- A
Insufficient workers
Insufficient workers create backpressure and increased latency as the pipeline cannot keep up with throughput.
- B
Pub/Sub subscription issue
Why wrong: Subscription issues would cause missing data or ingestion errors, not necessarily increased processing latency.
- C
Too many shards
Why wrong: Too many shards can cause overhead, but it's less common than insufficient workers.
- D
Wrong machine type
Why wrong: Machine type affects per-machine performance, but insufficient workers is more likely to cause increased latency.
Question 871 of 1,000
easyMultiple ChoiceObjective-mapped
Why Dataflow Streaming Latency Increases: Insufficient Workers
This PDE practice question tests your understanding of pde exam topics. 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 uses Dataflow to process streaming data from Pub/Sub. They notice increased processing latency. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Quick Answer
The answer is insufficient workers, because Dataflow streaming latency increases most commonly when the number of Compute Engine instances is too low to handle the incoming data rate from Pub/Sub. When workers are insufficient, the pipeline cannot process messages quickly enough, creating backpressure that causes unacknowledged messages to accumulate in Pub/Sub and drives up end-to-end latency. On the Google Professional Data Engineer exam, this scenario tests your understanding of Dataflow autoscaling limitations—specifically that autoscaling can be delayed or capped by a configured max worker count, making manual scaling or configuration review the primary corrective action. A common trap is to blame Pub/Sub throughput or code inefficiency first, but the root cause is almost always worker count when latency rises suddenly under steady load. Memory tip: think of a checkout line—if you have too few cashiers (workers), the line (Pub/Sub backlog) grows, and customers wait longer.
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
✓
Insufficient workers
In Dataflow, processing latency increases most commonly due to insufficient workers, as the streaming pipeline cannot keep up with the incoming data rate when the number of Compute Engine instances is too low. This causes backpressure from Pub/Sub, leading to growing unacknowledged messages and higher end-to-end latency. Autoscaling may be delayed or limited by max worker count settings, making manual or configuration-based worker scaling the primary corrective action.
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.
- ✓
Insufficient workers
Why this is correct
Insufficient workers create backpressure and increased latency as the pipeline cannot keep up with throughput.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Pub/Sub subscription issue
Why it's wrong here
Subscription issues would cause missing data or ingestion errors, not necessarily increased processing latency.
- ✗
Too many shards
Why it's wrong here
Too many shards can cause overhead, but it's less common than insufficient workers.
- ✗
Wrong machine type
Why it's wrong here
Machine type affects per-machine performance, but insufficient workers is more likely to cause increased latency.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that Pub/Sub subscription issues (like ack deadline) are the primary cause of latency, but the trap here is that latency in Dataflow is almost always a worker scaling problem, not a Pub/Sub configuration issue.
Detailed technical explanation
How to think about this question
Dataflow streaming pipelines use a 'committed' parallelism model where each worker processes a set of key ranges; if workers are insufficient, the backlog of unprocessed messages in Pub/Sub grows, increasing latency. Under the hood, Dataflow’s autoscaler monitors the 'System Lag' metric and requests additional workers, but if the max workers is set too low or the pipeline has a bottleneck (e.g., a GroupByKey with hot keys), scaling may not resolve the latency. In real-world scenarios, a sudden spike in traffic can overwhelm a fixed worker pool, causing latency to climb linearly with backlog size until workers are added.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Insufficient workers — In Dataflow, processing latency increases most commonly due to insufficient workers, as the streaming pipeline cannot keep up with the incoming data rate when the number of Compute Engine instances is too low. This causes backpressure from Pub/Sub, leading to growing unacknowledged messages and higher end-to-end latency. Autoscaling may be delayed or limited by max worker count settings, making manual or configuration-based worker scaling the primary corrective action.
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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
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