The correct answer is to enable Snappy compression for intermediate data because this directly reduces the volume of data written to disk during Spark shuffle operations, which is the primary cause of disk full issues on EMR core nodes when processing large Parquet files. Snappy offers an optimal balance between compression ratio and speed, minimizing I/O overhead while conserving storage space on the core node. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of Spark tuning within Amazon EMR, specifically how intermediate data compression prevents disk pressure without sacrificing job performance. A common trap is to assume resizing the cluster or increasing instance storage is the fix, but those are reactive and costly; compression is a proactive, cost-effective tuning practice. Remember the mnemonic “Snappy Saves Space” to recall that enabling Snappy compression for shuffle data is the first-line defense against disk full errors on core nodes.
DEA-C01 Data Operations and Support Practice Question
This DEA-C01 practice question tests your understanding of data operations and support. 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 data engineer is monitoring an Amazon EMR cluster and notices that one core node is running out of disk space. The cluster is running a Spark job that processes large Parquet files. What should the engineer do to prevent the issue?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable Snappy compression for intermediate data
Option C is correct because enabling Snappy compression for intermediate data reduces the volume of data written to disk during Spark shuffle operations, directly addressing the disk space issue on the core node. Snappy provides a good balance between compression ratio and speed, minimizing I/O overhead while conserving storage. This is a standard tuning practice in Amazon EMR for Spark jobs that process large Parquet files.
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.
✗
Terminate the core node and replace it with a larger instance type
Why it's wrong here
Terminating a core node may cause data loss.
✗
Use Spark's in-memory processing to avoid writing intermediate data to disk
Why it's wrong here
In-memory processing may still spill to disk.
✓
Enable Snappy compression for intermediate data
Why this is correct
Compression reduces disk usage for intermediate data.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of core nodes
Why it's wrong here
More nodes distribute load but do not free space on an existing node.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse increasing cluster capacity (options A or D) with optimizing data handling, whereas the exam tests the understanding that compression of intermediate data directly reduces disk usage without requiring hardware changes.
Detailed technical explanation
How to think about this question
Spark's shuffle operations (e.g., groupByKey, reduceByKey, join) write intermediate data to local disk as shuffle files, which are then fetched by reducers. Enabling Snappy compression via spark.shuffle.compress=true (default is false in some configurations) reduces the size of these shuffle files, typically by 2-3x, without significant CPU overhead. In Amazon EMR, this setting can be applied in the Spark configuration or via the EMR console, and it is especially effective when processing large Parquet files because Parquet already uses columnar storage and compression, but shuffle data remains uncompressed unless explicitly configured.
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 Operations and Support — This question tests Data Operations and Support — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable Snappy compression for intermediate data — Option C is correct because enabling Snappy compression for intermediate data reduces the volume of data written to disk during Spark shuffle operations, directly addressing the disk space issue on the core node. Snappy provides a good balance between compression ratio and speed, minimizing I/O overhead while conserving storage. This is a standard tuning practice in Amazon EMR for Spark jobs that process large Parquet files.
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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Question Discussion
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