Quick Answer
The answer is to switch to high-memory machine types like n1-highmem-4. This is the most cost-effective change because the job is memory-intensive, and the n1-standard-4 workers provide only 15 GB of RAM, causing excessive disk spill or out-of-memory errors that cripple performance. By moving to n1-highmem-4, you get 26 GB of RAM per worker—a 73% increase—without adding vCPUs, directly resolving the memory bottleneck at a lower cost than scaling out with more workers. On the Google Professional Data Engineer exam, this scenario tests your ability to match machine types to workload characteristics, specifically distinguishing between memory-optimized and general-purpose families. A common trap is to add more workers, which increases vCPU overhead and licensing costs without fixing the root memory shortage. Memory tip: For memory-intensive Dataproc workloads, think “highmem, not more nodes.”
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 data engineer is running a Dataproc cluster for a batch ETL job that needs to process 10 TB of data. The job is memory-intensive. The cluster currently uses n1-standard-4 workers. Performance is poor. What is the most cost-effective change to improve performance?
Use high-memory machine types (n1-highmem-4)
High-memory machines provide more memory per core, better for memory-bound jobs.
Use preemptible workers to reduce cost
Why wrong: Preemptible workers can be terminated, not suitable for memory-intensive tasks.
Switch to n2-standard-4 machine types
Why wrong: Similar memory/cpu ratio, not an improvement.
Add more n1-standard-4 workers
Why wrong: May not help if memory per worker is insufficient.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use high-memory machine types (n1-highmem-4)
The job is memory-intensive, and n1-standard-4 workers have 15 GB of RAM, which may be insufficient for the workload, causing excessive disk spill or OOM errors. Switching to n1-highmem-4 provides 26 GB of RAM per worker (a 73% increase) without increasing vCPU count, directly addressing the memory bottleneck at a lower cost than adding more workers. This is the most cost-effective change because it improves performance without incurring the overhead of additional vCPUs or licensing costs.
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 high-memory machine types (n1-highmem-4)
Why this is correct
High-memory machines provide more memory per core, better for memory-bound jobs.
Related concept
Read the scenario before looking for a memorised answer.
Use preemptible workers to reduce cost
Why it's wrong here
Preemptible workers can be terminated, not suitable for memory-intensive tasks.
Switch to n2-standard-4 machine types
Why it's wrong here
Similar memory/cpu ratio, not an improvement.
Add more n1-standard-4 workers
Why it's wrong here
May not help if memory per worker is insufficient.
Common exam traps
The trap here is that candidates often assume adding more workers (scaling out) is always the best way to improve performance, but for memory-intensive jobs, scaling up (using high-memory instances) is more cost-effective because it addresses the root cause—per-worker memory pressure—without wasting resources on additional vCPUs.
Trap categories for this question
Similar concept trap
Similar memory/cpu ratio, not an improvement.
Detailed technical explanation
In Dataproc, memory-intensive jobs like large shuffles or in-memory aggregations often hit the YARN container memory limit, causing tasks to spill to disk or fail. n1-highmem-4 instances have a memory-to-vCPU ratio of 6.5 GB per vCPU versus 3.75 GB for n1-standard-4, which aligns better with memory-bound workloads. Under the hood, YARN's `yarn.nodemanager.resource.memory-mb` and Spark's `spark.executor.memory` must be tuned to match the new instance type; simply switching machine types without adjusting these parameters may not yield full benefit.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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: Use high-memory machine types (n1-highmem-4) — The job is memory-intensive, and n1-standard-4 workers have 15 GB of RAM, which may be insufficient for the workload, causing excessive disk spill or OOM errors. Switching to n1-highmem-4 provides 26 GB of RAM per worker (a 73% increase) without increasing vCPU count, directly addressing the memory bottleneck at a lower cost than adding more workers. This is the most cost-effective change because it improves performance without incurring the overhead of additional vCPUs or licensing costs.
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 24, 2026
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