PDE Maintaining and Automating Data Workloads Practice Question
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?
⚠ Common exam trap
Many 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.
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.
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.
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.
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