DEA-C01 Data Operations and Support Practice Question
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?
⚠ Common exam trap
Many exam-takers 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.
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
✓
Enable Snappy compression for intermediate data
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.
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.
- ✗
Increase the number of core nodes
Why it's wrong here
More nodes distribute load but do not free space on an existing node.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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