Quick Answer
The answer is to use the `aiplatform.start_run()` context manager. This is correct because Vertex AI Experiments is designed to automatically log hyperparameters when they are passed as key-value arguments within this context manager, which captures and records them directly into the experiment run metadata without requiring any manual logging code. On the Google Professional Machine Learning Engineer exam, this tests your understanding of how the Vertex AI SDK streamlines experiment tracking, often appearing as a scenario where a team wants to compare different hyperparameters efficiently. A common trap is thinking you need to manually call `log_params()` or use a separate tracking library, but the native context manager handles auto-logging seamlessly. Memory tip: think of `start_run()` as a smart container that automatically snapshots whatever hyperparameters you feed it, so you never have to remember to log them yourself.
This PMLE practice question tests your understanding of collaborating to manage data and models. 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 team is using Vertex AI Experiments to compare different hyperparameters. They want to automatically record the hyperparameters. What is the correct way?
Manually log to console
Why wrong: Console logs are not persisted in Experiments.
Use the `aiplatform.start_run()` context manager
This context manager automatically logs hyperparameters and metrics to Vertex AI Experiments.
Write to a CSV file
Why wrong: CSV is not integrated with Experiments.
Use BigQuery
Why wrong: BigQuery is not an automatic logging mechanism for Experiments.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use the `aiplatform.start_run()` context manager
Option B is correct because Vertex AI Experiments provides a native `aiplatform.start_run()` context manager that automatically captures hyperparameters passed as key-value arguments, logging them to the experiment run metadata without manual intervention. This integrates directly with the Vertex AI SDK, ensuring consistency and traceability across runs.
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.
Manually log to console
Why it's wrong here
Console logs are not persisted in Experiments.
Use the `aiplatform.start_run()` context manager
Why this is correct
This context manager automatically logs hyperparameters and metrics to Vertex AI Experiments.
Related concept
Read the scenario before looking for a memorised answer.
Write to a CSV file
Why it's wrong here
CSV is not integrated with Experiments.
Use BigQuery
Why it's wrong here
BigQuery is not an automatic logging mechanism for Experiments.
Common exam traps
Google Cloud often tests the misconception that any logging method (console, CSV, BigQuery) is equivalent to native SDK integration, but the key requirement is automatic, structured recording tied to the experiment run, which only the SDK's context manager provides.
Detailed technical explanation
Under the hood, `aiplatform.start_run()` creates a Run resource in Vertex AI Experiments, and any parameters passed via `run.log_params()` or as keyword arguments are serialized as key-value pairs in the run's metadata. This enables automatic comparison across runs using the Vertex AI Experiments UI or SDK, where hyperparameters are indexed for filtering and analysis. A subtle behavior is that the context manager automatically ends the run on exit, preventing orphaned runs, which is critical for maintaining clean experiment logs in collaborative environments.
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
Collaborating to manage data and models — This question tests Collaborating to manage data and models — Read the scenario before looking for a memorised answer..
The correct answer is: Use the `aiplatform.start_run()` context manager — Option B is correct because Vertex AI Experiments provides a native `aiplatform.start_run()` context manager that automatically captures hyperparameters passed as key-value arguments, logging them to the experiment run metadata without manual intervention. This integrates directly with the Vertex AI SDK, ensuring consistency and traceability across runs.
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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Last reviewed: Jun 30, 2026
This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.
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