On the MLS-C01 exam, this tests your understanding of Spark’s memory management under the hood—specifically how data shuffling and worker allocation impact job stability. A common trap is assuming more DPUs always solve memory issues, but without early filtering, you simply shuffle more data across more nodes, worsening the bottleneck. Remember the mnemonic “Filter First, Then Scale” to anchor the sequence: reduce data volume before adding compute resources.
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. 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 is using AWS Glue ETL jobs to transform data. The jobs are failing due to insufficient memory. The data processing involves complex joins and aggregations. Which THREE actions can improve job performance and reduce memory usage?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Filter and project data early in the transformation to reduce data volume
Option A is correct because filtering and projecting data early in the transformation reduces the volume of data that must be processed in subsequent operations like joins and aggregations. By using pushdown predicates and selecting only necessary columns, you minimize the data shuffled across the cluster, which directly reduces memory pressure and improves job performance in AWS Glue ETL.
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.
✓
Filter and project data early in the transformation to reduce data volume
Why this is correct
Reduces memory footprint.
Related concept
Read the scenario before looking for a memorised answer.
✗
Decrease the number of DPUs allocated to the job
Why it's wrong here
Fewer DPUs mean less memory.
✓
Repartition the data and use bucketing to reduce shuffle size
Why this is correct
Reduces data movement across nodes.
Related concept
Read the scenario before looking for a memorised answer.
✓
Increase the number of DPUs (workers) allocated to the job
Why this is correct
More DPUs provide more memory and parallelism.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a single node cluster to avoid shuffle overhead
Why it's wrong here
Single node may not have enough memory.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume reducing resources (Option B) or eliminating parallelism (Option E) will solve memory issues, when in fact these actions exacerbate the problem by increasing the data load per executor or removing the benefits of distributed processing.
Detailed technical explanation
How to think about this question
Under the hood, AWS Glue ETL jobs run on Apache Spark, where memory management is critical for operations like joins and aggregations. Filtering early leverages Spark's predicate pushdown to reduce the data read from sources, and projecting only needed columns reduces the in-memory footprint of DataFrames. Repartitioning and bucketing (Option C) help control the number of partitions and reduce shuffle size by co-locating data with the same keys, which minimizes data movement across the network and reduces memory usage during wide transformations.
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 Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Filter and project data early in the transformation to reduce data volume — Option A is correct because filtering and projecting data early in the transformation reduces the volume of data that must be processed in subsequent operations like joins and aggregations. By using pushdown predicates and selecting only necessary columns, you minimize the data shuffled across the cluster, which directly reduces memory pressure and improves job performance in AWS Glue ETL.
What should I do if I get this MLS-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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Question Discussion
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