AI-300 ML Model Lifecycle And Operations Practice Question
You are using MLflow to track experiments in Azure Machine Learning. You need to log a custom metric that is calculated every 100 iterations. Which MLflow function should you use?
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
✓
'mlflow.log_metric()'.
The 'mlflow.log_metric' function is used to log key-value pairs of metrics, which can be called within the training loop.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
'mlflow.log_metric()'.
Why this is correct
This is the correct function to log numeric metrics.
- ✗
'mlflow.set_tag()'.
Why it's wrong here
Tags are for metadata, not metrics.
- ✗
'mlflow.log_artifact()'.
Why it's wrong here
Artifacts are for files, not metrics.
- ✗
'mlflow.log_param()'.
Why it's wrong here
Params are for static configuration values.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Microsoft exam blueprint
This AI-300 practice question is part of Courseiva's free Microsoft 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 AI-300 exam.