Quick Answer
The answer is to add `--properties spark.executor.memory=8g` to the submission command. This is correct because the out-of-memory error in Spark on Dataproc directly indicates that the executor processes lack sufficient heap space to hold the data partitions during shuffle or aggregation operations. By increasing `spark.executor.memory`, you allocate more JVM memory per executor, which reduces spill-to-disk and prevents the `java.lang.OutOfMemoryError` that halts the job. On the Google Professional Data Engineer exam, this scenario tests your ability to diagnose resource contention in Dataproc clusters—a common trap is mistakenly increasing driver memory or adding more executors, which does not fix the per-executor memory ceiling. Remember that executor memory governs the storage and execution memory pool within each worker; a quick memory tip is to think "executor memory is the bucket size, not the number of buckets." When you see an OOM error in a Spark job on Dataproc, always first check and raise `spark.executor.memory` before scaling out horizontally.
This PDE practice question tests your understanding of designing data processing systems. 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.
The exhibit shows a Spark job submitted to Dataproc that fails with an out-of-memory error. Which change should be made to the submission command to resolve the issue?
Use a different Spark example class.
Why wrong: Class is not the cause.
Increase the number of worker nodes in the cluster.
Why wrong: Doesn't increase per-executor memory.
Add --properties spark.executor.memory=8g to the command.
Increases executor heap space.
Add --driver-memory 8g to the command.
Why wrong: Driver memory is not the issue.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Add --properties spark.executor.memory=8g to the command.
The out-of-memory error indicates that the Spark executors do not have enough memory to process the data. Adding `--properties spark.executor.memory=8g` increases the memory allocated to each executor, directly addressing the root cause. This property overrides the default executor memory (typically 1g or 4g depending on the cluster configuration) and is the standard way to tune executor memory in Spark on Dataproc.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Use a different Spark example class.
Why it's wrong here
Class is not the cause.
Increase the number of worker nodes in the cluster.
Why it's wrong here
Doesn't increase per-executor memory.
Add --properties spark.executor.memory=8g to the command.
Why this is correct
Increases executor heap space.
Related concept
Read the scenario before looking for a memorised answer.
Add --driver-memory 8g to the command.
Why it's wrong here
Driver memory is not the issue.
Common exam traps
Google Cloud often tests the distinction between driver memory and executor memory, and candidates mistakenly choose `--driver-memory` because they confuse the driver's role with the executors' memory needs, or they assume that increasing cluster size (more nodes) automatically increases per-executor memory.
Detailed technical explanation
Spark executor memory is divided into regions: storage memory (for cached data), execution memory (for shuffle, joins, aggregations), and user memory (for user code). The `spark.executor.memory` property sets the total JVM heap for each executor, and if this is too low, tasks may fail with `java.lang.OutOfMemoryError: Java heap space` or `GC overhead limit exceeded`. In Dataproc, the default executor memory is often set to 4g for standard machine types, but data-intensive jobs may require tuning based on the `spark.memory.fraction` (default 0.6) and `spark.memory.storageFraction` (default 0.5) to avoid spilling to disk or OOM errors.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
What to study next
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FAQ
Designing data processing systems — This question tests Designing data processing systems — Read the scenario before looking for a memorised answer..
The correct answer is: Add --properties spark.executor.memory=8g to the command. — The out-of-memory error indicates that the Spark executors do not have enough memory to process the data. Adding `--properties spark.executor.memory=8g` increases the memory allocated to each executor, directly addressing the root cause. This property overrides the default executor memory (typically 1g or 4g depending on the cluster configuration) and is the standard way to tune executor memory in Spark on Dataproc.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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