Databricks-ML-Assoc Model Development Practice Question
Exhibit
log_model_config = {
"artifact_path": "model",
"signature": infer_signature(X_train, y_pred),
"input_example": X_train.iloc[:5]
}
mlflow.sklearn.log_model(model, **log_model_config)Refer to the exhibit. Why is including the `signature` and `input_example` in the `log_model` call considered a professional best practice?
⚠ Common exam trap
Candidates often view signatures as optional metadata, failing to realize they provide the essential interface documentation and validation required for seamless downstream integration by other services.
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
✓
It enables schema validation and improves model serving usability.
Providing a signature and input example allows MLflow to define the expected schema for the model. This metadata facilitates automatic type validation and allows the model serving infrastructure to generate accurate API documentation. This is critical for downstream consumers who need to integrate the model, as it prevents runtime integration errors and clarifies the interface expectations before the model is even deployed into 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.
- ✗
It forces the model to run on a GPU during inference.
Why it's wrong here
Model signatures and input examples define data types and structures, not execution hardware. The infrastructure choice for inference is independent of the model's metadata; the model remains framework-specific, and the platform determines the hardware resources allocated for running that model based on the service's configuration.
- ✗
It speeds up the model training process.
Why it's wrong here
Logging a signature and input example happens after the training is complete, during the model saving phase. It has no effect on the time taken to train the model, as these steps do not involve any iterative computation or data processing that would affect training convergence or speed.
- ✓
It enables schema validation and improves model serving usability.
Why this is correct
Signatures provide a clear contract for the model, enabling automatic validation of input data. Input examples help serving tools generate accurate API definitions, making it easier for external applications to call the model. This reduces integration friction and ensures that only valid data is passed to the model.
- ✗
It reduces the storage size of the model artifact.
Why it's wrong here
Including a signature and input example adds minimal metadata to the saved artifact, slightly increasing the size rather than reducing it. The primary benefit is usability and robustness in production deployment, not storage optimization. The impact on disk space is negligible compared to the model's weights and configuration files.
About these practice questions
This Databricks-ML-Assoc question is part of Courseiva's 319-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.