Databricks-ML-Assoc Model Development Practice Question
A machine learning engineer is using MLflow to log a model built with XGBoost. They want to ensure that the model's input schema is captured for validation during deployment. Which MLflow feature should they use?
⚠ Common exam trap
The trap here is assuming that an input example provides schema validation, when it only offers a sample for testing without enforcing structure.
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
✓
Model signature
The model signature is the MLflow feature that captures the input and output schema of a model. It is logged alongside the model and is used by deployment tools to validate incoming data. For XGBoost, you can infer the signature from training data or define it manually. This ensures that the model receives data in the expected format, reducing runtime errors.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Model signature
Why this is correct
The model signature defines the expected input and output schema of a model. It is logged with the model and used for validation during deployment. For XGBoost models, you can specify the signature using mlflow.models.infer_signature or manually. This ensures that the deployed model receives data in the correct format and helps catch schema mismatches early.
- ✗
Model version
Why it's wrong here
Model version is an identifier for a specific iteration of a registered model. It does not capture input schema. Versioning helps track changes but does not provide validation of input data. The schema is captured separately through the model signature, which is logged with the model artifact.
- ✗
Run ID
Why it's wrong here
Run ID is a unique identifier for an MLflow run. It is used to reference the run and its artifacts, but it does not contain schema information. While the run may have a signature logged as part of the model, the run ID itself is not a feature for capturing input schema. The signature is the appropriate feature.
- ✗
Input example
Why it's wrong here
An input example is a sample of input data that can be logged with the model to illustrate usage, but it does not enforce a schema. It is used for documentation and testing, not for validation. While it can be part of the model metadata, it does not provide the schema validation that a model signature does.
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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.