The correct answer is to increase the executor memory setting in the Spark configuration. This directly resolves the out-of-memory error because executors run out of heap space when processing large transformations from Delta Lake tables, and allocating more memory prevents tasks from spilling to disk or failing outright. On the DP-203 exam, this scenario tests your understanding of Spark resource tuning in Azure Synapse Analytics, often appearing as a first-step troubleshooting question where candidates mistakenly jump to repartitioning or caching. A common trap is assuming the issue is data skew, but the immediate fix for a generic OOM error is boosting executor memory via spark.executor.memory. Remember the mnemonic "OOM = Open Up Memory" to prioritize this adjustment before optimizing parallelism.
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 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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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.
Option C is correct because 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.
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.
✗
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.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use the cache() action on intermediate DataFrames.
Why it's wrong here
Caching can help with iterative algorithms but increases memory usage.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
In Apache Spark, executor memory is controlled by the spark.executor.memory configuration, which defines the JVM heap size per executor. When data exceeds this limit, Spark attempts to spill to disk, but if spilling is insufficient or disabled, an OutOfMemoryError occurs. Increasing executor memory is often the first step, but in production, you should also consider tuning spark.memory.offHeap.enabled or adjusting spark.sql.adaptive.coalescePartitions.enabled for more efficient memory usage.
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: Increase the executor memory setting in the Spark configuration. — Option C is correct because 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.
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: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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