Databricks-ML-Assoc ML Workflows Practice Question
Exhibit
log_model(model=my_model, artifact_path='model', signature=model_signature)
Refer to the exhibit. Why is the 'signature' parameter included in the log_model call?
⚠ Common exam trap
Candidates often confuse 'signature' with model versioning or metadata logging, failing to realize its primary function is runtime schema enforcement to prevent type mismatches during inference requests.
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
✓
To enforce input data schema validation during model inference.
The model signature defines the expected schema for inputs and outputs. Providing a signature allows Databricks to perform runtime validation of data types, ensuring that the model receives data in the format it expects. This prevents runtime errors during inference and allows for automated documentation and improved user experience within the MLflow Model Registry, which is crucial for scalable model deployment in production.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
To encrypt the model artifacts before saving them to DBFS.
Why it's wrong here
Signatures define schema, not security. Encryption is handled at the storage level by the cloud provider or Databricks platform settings. The signature parameter has no relationship with data at rest encryption or securing the model binary files from unauthorized access.
- ✓
To enforce input data schema validation during model inference.
Why this is correct
Signatures provide a contract between the model and the input data. By logging this schema, the model can automatically check if incoming request payloads match the expected feature names and types. This prevents type-mismatch errors that typically crash inference services when bad data is sent.
- ✗
To specify the hyperparameters used to train the model.
Why it's wrong here
Hyperparameters are logged via the 'log_params' or 'log_param' functions, not the model signature. The signature is strictly for the input and output data structures, whereas parameters describe the training configuration and algorithmic settings used during the model construction phase.
- ✗
To compress the model into a smaller binary file for faster deployment.
Why it's wrong here
Signatures do not affect the size of the model binary. Compression is usually determined by the serialization format (e.g., Pickle, ONNX, or Joblib). Adding a signature adds a small JSON file to the artifact directory that describes the data structure but does not change the model.
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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.