Databricks-ML-Assoc Databricks Machine Learning Practice Question
A data scientist is using MLflow on Databricks to track experiments. After running several training jobs, they notice that the run metrics are recorded but the model artifact is missing when they view the run details. They logged the model using the default MLflow API. What is the most likely cause?
⚠ Common exam trap
The trap here is assuming that logging a model automatically associates it with the most recent run, when MLflow requires an active run context.
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
✓
The model artifact was logged outside of the active MLflow run context, so it was not associated with the run.
The model artifact is missing because log_model was called outside an active MLflow run, so it was not linked to the run. Metrics were logged within the run, explaining their presence. To ensure artifacts are associated, all logging calls must occur inside a run context created by mlflow.start_run(). This is a common oversight when refactoring code or using helper functions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model was too large to be stored in the MLflow artifact store, so it was automatically skipped.
Why it's wrong here
MLflow does not impose a size limit that would cause silent skipping. Artifacts are stored in the configured artifact location, which can handle large files. If storage limits are exceeded, an error would be raised, not a silent omission. The scenario does not mention any error, so size is unlikely the cause.
- ✗
The model was logged using mlflow.sklearn.log_model, but the run was not ended, so artifacts are not persisted.
Why it's wrong here
Ending a run finalizes it and logs its status, but artifacts are written to the artifact store during the log_model call. If the run is not ended, artifacts may still appear until the run is terminated; however, the missing artifact is more likely due to incorrect artifact path configuration. Additionally, this scenario does not indicate that the run was left open.
- ✗
The MLflow tracking server was not configured to log artifacts, so only metrics were recorded.
Why it's wrong here
MLflow tracking servers log both metrics and artifacts by default. Configuration can specify a different artifact location, but it would not disable artifact logging entirely. If artifact logging were disabled, no artifacts would be stored for any run, but the scenario implies other runs may have artifacts. The issue is specific to this run's model missing.
- ✓
The model artifact was logged outside of the active MLflow run context, so it was not associated with the run.
Why this is correct
MLflow associates artifacts with the active run. If log_model is called without an active run context, the model is saved to the artifact store but not linked to any run. The run metrics appear because they were logged within a run, but the model artifact is missing from that run's details. To fix, ensure logging occurs inside a with mlflow.start_run() block.
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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.