The answer is a global window with allowed lateness of 10 minutes and an after-watermark trigger with early firings. This configuration minimizes cost in a Dataflow streaming pipeline to Bigtable because it avoids the state explosion and shuffle overhead of fixed or sliding windows, which would create many small window panes and increase Bigtable write operations. Instead, the global window keeps all data in a single, efficient state, while the after-watermark trigger with early firings ensures results are emitted promptly for late data up to 10 minutes, maintaining exactly-once semantics from Pub/Sub. On the Google Professional Data Engineer exam, this scenario tests your understanding of balancing cost and correctness in streaming pipelines—a common trap is choosing fixed windows for “freshness,” which dramatically raises costs. Remember the memory tip: “Global for cost, trigger for timeliness; late data needs a watermark, not a window.”
PDE Practice Question: Building and operationalizing data processing systems
This PDE practice question tests your understanding of building and operationalizing data processing systems. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 pipeline using Cloud Dataflow with exactly-once semantics. The source is Pub/Sub and the sink is Cloud Bigtable. The pipeline must handle late data up to 10 minutes. You need to minimize cost while maintaining correctness. Which configuration should you use?
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 the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Global window with allowed lateness 10 minutes and trigger=afterWatermark with early firings
Option C is correct because a global window with an after-watermark trigger and early firings is the most cost-effective way to handle unbounded data from Pub/Sub with exactly-once semantics, while allowing up to 10 minutes of lateness. Fixed or sliding windows would create many small window states, increasing Bigtable write costs and shuffle overhead. The global window minimizes state and processing, and the trigger ensures results are emitted promptly without accumulating panes.
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.
✗
Fixed windows of 1 minute with allowed lateness 10 minutes and accumulating fired panes
Why it's wrong here
Fixed windows with early firings generate many outputs, increasing cost.
✗
Sliding windows of 1 minute with allowed lateness 10 minutes and accumulating fired panes
Why it's wrong here
Sliding windows produce many overlapping windows, increasing cost.
✓
Global window with allowed lateness 10 minutes and trigger=afterWatermark with early firings
Why this is correct
Global window with watermark-based triggers handles late data efficiently.
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.
✗
Session windows of 5 minutes with gap duration 1 minute and discarding fired panes
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that windowing is always required for streaming pipelines, but here the sink (Bigtable) stores individual records, so a global window with triggers is the most efficient and correct choice, not fixed or sliding windows.
Trap categories for this question
Command / output trap
Fixed windows with early firings generate many outputs, increasing cost.
Detailed technical explanation
How to think about this question
Under the hood, Cloud Dataflow's global window with an after-watermark trigger uses the watermark to estimate completeness and fires when the watermark passes the event time, with early firings providing low-latency updates. The allowed lateness of 10 minutes means Dataflow will hold state for late data up to that duration, then drop it, which is critical for exactly-once semantics when using Pub/Sub as a source. In practice, this configuration is ideal for writing raw event data to Bigtable where downstream systems handle aggregation, avoiding unnecessary windowing overhead.
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: Global window with allowed lateness 10 minutes and trigger=afterWatermark with early firings — Option C is correct because a global window with an after-watermark trigger and early firings is the most cost-effective way to handle unbounded data from Pub/Sub with exactly-once semantics, while allowing up to 10 minutes of lateness. Fixed or sliding windows would create many small window states, increasing Bigtable write costs and shuffle overhead. The global window minimizes state and processing, and the trigger ensures results are emitted promptly without accumulating panes.
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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Question Discussion
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