PMLE Monitoring ML Solutions Practice Question

A company has deployed a model to a Vertex AI Endpoint and wants to receive an alert when the model's prediction latency exceeds a threshold. They have configured Cloud Monitoring to track the endpoint's latency metrics. They now need to create a notification channel to send alerts to their on-call team. Which Cloud Monitoring resource should they use to define the condition that triggers the alert?

⚠ Common exam trap

Many candidates confuse the notification channel with the alerting policy; the notification channel only specifies where to send alerts, not when to send them.

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

✓

An alerting policy with a condition based on the endpoint's latency metric.

Cloud Monitoring alerting policies are used to define conditions that trigger alerts. To alert on prediction latency, you create an alerting policy with a condition based on the endpoint's latency metric and attach a notification channel to it. Dashboards and notification channels do not define conditions.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
A notification channel that sends emails to the on-call team.
Why it's wrong here
A notification channel specifies where to send alerts (e.g., email, SMS), but it does not define the condition that triggers the alert. You need both an alerting policy (to define the condition) and a notification channel (to define the recipient). The question asks for the resource to define the condition, so the notification channel alone is insufficient.
✓
An alerting policy with a condition based on the endpoint's latency metric.
Why this is correct
Cloud Monitoring alerting policies define conditions that trigger alerts. To alert on prediction latency, you create an alerting policy with a condition that monitors the endpoint's latency metric (e.g., aiplatform.googleapis.com/prediction/latencies). This is the correct resource to define the threshold and trigger notifications.
✗
A dashboard that displays the endpoint's latency over time.
Why it's wrong here
Dashboards are for visualization and do not trigger alerts. They can help you monitor trends manually, but they do not send notifications. The requirement is to receive an alert when latency exceeds a threshold, which requires an alerting policy, not a dashboard.
✗
A log-based metric that counts the number of high-latency requests.
Why it's wrong here
Log-based metrics can be used to create metrics from logs, but they are not the primary resource for defining alert conditions on existing metrics. While you could create a log-based metric for latency if logs contain that information, the endpoint's latency is already available as a built-in metric, making an alerting policy on that metric more direct and efficient.

Go deeper

PMLE Monitoring ML Solutions Practice Question

Why each option matters

Option-by-option breakdown

Related to this question