Quick Answer
The answer is to increase the number of worker nodes and reduce the number of cores per worker. This configuration reduces memory pressure by distributing the workload across more JVMs, each with a smaller heap, which lowers the per-executor memory requirement and prevents OutOfMemory errors without altering Spark code. On the Google Professional Data Engineer exam, this scenario tests your understanding of Spark resource tuning within Dataproc, specifically how to balance parallelism and memory contention—a common trap is to simply add more memory per node, which can waste resources and fail to address core-level heap fragmentation. A key memory tip: remember that fewer cores per executor means less concurrent task overhead, so when you see "Dataproc Spark memory pressure reduce workers cores," think "spread the load, shrink the heap."
This PDE practice question tests your understanding of designing data processing systems. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
A company uses Cloud Dataproc to run Spark ML jobs. The jobs are memory-intensive and often fail with OutOfMemory errors. Which action would most effectively reduce memory pressure without changing the Spark code?
Increase the number of worker nodes and reduce the number of cores per worker.
More workers spread memory load.
Increase the master node's memory.
Why wrong: Master does not process data.
Increase the number of Spark partitions.
Why wrong: May increase overhead, not reduce memory per task.
Use preemptible VMs for workers.
Why wrong: Does not reduce memory pressure; may cause failures.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Increase the number of worker nodes and reduce the number of cores per worker.
Increasing the number of worker nodes while reducing the number of cores per worker reduces memory pressure by distributing the workload across more JVMs, each with a smaller heap. This lowers the per-executor memory requirement and reduces the risk of OutOfMemory errors without modifying Spark code. In Cloud Dataproc, this approach directly addresses memory contention by giving each executor fewer tasks to process concurrently.
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.
Increase the number of worker nodes and reduce the number of cores per worker.
Why this is correct
More workers spread memory load.
Related concept
Read the scenario before looking for a memorised answer.
Increase the master node's memory.
Why it's wrong here
Master does not process data.
Increase the number of Spark partitions.
Why it's wrong here
May increase overhead, not reduce memory per task.
Use preemptible VMs for workers.
Why it's wrong here
Does not reduce memory pressure; may cause failures.
Common exam traps
Google Cloud often tests the misconception that adding more partitions or increasing master memory solves executor-level memory issues, when the real solution is to reduce per-executor task concurrency by adjusting the worker-to-core ratio.
Detailed technical explanation
Under the hood, each Spark executor runs in a JVM with a fixed heap size (spark.executor.memory). Reducing cores per worker means fewer concurrent tasks per executor, so each task gets a larger share of the executor's heap, reducing the chance of spilling to disk or OOM. In Cloud Dataproc, this is configured via the --num-workers and --worker-machine-type-uri flags, and the optimal ratio often follows the formula: executor memory = (total worker memory / (cores per worker + 1)) to leave room for overhead.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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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: Increase the number of worker nodes and reduce the number of cores per worker. — Increasing the number of worker nodes while reducing the number of cores per worker reduces memory pressure by distributing the workload across more JVMs, each with a smaller heap. This lowers the per-executor memory requirement and reduces the risk of OutOfMemory errors without modifying Spark code. In Cloud Dataproc, this approach directly addresses memory contention by giving each executor fewer tasks to process concurrently.
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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