- A
Increase the maximum number of workers in autoscaling from 20 to 50.
Why wrong: Adding more workers spreads the load but each worker still needs to handle its share; the per-worker memory issue may persist.
- B
Change the worker machine type to n2-highmem-8 (8 vCPU, 64 GB memory) in the Dataflow job configuration.
Increasing memory per worker directly addresses OOM without major pipeline changes.
- C
Reduce the batch size in the Dataflow pipeline by setting the `max_batch_size` parameter to a lower value.
Why wrong: Reducing batch size may increase the number of batches and overhead, potentially worsening performance.
- D
Increase the number of Bigtable nodes to improve read throughput.
Why wrong: Bigtable reads are not the bottleneck; the OOM is on Dataflow workers, not Bigtable.
Quick Answer
The answer is to change the worker machine type to n2-highmem-8, which doubles the memory from 32 GB to 64 GB while keeping the same 8 vCPU count. This resolves the Dataflow out of memory error because the root cause is a per-worker memory bottleneck: the Bigtable row size increased tenfold from 1 KB to 10 KB, and the event rate jumped from 500 to 5000 events per second, forcing each worker to cache larger user profiles and process bigger batches. On the Google Professional Data Engineer exam, this scenario tests your understanding of Dataflow memory management under streaming workloads, where simply increasing worker count via autoscaling won't help if each worker individually runs out of heap space. A common trap is to add more workers or reduce batch size, but the correct fix is to match the machine type to the memory-intensive workload. Remember the mnemonic: "High memory for high rows" — when Bigtable rows grow, upgrade to highmem.
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.
You are a data engineer at a global e-commerce company. Your team manages a real-time recommendation system that ingests user clickstream events from a Pub/Sub topic (topic-clickstream). The pipeline uses Dataflow to read events, join with user profile data from Cloud Bigtable, compute recommendations using a machine learning model hosted on Cloud Run, and write results to a BigQuery table for analytics. The pipeline has been running smoothly for months, but recently the Dataflow job started failing with the error: "Workflow failed. Causes: S01:ReadPubSub/Read+Transform/ParDo(ExtractUserID)+ ... (5a3b2c1d) The job failed because a worker encountered an out-of-memory error." The Dataflow job uses the Streaming Engine feature with a worker type of n2-standard-8 (8 vCPU, 32 GB memory) and autoscaling from 2 to 20 workers. The clickstream event rate has increased from 500 events/second to 5000 events/second over the past week. The user profile data in Bigtable has also grown, with average row size increasing from 1 KB to 10 KB due to additional fields. You need to resolve the out-of-memory errors without completely redesigning the pipeline. What should you do?
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
Change the worker machine type to n2-highmem-8 (8 vCPU, 64 GB memory) in the Dataflow job configuration.
Option B is correct because the out-of-memory error is caused by the increased per-worker memory load from larger Bigtable rows (1 KB to 10 KB) and higher event throughput (500 to 5000 events/sec). Switching to n2-highmem-8 doubles the memory from 32 GB to 64 GB, giving each worker more headroom to cache user profiles and process larger batches without OOM. This directly addresses the root cause without redesigning the pipeline.
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.
- ✗
Increase the maximum number of workers in autoscaling from 20 to 50.
Why it's wrong here
Adding more workers spreads the load but each worker still needs to handle its share; the per-worker memory issue may persist.
- ✓
Change the worker machine type to n2-highmem-8 (8 vCPU, 64 GB memory) in the Dataflow job configuration.
Why this is correct
Increasing memory per worker directly addresses OOM without major pipeline changes.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Reduce the batch size in the Dataflow pipeline by setting the `max_batch_size` parameter to a lower value.
Why it's wrong here
Reducing batch size may increase the number of batches and overhead, potentially worsening performance.
- ✗
Increase the number of Bigtable nodes to improve read throughput.
Why it's wrong here
Bigtable reads are not the bottleneck; the OOM is on Dataflow workers, not Bigtable.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that scaling out (more workers) solves memory issues, when in fact the per-worker memory limit is the bottleneck and must be increased via a higher-memory machine type.
Detailed technical explanation
How to think about this question
Dataflow workers using Streaming Engine still buffer state and user profile data in memory for joins; with 10 KB rows and 5000 events/sec, each worker can easily accumulate hundreds of megabytes of cached profiles. The n2-highmem-8 machine type provides a 2:1 memory-to-vCPU ratio (64 GB for 8 vCPUs) versus the 4:1 ratio of n2-standard-8 (32 GB for 8 vCPUs), which is critical for memory-bound streaming joins. In real-world scenarios, simply scaling out without scaling up memory often leads to the same OOM errors because the memory per worker remains the bottleneck.
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
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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: Change the worker machine type to n2-highmem-8 (8 vCPU, 64 GB memory) in the Dataflow job configuration. — Option B is correct because the out-of-memory error is caused by the increased per-worker memory load from larger Bigtable rows (1 KB to 10 KB) and higher event throughput (500 to 5000 events/sec). Switching to n2-highmem-8 doubles the memory from 32 GB to 64 GB, giving each worker more headroom to cache user profiles and process larger batches without OOM. This directly addresses the root cause without redesigning the pipeline.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 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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