Databricks-ML-Assoc Model Development Practice Question
A machine learning engineer is using MLflow to log a scikit-learn model. They call mlflow.sklearn.log_model(model, "model") without specifying a signature. When the model is later loaded for batch inference, the engineer observes that the model's predict method works correctly, but they cannot determine the expected input schema from the logged model. Which MLflow component is missing and would have provided this information?
⚠ Common exam trap
Many exam-takers confuse an input example with a model signature; an example is a sample, not a formal schema definition.
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 component that explicitly defines the input and output schema of a logged model. Without it, MLflow does not store metadata about column names, data types, or tensor shapes, which complicates downstream validation and deployment. Logging a signature ensures that consumers of the model understand its expected interface, reducing integration 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.
- ✗
Run ID
Why it's wrong here
The run ID is a unique identifier for an MLflow run that groups logged parameters, metrics, and artifacts. It does not describe the model's input or output schema. While the run ID is necessary for retrieving artifacts, it does not provide the missing schema information that the engineer needs.
- ✓
Model signature
Why this is correct
The model signature captures the expected input and output schema of the model, including column names, data types, and shapes. Without it, MLflow does not store this metadata, making it difficult to validate inputs or understand the model's interface. Logging a signature via mlflow.models.infer_signature or manually specifying it would provide the missing schema information.
- ✗
Model version
Why it's wrong here
The model version is an integer assigned by the Model Registry when a model is registered. It tracks iterations but does not contain any information about the input schema. The absence of a version would not affect the ability to determine the expected input format; versioning is unrelated to schema metadata.
- ✗
Input example
Why it's wrong here
An input example is a sample of valid input data that MLflow can log alongside the model. While useful for documentation and testing, it does not define the full expected schema with types and shapes. The engineer could infer some information from an example, but it is not the formal schema definition that a signature provides.
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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.