PDE Maintaining and Automating Data Workloads Practice Question
Your Dataflow streaming job is experiencing high system lag. You want to identify the root cause. Which Cloud Monitoring metrics should you examine first? (Choose the best option.)
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
✓
System lag and worker CPU utilization
For streaming Dataflow jobs, system lag measures the maximum time between the event timestamp and when it is processed. High system lag typically indicates that the pipeline cannot keep up with the input rate. Worker CPU utilization is a key metric to check if workers are overloaded. Data freshness is for batch pipelines. Element count alone doesn't show lag. Backlog bytes could be useful but worker CPU is more directly indicative of processing capacity issues.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Data freshness and worker CPU utilization
Why it's wrong here
Data freshness is more relevant for batch pipelines; system lag is the streaming metric.
- ✓
System lag and worker CPU utilization
Why this is correct
High system lag combined with high CPU suggests workers are bottlenecked; low CPU may indicate other issues.
- ✗
Element count and data freshness
Why it's wrong here
Element count shows throughput but not lag; data freshness is for batch.
- ✗
Backlog bytes and system lag
Why it's wrong here
Backlog bytes is useful but worker CPU is more direct for diagnosing capacity.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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