Databricks-ML-Assoc ML Workflows Practice Question
A data scientist finishes a training notebook and wants to capture the source code revision and the Git repository URL on the MLflow run so reviewers can reproduce the exact code state. The repository is already connected to the Databricks workspace through Git integration. Which mechanism records this information automatically?
⚠ Common exam trap
The trap here is assuming autologging captures Git provenance, when Git source tags come from running the notebook inside a connected Git folder.
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 Git-based source tracking, which stores the repository URL and commit hash as run tags when the notebook runs from a Git folder.
Databricks integrates Git folders with MLflow so that runs executed from a connected repository automatically receive source tags including the repository URL and commit hash. This gives reviewers a precise revision to check out for reproduction. Manual tagging is not automatic, autologging targets framework artifacts rather than Git metadata, and model registration does not populate run source tags.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enabling autologging, which always injects Git metadata regardless of where the notebook is executed.
Why it's wrong here
Autologging captures framework-specific parameters, metrics, and models, and it does not by itself inject Git repository metadata. Git source tags depend on the notebook running from a connected Git folder, not on autologging being enabled. Relying on autologging alone would leave the run without the repository URL and commit information reviewers need.
- ✓
MLflow Git-based source tracking, which stores the repository URL and commit hash as run tags when the notebook runs from a Git folder.
Why this is correct
When a notebook executes from a Databricks Git folder, MLflow records the Git repository URL and commit hash as run tags such as mlflow.source.git.repoURL and mlflow.source.git.commit. Reviewers can then check out that exact revision to reproduce the run. No manual logging call is needed, which is why this is the correct mechanism for the described requirement.
- ✗
Manually calling mlflow.set_tag() with a hand-copied commit hash after each training run.
Why it's wrong here
Manual tagging can record a commit hash, but it is error-prone and easy to forget or mis-copy, and it is not automatic. The scenario asks for automatic capture of repository URL and revision, and hand-entered tags also omit the repository URL unless the scientist adds it separately. This is a fallback at best, not the mechanism Databricks provides.
- ✗
Registering the model in Unity Catalog, which copies the workspace Git configuration into model version metadata.
Why it's wrong here
Model registration records the model artifact, signature, and lineage to its run, but it does not copy workspace Git configuration into version metadata. Registration happens after training and does not retroactively add source tags to the run. The requirement is about run-level code provenance, which registration does not supply.
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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
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