DP-203 Develop data processing Practice Question
You are running a data transformation pipeline in Azure Synapse Spark that writes output to Delta tables. You notice that the job eventually slows down and then fails with an out-of-memory error. The input data size is 1 TB, and the cluster has 10 nodes with 16 GB memory each. What is the most likely cause?
⚠ Common exam trap
Many candidates confuse out-of-memory errors with driver-side collection (Option A) or shuffle-related issues (Option D), but the specific context of writing to Delta tables points to executor memory exhaustion from oversized partitions, not driver memory or shuffle overhead.
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 data is not partitioned properly, leading to large partitions that exceed executor memory
The most likely cause is that the data is not partitioned properly, leading to large partitions that exceed executor memory. In Azure Synapse Spark, each executor has a limited memory (16 GB per node in this cluster), and if a single partition is too large to fit in memory, the task processing that partition will fail with an out-of-memory error. Proper partitioning ensures that data is evenly distributed across executors, preventing any single partition from overwhelming available memory.
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 driver node does not have enough memory to collect the results
Why it's wrong here
Driver OOM is unlikely when writing to Delta tables; the executors do the work.
- ✓
The data is not partitioned properly, leading to large partitions that exceed executor memory
Why this is correct
Unpartitioned data can result in a few large partitions that cause OOM. Increasing parallelism or repartitioning can help.
- ✗
The Delta table is being written in non-optimized format causing memory pressure
Why it's wrong here
Delta format is optimized; the issue is not the format but the partition size.
- ✗
The transformation involves a wide dependency causing excessive shuffle
Why it's wrong here
While shuffle can cause OOM, it is often manageable with tuning; the primary issue is likely partition size.
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