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 truncates the RDD lineage, which helps recovery and iterative algorithms, but it does not reduce the memory held by cached partitions or shuffle data during transformations. It is tempting because lineage truncation is a standard Spark tuning technique, and would be correct for long lineage chains causing driver-side stack or metadata pressure.
- ✗
Decrease the number of partitions to reduce overhead.
Why it's wrong here
Fewer partitions concentrate more data into each task, increasing per-executor memory pressure and worsening the out-of-memory failure. It is tempting because reducing partition count lowers task scheduling overhead, and would be correct when small partitions cause excessive overhead rather than memory exhaustion.
- ✓
Increase the executor memory setting in the Spark configuration.
Why this is correct
Executor out-of-memory errors arise when each executor's JVM heap cannot hold the partition data during transformations. Raising spark.executor.memory gives those executors more heap, directly relieving the constraint. Partition tuning or skew handling may follow, but increasing memory is the quickest first action.
- ✗
Use the cache() action on intermediate DataFrames.
Why it's wrong here
Caching intermediate DataFrames consumes executor memory to store partitions, directly aggravating the out-of-memory condition. It is tempting because caching avoids recomputation across repeated transformations, and would be correct when the same DataFrame is reused many times and memory headroom exists.
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