PMLE Monitoring ML Solutions Practice Question
A team uses Vertex AI Pipelines for continuous training triggered by model drift. They want to monitor the pipeline execution cost and optimize resource usage. Which THREE metrics should they track? (Choose 3)
⚠ Common exam trap
The trap is selecting model-quality metrics (accuracy, failure count) as cost metrics — the exam tests whether you distinguish operational cost/resource metrics from model performance metrics.
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
✓
Pipeline execution duration
Option A (Pipeline execution duration) is correct because the wall-clock time a Vertex AI Pipeline run takes directly drives the compute resources billed by the underlying services (Vertex AI Training, Dataflow, etc.), so tracking duration is essential for spotting inefficiencies and optimizing resource usage. Option D (Total GPU hours consumed per pipeline run) is correct because GPUs are the most expensive accelerator resource in Vertex AI; measuring GPU hours per run quantifies accelerator consumption and reveals over-provisioning or idle GPU time that can be right-sized. Option E (Cost per pipeline run in Cloud Billing) is correct because Cloud Billing cost data (exported to BigQuery and labeled per pipeline run) gives the actual monetary cost, which is the ground truth for monitoring execution cost and validating optimization efforts. Option B (Number of failed pipeline runs) is not a cost or resource-usage metric; it measures reliability, so it does not directly address cost monitoring or resource optimization. Option C (Model accuracy on validation set) is a model-quality metric, not an execution-cost or resource-utilization metric, so it is out of scope for this question.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Pipeline execution duration
Why this is correct
Pipeline execution duration exposes wall-clock time each run consumes, revealing whether drift-triggered retraining is becoming slower and therefore costlier. Tracking it against a baseline highlights inefficient steps or resource contention, directly supporting the optimisation goal alongside billing and GPU-hour metrics.
- ✗
Number of failed pipeline runs
Why it's wrong here
Tracking failed pipeline runs measures reliability, not spend or resource consumption, so it cannot reveal cost drivers or capacity waste. It is tempting because failure counts belong to pipeline health monitoring and would suit alerting on training reliability, yet the stem asks specifically for cost and resource-usage metrics.
- ✗
Model accuracy on validation set
Why it's wrong here
Model accuracy measures predictive quality, not pipeline execution cost or resource consumption, so it cannot satisfy the monitoring requirement. It is tempting because accuracy tracking is central to model performance monitoring and drift detection, and would be the right choice when the goal is evaluating whether retraining improved the deployed model.
- ✓
Total GPU hours consumed per pipeline run
Why this is correct
Total GPU hours per pipeline run quantifies accelerator consumption, the dominant cost driver in Vertex AI training. Because drift-triggered retraining scales GPU usage, this metric exposes over-provisioned or idle accelerators, enabling right-sizing of machine types and reduction of wasted spend.
- ✓
Cost per pipeline run in Cloud Billing
Why this is correct
Cloud Billing's cost per pipeline run converts consumed resources into actual spend, the only metric expressing the drift-triggered training cost directly. It satisfies the monitoring requirement by attributing Vertex AI charges to individual executions, letting the team compare runs and justify optimisation decisions.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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