- A
BigQuery write throughput is throttling the pipeline.
Why wrong: BigQuery throttling would cause backpressure, but system lag is more directly tied to processing capacity. However, this is less likely than worker underprovisioning.
- B
The Pub/Sub subscription has too many unacknowledged messages.
Why wrong: High undelivered messages are a symptom, not a cause; the pipeline is not processing fast enough.
- C
The pipeline is using a global window with late data handling.
Why wrong: Global windows with late data can cause watermark lag, but not necessarily increasing system lag; system lag is about processing delay.
- D
The Dataflow pipeline is underprovisioned with workers, causing processing backlog.
Insufficient workers lead to a backlog, increasing system lag. Autoscaling may be delayed or maxed out.
PDE Maintaining and Automating Data Workloads Practice Question
This PDE practice question tests your understanding of maintaining and automating data workloads. 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 monitoring a streaming Dataflow pipeline that reads from Pub/Sub and writes to BigQuery. In Cloud Monitoring, you notice that the 'system_lag' metric is increasing over time and now exceeds 10 minutes. The 'data_watermark' metric shows a steady lag. What is the most likely cause of the increasing system lag?
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.
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
The Dataflow pipeline is underprovisioned with workers, causing processing backlog.
Option D is correct because an underprovisioned pipeline lacks sufficient worker resources to process incoming messages at the rate they arrive. This causes a growing backlog in the pipeline's internal buffers, which directly increases the 'system_lag' metric (the time between data ingestion and processing). The 'data_watermark' lag remaining steady indicates that the pipeline is still making progress on event-time processing, but the overall processing capacity is insufficient to keep up with the input rate.
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.
- ✗
BigQuery write throughput is throttling the pipeline.
Why it's wrong here
BigQuery throttling would cause backpressure, but system lag is more directly tied to processing capacity. However, this is less likely than worker underprovisioning.
- ✗
The Pub/Sub subscription has too many unacknowledged messages.
Why it's wrong here
High undelivered messages are a symptom, not a cause; the pipeline is not processing fast enough.
- ✗
The pipeline is using a global window with late data handling.
Why it's wrong here
Global windows with late data can cause watermark lag, but not necessarily increasing system lag; system lag is about processing delay.
- ✓
The Dataflow pipeline is underprovisioned with workers, causing processing backlog.
Why this is correct
Insufficient workers lead to a backlog, increasing system lag. Autoscaling may be delayed or maxed out.
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'system_lag' (processing delay) with 'data_watermark' (event-time completeness), leading them to incorrectly attribute the issue to late data handling or Pub/Sub acknowledgment problems instead of a simple resource underprovisioning.
Detailed technical explanation
How to think about this question
The 'system_lag' metric in Dataflow represents the time between when data enters the pipeline (e.g., from Pub/Sub) and when it is processed by the current stage. An increasing system lag with a steady watermark lag is a classic sign of a resource bottleneck—often CPU or memory—rather than a data-ordering or late-data issue. In practice, this can occur when the pipeline's autoscaling is disabled or max workers is set too low, causing the backlog to grow even though the pipeline is correctly processing events in order.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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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Maintaining and Automating Data Workloads — study guide chapter
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FAQ
Questions learners often ask
What does this PDE question test?
Maintaining and Automating Data Workloads — This question tests Maintaining and Automating Data Workloads — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The Dataflow pipeline is underprovisioned with workers, causing processing backlog. — Option D is correct because an underprovisioned pipeline lacks sufficient worker resources to process incoming messages at the rate they arrive. This causes a growing backlog in the pipeline's internal buffers, which directly increases the 'system_lag' metric (the time between data ingestion and processing). The 'data_watermark' lag remaining steady indicates that the pipeline is still making progress on event-time processing, but the overall processing capacity is insufficient to keep up with the input rate.
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.
About these practice questions
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Last reviewed: Jul 4, 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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