Databricks-ML-Assoc ML Workflows Practice Question
A data scientist wants to track the progress of a training script that runs for several hours on a Databricks cluster. The script uses MLflow and needs to record metrics such as loss and accuracy at the end of each epoch so they can be visualized in real time. Which MLflow API call should be used inside the training loop?
⚠ Common exam trap
Many candidates confuse parameters, artifacts, and tags with metrics; only mlflow.log_metric produces the numeric time-series data that MLflow charts over steps or time.
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
To record metrics like loss and accuracy at each epoch for real-time visualization, the training loop should call mlflow.log_metric. This API accepts a metric name, a numeric value, and an optional step, and MLflow stores each call as a data point. The MLflow UI plots these points as a time-series chart, allowing progress monitoring during long training runs.
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_param
Why it's wrong here
mlflow.log_param records a single parameter value, such as learning rate or batch size, and is intended for configuration settings, not time-series metrics. It cannot capture multiple values over epochs, and the MLflow UI will not plot it as a metric curve. Using it inside the training loop would overwrite or conflict with existing parameters and fail to show progress.
- ✗
mlflow.set_tag
Why it's wrong here
mlflow.set_tag attaches a string key-value pair to the run for categorization and search. Tags are not numeric and are not plotted over time. Using set_tag inside the training loop would only overwrite the tag with the latest value and provide no curve of loss or accuracy, so it cannot satisfy the requirement for real-time metric visualization.
- ✓
mlflow.log_metric
Why this is correct
mlflow.log_metric logs a numeric value for a named metric and can be called repeatedly with the same metric name, optionally specifying a step. MLflow stores each value with its timestamp and step, enabling the UI to render a live-updating chart of loss and accuracy per epoch. This is the standard way to track training progress in real time.
- ✗
mlflow.log_artifact
Why it's wrong here
mlflow.log_artifact uploads a file, such as a plot or a model file, to the run's artifact store. It does not record numeric values that MLflow can chart. While you could write metrics to a file and log it, that approach is indirect and does not provide real-time metric visualization in the MLflow UI, which expects metrics logged via the metrics API.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-ML-Assoc practice question is part of Courseiva's free Databricks 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 Databricks-ML-Assoc exam.