- A
Use a single-node cluster
Why wrong: A single node cannot process large files efficiently.
- B
Use a cluster with preemptible worker nodes and high-CPU machine types
Preemptible VMs reduce cost, high-CPU machines improve speed.
- C
Use HDFS for input data to avoid network latency
Why wrong: HDFS on Dataproc is ephemeral; Cloud Storage is the recommended input source.
- D
Use a cluster with many standard worker nodes
Why wrong: Standard nodes are fine but not optimized for speed; they cost more.
PDE Designing data processing systems Practice Question
This PDE practice question tests your understanding of designing data processing systems. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 needs to process large files (100GB each) from Cloud Storage using Dataproc. They want to minimize job execution time. Which configuration is most appropriate?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use a cluster with preemptible worker nodes and high-CPU machine types
Option B is correct because preemptible worker nodes are significantly cheaper than standard nodes, allowing you to scale out the cluster with many more workers for the same cost, which directly reduces job execution time for embarrassingly parallel data processing tasks. High-CPU machine types are ideal for compute-intensive Dataproc jobs like data transformation or machine learning, as they provide more vCPUs per core for parallel processing. This combination maximizes parallelism and minimizes wall-clock time for large-scale batch jobs.
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.
- ✗
Use a single-node cluster
Why it's wrong here
A single node cannot process large files efficiently.
- ✓
Use a cluster with preemptible worker nodes and high-CPU machine types
Why this is correct
Preemptible VMs reduce cost, high-CPU machines improve speed.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use HDFS for input data to avoid network latency
Why it's wrong here
HDFS on Dataproc is ephemeral; Cloud Storage is the recommended input source.
- ✗
Use a cluster with many standard worker nodes
Why it's wrong here
Standard nodes are fine but not optimized for speed; they cost more.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume standard worker nodes are always better for performance, ignoring the cost-benefit of preemptible nodes that allow scaling to many more workers for the same budget, which directly reduces execution time for parallelizable jobs.
Detailed technical explanation
How to think about this question
Preemptible VMs in Google Cloud are Compute Engine instances that last up to 24 hours and can be terminated at any time, but they cost about 60-80% less than standard instances. Dataproc handles preemptible node failures gracefully by redistributing tasks to other workers, making them ideal for fault-tolerant batch jobs. High-CPU machine types (e.g., n1-highcpu-16) offer a higher vCPU-to-memory ratio, which is optimal for CPU-bound processing like data shuffling or map-reduce operations, but they can be memory-constrained for shuffle-heavy workloads, so you must ensure your job's memory requirements are met.
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 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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this PDE question test?
Designing data processing systems — This question tests Designing data processing systems — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a cluster with preemptible worker nodes and high-CPU machine types — Option B is correct because preemptible worker nodes are significantly cheaper than standard nodes, allowing you to scale out the cluster with many more workers for the same cost, which directly reduces job execution time for embarrassingly parallel data processing tasks. High-CPU machine types are ideal for compute-intensive Dataproc jobs like data transformation or machine learning, as they provide more vCPUs per core for parallel processing. This combination maximizes parallelism and minimizes wall-clock time for large-scale batch jobs.
What should I do if I get this PDE 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: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.
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