The answer is that the cluster is configured with only primary workers, because Dataproc autoscaling can only add or remove secondary workers, not primary ones. Primary workers run both HDFS and compute, while secondary workers are compute-only, and the autoscaler is designed to adjust the number of secondary workers based on YARN memory utilization. If a cluster has no secondary workers, the autoscaler has no nodes to scale, leaving the cluster static even under high load. This question tests your understanding of Dataproc’s two-tier worker architecture, a common trap on the Google Professional Data Engineer exam where candidates assume autoscaling applies to all nodes. A useful memory tip: think of primary workers as the “foundation” that cannot be changed, and secondary workers as the “elastic layer” that the autoscaler stretches or shrinks.
PDE Designing data processing systems Practice Question
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.
A company runs a batch data processing workload using Dataproc clusters that are auto-scaled based on YARN memory utilization. During peak times, jobs take much longer than expected. Analysis shows the cluster is not scaling up despite high YARN memory utilization. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The cluster is using primary workers only; auto-scaling only adds secondary workers
Dataproc clusters have two types of workers: primary workers (which run both HDFS and compute) and secondary workers (compute-only). The autoscaler can only add or remove secondary workers; it cannot scale primary workers. If the cluster uses only primary workers, the autoscaler has no secondary workers to add, so it cannot scale up even under high YARN memory utilization. This explains why the cluster remains static during peak times.
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.
✗
Spark dynamic allocation is disabled, preventing executors from using added workers
Why it's wrong here
Spark dynamic allocation affects executor lifecycle, not cluster scaling.
✗
The cluster autoscaler is misconfigured to scale based on CPU, not memory
Why it's wrong here
Autoscaler uses YARN memory utilization by default; this would still trigger scale-up.
✗
The autoscaler is set to scale down secondary workers, not up
Why it's wrong here
Autoscaler can scale up and down; this does not explain lack of scale-up.
✓
The cluster is using primary workers only; auto-scaling only adds secondary workers
Why this is correct
Auto-scaling adds secondary workers, not primary; if only primary workers exist, no scale-up occurs.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume autoscaling applies to all worker nodes equally, overlooking the Dataproc-specific distinction between primary and secondary workers and the autoscaler's limitation to secondary workers only.
Detailed technical explanation
How to think about this question
In Dataproc, primary workers host both HDFS data nodes and YARN node managers, making them critical for data durability. The autoscaler only adjusts the number of secondary (preemptible or non-preemptible compute-only) workers to avoid data loss risks. When a cluster is created with only primary workers, the autoscaler has no secondary worker pool to manipulate, so it remains idle regardless of YARN memory pressure. This design forces users to explicitly add secondary workers if they want elastic scaling.
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
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.
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.
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: The cluster is using primary workers only; auto-scaling only adds secondary workers — Dataproc clusters have two types of workers: primary workers (which run both HDFS and compute) and secondary workers (compute-only). The autoscaler can only add or remove secondary workers; it cannot scale primary workers. If the cluster uses only primary workers, the autoscaler has no secondary workers to add, so it cannot scale up even under high YARN memory utilization. This explains why the cluster remains static during peak times.
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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.