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. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
Which THREE steps can reduce processing costs in a Dataflow streaming pipeline? (Choose three.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use side inputs instead of a cross join.
Option A is correct because side inputs allow you to broadcast a static or slowly-changing dataset to all workers, avoiding the expensive shuffle and per-element processing required by a cross join. In Dataflow, cross joins in streaming mode require stateful processing and can lead to high data amplification, whereas side inputs are distributed efficiently via the streaming engine. This reduces both CPU and memory costs by eliminating redundant data movement.
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 side inputs instead of a cross join.
Why this is correct
Side inputs are more efficient than cross joins, reducing processing cost.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use a batch pipeline for non-critical data.
Why this is correct
Batch pipelines are cheaper than streaming for data that does not require real-time processing.
Related concept
Read the scenario before looking for a memorised answer.
✓
Minimize the use of GroupByKey in streaming mode.
Why this is correct
GroupByKey causes state and shuffle; minimizing reduces resource usage.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a custom runner.
Why it's wrong here
Custom runners are not typically cost-saving and require development effort.
✗
Increase the number of workers.
Why it's wrong here
More workers increase parallelism but also cost.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that scaling out (increasing workers) always reduces costs, when in fact it increases costs unless the pipeline is bottlenecked; the trap is to confuse throughput optimization with cost reduction.
Detailed technical explanation
How to think about this question
Under the hood, Dataflow's streaming engine separates compute from state storage, so minimizing GroupByKey reduces the need for persistent shuffle and state lookups, which are billed as streaming processing units. In real-world scenarios, a pipeline processing IoT sensor data that uses a side input for device metadata instead of a cross join can cut costs by 30-50% because side inputs are cached in memory and do not trigger per-record shuffle. Additionally, batch pipelines for non-critical data avoid the higher per-element cost of streaming mode, which charges for data processed in the streaming engine even during idle windows.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this PCA question in full detail.
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: Use side inputs instead of a cross join. — Option A is correct because side inputs allow you to broadcast a static or slowly-changing dataset to all workers, avoiding the expensive shuffle and per-element processing required by a cross join. In Dataflow, cross joins in streaming mode require stateful processing and can lead to high data amplification, whereas side inputs are distributed efficiently via the streaming engine. This reduces both CPU and memory costs by eliminating redundant data movement.
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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Question Discussion
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