Quick Answer
The answer is to use the OpenCensus or OpenTelemetry SDK. These open-source frameworks are the correct method because they allow your custom container to instrument application code directly and export custom metrics to Cloud Monitoring via the Cloud Monitoring API, eliminating the need for sidecar agents or log-based parsing. On the Google Professional Machine Learning Engineer exam, this question tests your understanding of Vertex AI’s native integration with observability standards, often appearing as a scenario where you must choose between agent-based solutions (like the Ops Agent) and direct SDK instrumentation—a common trap is selecting a log-based workaround, which is less efficient and not recommended for real-time metrics. The key insight is that Vertex AI Prediction containers support these SDKs out of the box, making them the most streamlined path. Memory tip: think “SDK, not sidecar” to recall that custom metrics flow directly from your code, not from an external agent.
This PMLE practice question tests your understanding of monitoring ml solutions. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company uses a custom container on Vertex AI Prediction. They want to send custom metrics from their prediction container to Cloud Monitoring. Which method should they use?
OpenCensus or OpenTelemetry SDK
Vertex AI Prediction integrates with OpenTelemetry for custom metrics.
Vertex AI built-in metrics
Why wrong: Built-in metrics do not support custom definitions.
Stackdriver Monitoring agent installed in the container
Why wrong: The monitoring agent is not recommended inside Vertex AI containers.
Cloud Logging log-based metrics
Why wrong: Log-based metrics are derived from logs, not direct metric emission.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
OpenCensus or OpenTelemetry SDK
Option A is correct because OpenCensus and OpenTelemetry are the recommended open-source frameworks for exporting custom metrics from custom containers on Vertex AI Prediction to Cloud Monitoring. They provide a standardized way to instrument your application code, collect metrics, and send them directly to Cloud Monitoring via the Cloud Monitoring API, without requiring additional agents or log-based workarounds.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
OpenCensus or OpenTelemetry SDK
Why this is correct
Vertex AI Prediction integrates with OpenTelemetry for custom metrics.
Related concept
Read the scenario before looking for a memorised answer.
Vertex AI built-in metrics
Why it's wrong here
Built-in metrics do not support custom definitions.
Stackdriver Monitoring agent installed in the container
Why it's wrong here
The monitoring agent is not recommended inside Vertex AI containers.
Cloud Logging log-based metrics
Why it's wrong here
Log-based metrics are derived from logs, not direct metric emission.
Common exam traps
The trap here is that candidates often confuse built-in Vertex AI metrics (which are automatic but limited) with the need for custom metrics, or they incorrectly assume that log-based metrics are the simplest path, when in fact OpenCensus/OpenTelemetry are the direct and recommended method for custom containers.
Detailed technical explanation
OpenCensus and OpenTelemetry use exporters (e.g., the Stackdriver exporter for OpenCensus or the Google Cloud Monitoring exporter for OpenTelemetry) to push metrics directly to Cloud Monitoring via gRPC or HTTP, leveraging the Cloud Monitoring API. In a custom container on Vertex AI, you typically run a sidecar or embed the SDK in your prediction server (e.g., using the OpenTelemetry Python SDK with the `opentelemetry-exporter-gcp-monitoring` package) to export metrics like prediction latency or error counts. A real-world scenario is a fraud detection model that needs to emit custom metrics for false positive rate per model version, which cannot be captured by built-in Vertex AI metrics.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Monitoring ML solutions — This question tests Monitoring ML solutions — Read the scenario before looking for a memorised answer..
The correct answer is: OpenCensus or OpenTelemetry SDK — Option A is correct because OpenCensus and OpenTelemetry are the recommended open-source frameworks for exporting custom metrics from custom containers on Vertex AI Prediction to Cloud Monitoring. They provide a standardized way to instrument your application code, collect metrics, and send them directly to Cloud Monitoring via the Cloud Monitoring API, without requiring additional agents or log-based workarounds.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company uses Vertex AI Predictions with a custom container that invokes an external API for feature enrichment. The prediction response time is highly variable. The engineer wants to monitor the external API's contribution to latency. What should the engineer do?
hardWhy A: Option A is correct because instrumenting the custom container to emit custom metrics (e.g., using OpenTelemetry or a Prometheus client library) allows the engineer to directly measure the time spent in each prediction step, isolating the external API call's contribution to latency. This provides granular, real-time visibility into the specific bottleneck, which is essential when the response time is highly variable and the external API is a known dependency.
Last reviewed: Jun 24, 2026
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