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PMLE Practice Question: A team is responsible for monitoring the health…
A team is responsible for monitoring the health of a Vertex AI pipeline that runs daily. Which THREE resources should they use to gain visibility into pipeline performance and failures? (Choose 3.)
⚠ Common exam trap
Google Cloud often tests the distinction between monitoring (observing run-level metrics and logs) and tracing (analyzing request-level latency), leading candidates to incorrectly select Cloud Trace for pipeline health visibility when it is actually intended for distributed request tracing.
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
✓
Vertex AI Experiments for comparing pipeline runs
Vertex AI Experiments (C) is correct because it records and compares pipeline runs, letting the team track parameters, metrics, and artifacts across daily executions to evaluate performance trends. Cloud Monitoring (D) is correct because it collects pipeline run metrics and supports alerting policies so the team can be notified of failures or anomalies in the daily pipeline. Cloud Logging (E) is correct because pipeline step logs are written to Cloud Logging, providing detailed diagnostic visibility into individual step failures. Cloud Trace (A) is not the right fit here since it targets distributed request latency tracing rather than Vertex AI pipeline run health, and Cloud Composer (B) is a separate managed Airflow service for orchestrating DAGs, not the native monitoring surface for a Vertex AI pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud Trace for analyzing distributed execution
Why it's wrong here
Cloud Trace records distributed request latency across services, which does not surface Vertex AI pipeline run status, task failures or scheduling outcomes. It is tempting because tracing genuinely aids performance diagnosis, and would be correct for latency analysis of instrumented microservices rather than pipeline health monitoring.
- ✗
Cloud Composer for tracking DAGs
Why it's wrong here
Cloud Composer tracks Airflow DAGs, whereas Vertex AI Pipelines runs are orchestrated by Vertex AI itself, so DAG state reveals nothing about pipeline execution. It is tempting because Composer monitors workflow orchestration, and would be correct if the pipelines were authored as Airflow DAGs instead.
- ✓
Vertex AI Experiments for comparing pipeline runs
Why this is correct
Vertex AI Experiments records pipeline runs with their parameters, metrics and artefacts, enabling side-by-side comparison of successive daily executions. This satisfies the visibility requirement by exposing run-to-run performance changes and surfacing which pipeline iteration failed or degraded.
- ✓
Cloud Monitoring for metrics and alerts on pipeline runs
Why this is correct
Cloud Monitoring captures Vertex AI pipeline run metrics and lets the team configure alerts on failures or performance thresholds, satisfying the requirement for proactive visibility into daily pipeline health rather than relying on manual inspection of individual step outputs.
- ✓
Cloud Logging for viewing pipeline step logs
Why this is correct
Cloud Logging retains the per-step logs emitted by Vertex AI Pipelines components, letting the team inspect error messages and stack traces from failed steps, which satisfies the need to diagnose why a daily pipeline run failed.
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