DEA-C01 Data Operations and Support Practice Question
Exhibit
2024-05-10T12:00:00Z ERROR 1234567890 Job failed: java.lang.OutOfMemoryError: Java heap space 2024-05-10T12:01:00Z INFO 1234567890 Job terminated with exit code 1
Refer to the exhibit. This log snippet is from a failed AWS Glue job. The job processes a large dataset in memory. What is the MOST likely cause of the OutOfMemoryError?
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
✓
The Glue job is running with insufficient DPUs or worker type.
An OutOfMemoryError in AWS Glue typically occurs when the allocated DPUs or worker type are insufficient for the in-memory processing of a large dataset. Option B is incorrect because unsupported file formats cause parsing errors, not memory errors. Option C is incorrect because mismatched keys in a join cause data skew or incorrect results, but not directly an OutOfMemoryError. Option D is incorrect because too many partitions usually lead to small file overhead, not heap space exhaustion.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The Glue job is running with insufficient DPUs or worker type.
Why this is correct
Glue allocates memory per executor based on DPU count and worker type; a large in-memory dataset exceeding that allocation triggers OutOfMemoryError. Insufficient DPUs or an undersized worker type directly caps heap available to the job, so scaling them addresses the constraint stated in the stem.
- ✗
The input data is in an unsupported file format.
Why it's wrong here
An unsupported file format fails at read time with a parsing or SerDe error, before any in-memory processing begins. Format selection matters when Glue cannot infer a schema or classifier, but it cannot cause an OutOfMemoryError during a job that has already loaded and is processing data.
- ✗
The job is attempting to join two tables with mismatched keys.
Why it's wrong here
Mismatched join keys produce incorrect or empty results, not memory exhaustion; the join still processes rows within available heap. Key alignment is the fix when joins return wrong matches or Cartesian explosions from duplicate keys, not when the log shows heap space exhausted during an in-memory operation.
- ✗
The job has too many partitions.
Why it's wrong here
Too many partitions creates many small tasks, each with modest memory, so it causes overhead and slow shuffles rather than a driver or executor heap exhaustion. Repartitioning is the remedy when partitions are skewed or too few, forcing one task to hold an oversized dataset in memory.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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