PDE Maintaining and Automating Data Workloads Practice Question
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.
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
Why it's wrong here
num_undelivered_messages is a Pub/Sub metric; watermark_lag is similar to system lag but not standard. Better metrics exist.
- ✗
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.
- ✗
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.
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