DP-203 Develop data processing Practice Question
You are running a Spark job in Azure Synapse Analytics that reads from a Delta Lake table and performs multiple transformations. The job fails with an out-of-memory error on the executors. Which action should you take first to resolve the issue?
⚠ Common exam trap
A common mix-up: candidates confuse memory issues with partitioning or caching optimizations, but the immediate fix for an out-of-memory error is to increase executor memory, not to reduce parallelism or persist data.
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 setting in the Spark configuration.
An out-of-memory error on executors indicates that the available memory per executor is insufficient for the data being processed. Increasing the executor memory setting in the Spark configuration directly addresses this by allocating more heap space, allowing transformations to complete without spilling to disk or failing. This is the first and most straightforward action to take before optimizing partitioning or caching.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable checkpointing to truncate the lineage.
Why it's wrong here
Checkpointing helps with lineage but does not free memory directly.
- ✗
Decrease the number of partitions to reduce overhead.
Why it's wrong here
Decreasing partitions may cause data skew and memory issues on fewer executors.
- ✓
Increase the executor memory setting in the Spark configuration.
Why this is correct
Increasing executor memory provides more heap space to avoid OOM errors.
- ✗
Use the cache() action on intermediate DataFrames.
Why it's wrong here
Caching can help with iterative algorithms but increases memory usage.
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