- A
Enable autoscaling based on CPU utilization
Autoscaling adjusts the number of workers to meet demand, avoiding over-provisioning and reducing cost.
- B
Use batch loads to BigQuery for streaming inserts
Why wrong: Batch loads are not appropriate for streaming; they add latency and require multiple writes.
- C
Enable Streaming Engine to decouple compute and storage
Streaming Engine reduces cost by allowing compute and state storage to scale independently, reducing idle resources.
- D
Use preemptible VMs for all workers
Why wrong: Preemptible VMs are not recommended for streaming pipelines because they can be terminated at any time, causing data loss.
- E
Use a global window and batch output to BigQuery every hour
Why wrong: Global windows are not suitable for streaming and may incur high costs due to long-running state.
Quick Answer
The answer is to enable Streaming Engine to decouple compute from storage and to activate autoscaling based on throughput or CPU utilization. Streaming Engine reduces costs by shifting the heavy lifting of data buffering and shuffle operations away from worker VMs to a persistent, backend service, which minimizes the compute resources needed per element and allows for more efficient resource usage. Autoscaling further cuts expenses by dynamically adjusting the number of workers to match the actual processing load, preventing over-provisioning during low-throughput periods while maintaining performance during spikes. On the Google Professional Data Engineer exam, this pairing tests your understanding of Dataflow’s architecture for high-throughput streaming, often appearing as a trap where candidates mistakenly choose manual worker tuning or fixed resource allocation. A common memory tip is to think “Streaming Engine saves storage costs, autoscaling saves compute costs”—together they form the cost-minimization duo for any streaming pipeline.
PDE Designing data processing systems Practice Question
This PDE practice question tests your understanding of designing data processing systems. 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.
You are designing a streaming Dataflow pipeline that processes high-throughput data. Which two features can help minimize cost? (Choose TWO.)
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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 autoscaling based on CPU utilization
Option A is correct because enabling autoscaling based on CPU utilization allows the Dataflow pipeline to dynamically adjust the number of worker instances in response to the actual processing load. This prevents over-provisioning during low-throughput periods, directly reducing compute cost while maintaining performance during spikes.
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.
- ✓
Enable autoscaling based on CPU utilization
Why this is correct
Autoscaling adjusts the number of workers to meet demand, avoiding over-provisioning and reducing cost.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use batch loads to BigQuery for streaming inserts
Why it's wrong here
Batch loads are not appropriate for streaming; they add latency and require multiple writes.
- ✓
Enable Streaming Engine to decouple compute and storage
Why this is correct
Streaming Engine reduces cost by allowing compute and state storage to scale independently, reducing idle resources.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use preemptible VMs for all workers
Why it's wrong here
Preemptible VMs are not recommended for streaming pipelines because they can be terminated at any time, causing data loss.
- ✗
Use a global window and batch output to BigQuery every hour
Why it's wrong here
Global windows are not suitable for streaming and may incur high costs due to long-running state.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that preemptible VMs are always cost-effective for streaming workloads, but the trap here is that preemptible VMs are unsuitable for stateful streaming pipelines due to frequent preemption causing data reprocessing and instability.
Detailed technical explanation
How to think about this question
Under the hood, Dataflow's autoscaling uses the 'autoscalingAlgorithm' parameter (e.g., THROUGHPUT_BASED) which monitors backlog metrics like 'ElementCount' and 'EstimatedBytes' per worker, not just CPU. Streaming Engine decouples compute from storage by offloading shuffle and state data to persistent storage (e.g., Cloud Storage), allowing workers to be scaled down without losing state, which is critical for cost-efficient streaming. In real-world scenarios, a pipeline processing IoT sensor data can see 10x throughput variation; autoscaling combined with Streaming Engine avoids paying for idle workers while ensuring no data loss.
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.
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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: Enable autoscaling based on CPU utilization — Option A is correct because enabling autoscaling based on CPU utilization allows the Dataflow pipeline to dynamically adjust the number of worker instances in response to the actual processing load. This prevents over-provisioning during low-throughput periods, directly reducing compute cost while maintaining performance during spikes.
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: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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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