The answer is preemptible worker reclamation causing YARN container failures and task retries. When Google Cloud reclaims preemptible workers, active YARN containers are abruptly terminated, losing intermediate shuffle data and forcing Spark to recompute partitions from scratch. This explains both the high shuffle spill ratio—since partial data is discarded mid-operation—and the doubled job time, as each reclaimed worker triggers cascading retries across dependent stages. On the Google Professional Data Engineer exam, this scenario tests your understanding of how preemptible VM behavior impacts distributed compute frameworks like Spark on Dataproc; a common trap is blaming data skew or cluster sizing when the real culprit is infrastructure instability. Remember the mnemonic “Preempted = Partition Pain”: reclamation kills containers, kills shuffle data, and kills job speed.
PDE Practice Question: Building and operationalizing data processing systems
This PDE practice question tests your understanding of building and operationalizing data processing systems. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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 daily batch data processing pipeline using Cloud Dataproc. The pipeline reads 10 TB of CSV files from Cloud Storage, performs a heavy aggregation (GroupBy) and joins with a small reference table, then writes the results to BigQuery. The cluster consists of 20 n1-standard-8 nodes, including 10 preemptible workers for cost savings. Recently, the job completion time has doubled from 30 minutes to over an hour. The job logs show many tasks being retried, and the Shuffle spill ratio is high. No significant data volume change was observed. What is the most likely root 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
✓
Preemptible workers are being reclaimed, causing YARN container failures and task retries.
The correct answer is C because preemptible workers are frequently reclaimed by Google Cloud, causing YARN containers to fail and tasks to be retried. This leads to increased job completion time and a high shuffle spill ratio, as partial shuffle data is lost and must be recomputed. The doubling of job time without data volume change strongly points to infrastructure instability rather than data or configuration issues.
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.
✗
The cluster's HDFS is running out of space due to intermediate shuffle data.
Why it's wrong here
Cloud Dataproc uses Cloud Storage for temporary data, not HDFS, and spill is to disk.
✗
Data skew has developed, causing a few tasks to process most of the data.
Why it's wrong here
Data skew could cause slowdown, but logs show many retries, not just a few slow tasks.
✓
Preemptible workers are being reclaimed, causing YARN container failures and task retries.
Why this is correct
Preemptible nodes can be taken at any time; Shuffle-heavy jobs suffer greatly from lost intermediate data.
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.
✗
The reference table has increased in size, causing more data to be broadcast to all workers.
Why it's wrong here
Small reference table is unlikely to cause such a dramatic slowdown.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may attribute high shuffle spill and task retries to data skew or HDFS space, but the key clue is the unchanged data volume and the use of preemptible workers, which directly cause container failures and retries.
Trap categories for this question
Command / output trap
Data skew could cause slowdown, but logs show many retries, not just a few slow tasks.
Detailed technical explanation
How to think about this question
Preemptible workers in Cloud Dataproc are reclaimed within 24 hours (average 30 seconds notice), causing YARN NodeManager to lose containers. When a container fails, Spark's speculative execution re-runs the task, but shuffle data written to local disk is lost, forcing recomputation and increasing shuffle spill. The high shuffle spill ratio indicates that intermediate data is being written to disk more than expected, often due to container failures or insufficient memory, but here the root cause is preemption-induced recomputation.
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.
Building and operationalizing data processing systems — This question tests Building and operationalizing data processing systems — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Preemptible workers are being reclaimed, causing YARN container failures and task retries. — The correct answer is C because preemptible workers are frequently reclaimed by Google Cloud, causing YARN containers to fail and tasks to be retried. This leads to increased job completion time and a high shuffle spill ratio, as partial shuffle data is lost and must be recomputed. The doubling of job time without data volume change strongly points to infrastructure instability rather than data or configuration issues.
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.
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Question Discussion
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