- A
Pub/Sub subscription/num_undelivered_messages and Dataflow job/watermark_lag
Why wrong: num_undelivered_messages is a Pub/Sub metric; watermark_lag is similar to system lag but not standard. Better metrics exist.
- B
Dataproc cluster/yarn_allocated_memory_percentage and Dataflow job/worker_cpu
Why wrong: Dataproc metrics are irrelevant for Dataflow.
- C
Dataflow job/system_lag and Dataflow job/data_freshness
system_lag indicates processing delay; data_freshness shows watermark progress. Both are key for streaming lag.
- D
BigQuery query/execution_times and Dataflow job/elapsed_time
Why wrong: BigQuery metrics irrelevant. elapsed_time is total job duration, not lag.
Question 552 of 1,000
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.
A company uses Dataflow streaming pipelines to process real-time events. They notice increasing system lag over time. Which two Cloud Monitoring metrics should be examined to diagnose the cause?
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
✓
Dataflow job/system_lag and Dataflow job/data_freshness
System lag measures the time between event ingestion and processing. Data freshness shows the watermark. Worker CPU indicates compute resource issues.
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.
- ✗
Pub/Sub subscription/num_undelivered_messages and Dataflow job/watermark_lag
- ✗
Dataproc cluster/yarn_allocated_memory_percentage and Dataflow job/worker_cpu
Why it's wrong here
Dataproc metrics are irrelevant for Dataflow.
- ✓
Dataflow job/system_lag and Dataflow job/data_freshness
Why this is correct
system_lag indicates processing delay; data_freshness shows watermark progress. Both are key for streaming lag.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
BigQuery query/execution_times and Dataflow job/elapsed_time
Why it's wrong here
BigQuery metrics irrelevant. elapsed_time is total job duration, not lag.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Trap categories for this question
Similar concept trap
num_undelivered_messages is a Pub/Sub metric; watermark_lag is similar to system lag but not standard. Better metrics exist.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 PDE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
- →
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: Dataflow job/system_lag and Dataflow job/data_freshness — System lag measures the time between event ingestion and processing. Data freshness shows the watermark. Worker CPU indicates compute resource issues.
What should I do if I get this PDE question wrong?
Identify which PDE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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