- A
Increase the worker machine type and memory
Why wrong: This may temporarily mitigate but does not eliminate memory issues; Streaming Engine is more effective.
- B
Use Cloud Pub/Sub for buffering and then load into BigQuery in batches
Why wrong: This changes the architecture significantly and may introduce additional latency.
- C
Enable autoscaling and increase the maximum number of workers
Why wrong: More workers do not fix per-worker memory issues.
- D
Enable Dataflow Streaming Engine
Streaming Engine moves state to a backend service, reducing memory usage per worker.
Google PCA Practice Question: Analyze and optimize technical and business processes
This PCA practice question tests your understanding of analyze and optimize technical and business processes. 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 streaming data pipeline using Dataflow to process real-time data and insert into BigQuery. Recently, workers are frequently failing with out-of-memory errors and the pipeline latency is increasing. What should they do to resolve the issue?
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
Enable Dataflow Streaming Engine
Dataflow Streaming Engine offloads the streaming data processing state and shuffle data from worker memory to a backend service, reducing memory pressure on workers. This directly addresses out-of-memory errors and latency increases without requiring manual scaling or machine type changes. It is the recommended solution for streaming pipelines experiencing memory bottlenecks.
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 worker machine type and memory
Why it's wrong here
This may temporarily mitigate but does not eliminate memory issues; Streaming Engine is more effective.
- ✗
Use Cloud Pub/Sub for buffering and then load into BigQuery in batches
Why it's wrong here
This changes the architecture significantly and may introduce additional latency.
- ✗
Enable autoscaling and increase the maximum number of workers
Why it's wrong here
More workers do not fix per-worker memory issues.
- ✓
Enable Dataflow Streaming Engine
Why this is correct
Streaming Engine moves state to a backend service, reducing memory usage per worker.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that scaling up resources (more memory or more workers) is the primary fix for streaming pipeline memory issues, when the real solution is to offload state management using Streaming Engine.
Detailed technical explanation
How to think about this question
Dataflow Streaming Engine uses a persistent backend to store window state, timers, and shuffle data, which are typically held in worker memory in standard streaming mode. This offloading reduces the memory footprint per worker, allowing pipelines to handle larger state sizes and longer windows without OOM errors. In practice, this is critical for pipelines with large sliding windows or global aggregations where state can grow unbounded.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this PCA question test?
Analyze and optimize technical and business processes — This question tests Analyze and optimize technical and business processes — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable Dataflow Streaming Engine — Dataflow Streaming Engine offloads the streaming data processing state and shuffle data from worker memory to a backend service, reducing memory pressure on workers. This directly addresses out-of-memory errors and latency increases without requiring manual scaling or machine type changes. It is the recommended solution for streaming pipelines experiencing memory bottlenecks.
What should I do if I get this PCA 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 PCA 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 PCA exam.
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