The answer is that the data is not partitioned properly, leading to large partitions that exceed executor memory. In Azure Synapse Spark, each executor has a fixed memory allocation—here, 16 GB per node—and when a single partition grows too large to fit within that limit, the task processing it fails with an out-of-memory error. With 1 TB of input data spread across only 10 nodes, improper partitioning can create skewed partitions that overwhelm individual executors, while proper partitioning distributes data evenly to prevent such bottlenecks. On the DP-203 exam, this scenario tests your understanding of Spark memory management and the critical role of partitioning in avoiding OOM failures; a common trap is to blame cluster size or code inefficiency instead of partition imbalance. Remember the memory tip: “Partition to fit, not to split”—ensure each partition’s data size is well under the executor memory limit to keep jobs stable.
DP-203 Develop data processing Practice Question
This DP-203 practice question tests your understanding of develop data processing. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
Under the hood, Spark splits data into partitions, and each partition is processed by a single executor task. If a partition exceeds the executor's memory (including overhead for shuffle, serialization, and output buffers), the task will spill to disk or fail with OOM. In Synapse Spark, the default shuffle partitions (200) may not be sufficient for 1 TB of data, leading to partitions of ~5 GB each, which can exceed the 16 GB executor memory when accounting for overhead and concurrent tasks. Real-world scenarios often require tuning `spark.sql.shuffle.partitions` or using `repartition()` to ensure partitions are roughly 128–256 MB.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-203 question in full detail.
Develop data processing — This question tests Develop data processing — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this DP-203 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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