The answer is to use preemptible VMs for Dataflow workers and FlexRS (Flexible Resource Scheduling) for Dataflow batch cost optimization. Preemptible VMs are significantly cheaper than standard Compute Engine instances, making them ideal for fault-tolerant batch pipelines that can handle occasional worker termination, such as a daily 100 GB load from Cloud Storage. FlexRS further reduces costs by offering a discount in exchange for allowing the job to wait up to six hours for resource availability, which suits a daily batch window perfectly. On the Google Professional Data Engineer exam, this question tests your understanding of cost-control strategies for Dataflow, often appearing alongside traps like using standard VMs or increasing worker count. A common memory tip is to associate “batch” with “flexible timing” and “preemptible” with “cheap but killable”—if your pipeline can handle delays and interruptions, these two options are your cost-saving pair.
PDE Practice Question: Building and operationalizing data processing systems
This PDE practice question tests your understanding of building and operationalizing 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.
Which TWO options can help reduce costs for a Dataflow batch pipeline that processes 100 GB of data daily from Cloud Storage? (Choose 2)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use FlexRS (Flexible Resource Scheduling)
FlexRS (Flexible Resource Scheduling) allows you to run batch workloads on a discounted, flexible schedule. It reduces costs by offering lower prices in exchange for the job being able to wait up to 6 hours for resources to become available. This is ideal for a daily 100 GB batch pipeline that can tolerate some scheduling delay.
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 Dataflow Prime (now Dataflow Runner v2)
Why it's wrong here
Dataflow Prime is a performance improvement, not a cost reduction option.
✗
Use high-memory machine types
Why it's wrong here
High-memory machines are more expensive, increasing cost.
✗
Use Streaming Engine
Why it's wrong here
Streaming Engine applies to streaming pipelines, not batch.
✓
Use FlexRS (Flexible Resource Scheduling)
Why this is correct
FlexRS offers discounted pricing for batch jobs that are flexible on start time.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use preemptible VMs for Dataflow workers
Why this is correct
Preemptible VMs are cheaper but can be reclaimed; suitable for batch jobs with checkpointing.
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 distinction between batch and streaming optimizations, so the trap here is that candidates might select Streaming Engine (Option C) thinking it reduces costs in batch pipelines, when it is only relevant for streaming.
Detailed technical explanation
How to think about this question
FlexRS works by allowing Dataflow to schedule the job in a batch queue that can be preempted and resumed, similar to preemptible VMs but with a guaranteed completion window. Under the hood, it uses discounted Compute Engine resources that are reclaimed after 24 hours if not started, and the job must be resilient to delays. In practice, FlexRS can reduce costs by 30-50% compared to on-demand resources for batch pipelines that can wait.
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: Use FlexRS (Flexible Resource Scheduling) — FlexRS (Flexible Resource Scheduling) allows you to run batch workloads on a discounted, flexible schedule. It reduces costs by offering lower prices in exchange for the job being able to wait up to 6 hours for resources to become available. This is ideal for a daily 100 GB batch pipeline that can tolerate some scheduling delay.
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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Question Discussion
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