Databricks-ML-Pro ML Ops Practice Question
A data science team uses MLflow Tracking with a remote tracking server backed by a Databricks-hosted MySQL instance for the backend store and an Azure Data Lake Storage Gen2 path for artifacts. A model-training notebook writes metrics and a model artifact, then calls mlflow.register_model to promote the run into Unity Catalog. Reviewers report that the run's metrics appear in the experiment UI, but the model version in the registry cannot be loaded by the deployment job. The deployment job fails when it tries to download the artifact. Which action most directly resolves the deployment failure?
⚠ Common exam trap
The trap here is assuming that because metrics and run metadata are visible in the UI, the artifacts are equally accessible to every consumer of the run.
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
✓
Grant the deployment job's service principal READ privileges on the ADLS Gen2 artifact location, or configure a storage credential and external location that the deployment job can access.
MLflow separates metadata from artifacts: the backend store holds run and metric records, while the artifact store holds the model files. A deployment job that can read experiment metadata still needs its own credentials to the artifact location. Aligning the service principal's access, or exposing the path through Unity Catalog storage credentials and external locations, is what lets the artifact download succeed.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Re-run the training notebook with mlflow.set_tracking_uri pointing at a local file path so the artifact is written to the driver's disk.
Why it's wrong here
Writing artifacts to the driver's local disk makes them unavailable to any other cluster or job. The deployment job runs separately and cannot read a path that exists only on the training driver. This changes where artifacts live without fixing cross-principal access, and it breaks the reproducibility the remote tracking server was meant to provide.
- ✓
Grant the deployment job's service principal READ privileges on the ADLS Gen2 artifact location, or configure a storage credential and external location that the deployment job can access.
Why this is correct
Artifacts are stored at the artifact location configured on the tracking server, not inside the backend database. When the deployment job lacks permission to that ADLS path, it cannot download the model, even though metadata and metrics are visible. Aligning the service principal's access or wiring a Unity Catalog storage credential and external location resolves the download failure.
- ✗
Register the model again using the run ID instead of the artifact URI so the registry stores the artifact inline in the backend database.
Why it's wrong here
The Model Registry never stores model binaries inside the backend store; it stores metadata and references to the artifact location. Re-registering with a run ID does not move or copy the artifact bytes, so the deployment job still needs read access to the same storage path. The permission gap remains the actual blocker.
- ✗
Increase the --model-serve-timeout on the deployment cluster so the job has more time to fetch the artifact from the tracking server.
Why it's wrong here
Timeouts affect how long a serving process waits for model initialization, but they do not grant permission to an inaccessible object store. The failure is an authorization problem on the artifact path, not a duration problem, so raising a timeout merely delays the same error. It leaves the underlying storage access misconfiguration untouched.
Visual reference
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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-Pro 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-Pro exam.