Databricks-ML-Pro ML Ops Practice Question
A data scientist has completed hyperparameter tuning with Hyperopt on Databricks and now needs to register the best-performing model to the MLflow Model Registry, including its signature and input example, so that downstream scoring jobs can validate incoming data. The training script uses MLflow autologging. Which approach most reliably captures the signature and input example during registration?
⚠ Common exam trap
The trap here is assuming that MLflow autologging always captures both a signature and an input example, when in practice the input example must be supplied explicitly.
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
✓
Explicitly call mlflow.log_model() (or the flavor-specific log_model) with the signature and input_example arguments inside the training function, then register that logged model.
The signature and input example are artifacts attached at model logging time, so the reliable path is to pass them explicitly to the flavor-specific log_model call used by the training function. Registering afterward from the run URI, editing tags, or hand-editing the MLmodel file does not produce the complete, tooling-readable artifacts needed for downstream data validation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the MLflow Client's update_model_version() method after registration to attach the signature and input example as tags on the model version.
Why it's wrong here
update_model_version() can change description and tags, but the model signature and input example are stored as part of the logged model artifacts, not as tags. Attempting to attach them this way will not produce a usable signature that serving or scoring tooling can enforce, so the requirement remains unmet.
- ✗
Call mlflow.register_model() directly on the run URI returned by the tuning run, because autologging automatically infers and stores the signature and input example.
Why it's wrong here
Autologging for supported frameworks does infer a signature from the training data, but it does not capture an input example by default, and registering directly from a run URI does not add the example. The scenario explicitly requires both artifacts for downstream validation, so relying on autologging alone leaves the input example missing and the requirement unmet.
- ✓
Explicitly call mlflow.log_model() (or the flavor-specific log_model) with the signature and input_example arguments inside the training function, then register that logged model.
Why this is correct
Passing signature and input_example to a flavor-specific log_model call records both artifacts with the model version. This is the deterministic way to guarantee the signature schema and a representative input example are attached, which downstream scoring jobs can then use to validate incoming payloads before invoking the model.
- ✗
Register the model from the MLflow experiment's artifact location by copying the MLmodel file and manually editing it to include the signature and input example fields.
Why it's wrong here
Manually editing the MLmodel file is fragile and unsupported; the signature and input example are expected as separate artifacts referenced by the MLmodel file. Hand-editing risks an inconsistent registry entry that scoring jobs cannot parse, and it bypasses the API that guarantees correct serialization of the schema and example.
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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.