MLS-C01 Exploratory Data Analysis Practice Question
Exhibit
Refer to the exhibit. ``` $ cat /var/log/syslog | grep "OutOfMemory" 2024-01-15 10:30:45 ERROR OutOfMemoryError: Java heap space at org.apache.spark.sql.catalyst.expressions.GenerateMutableProjection.apply(Unknown Source) ```
Refer to the exhibit. A data scientist is running an Amazon EMR Spark job for exploratory data analysis on a large dataset. The job fails with the error shown. What is the most appropriate action to resolve this?
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
✓
Increase the executor memory in Spark configuration.
The error message indicates an OutOfMemoryError in the Spark executors. Increasing executor memory (option C) directly addresses this by providing more heap space for data processing. Option A (fewer nodes) reduces total cluster memory, worsening the problem. Option B (Parquet format) can improve I/O performance but does not resolve insufficient memory allocation. Option D (increase driver memory) only helps the driver process, not the executors.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the number of worker nodes.
Why it's wrong here
Fewer nodes reduce total memory.
- ✗
Convert the input data to Parquet format.
Why it's wrong here
Parquet is efficient but not a direct fix for OOM.
- ✓
Increase the executor memory in Spark configuration.
Why this is correct
More memory per executor prevents heap overflow.
- ✗
Increase the driver memory.
Why it's wrong here
Driver memory is for coordination, not data processing.
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