Databricks-GenAI-Assoc Application Development Practice Question
Exhibit
{
"model_signature": {
"inputs": [{"name": "prompt", "type": "string"}],
"outputs": [{"name": "response", "type": "string"}]
}
}Refer to the exhibit. A developer is registering a model. Why is the model signature, as shown in the exhibit, considered a best practice for model registration?
⚠ Common exam trap
Candidates often assume model signatures are primarily used for logging metrics or tracking lineage, overlooking their critical runtime role in automated input validation.
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 provides automated input validation to improve service reliability.
Model signatures define the schema for inputs and outputs, allowing the serving layer to perform automated validation. When an incoming request doesn't match the signature, the serving infrastructure can reject it immediately, providing useful error messages. This prevents invalid data from reaching the model, reducing runtime errors and debugging time, and ensures that the application behaves predictably in production environments, which is critical for maintaining high service quality and reliability.
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 enables automatic data masking for sensitive strings.
Why it's wrong here
Model signatures do not perform data masking. Data masking is a security feature applied via Unity Catalog policies at the data access layer. Confusing signatures with security policies is a common error; signatures exist solely to define and validate expected API traffic structures, not to enforce data privacy rules.
- ✓
It provides automated input validation to improve service reliability.
Why this is correct
By defining the input schema, the model signature allows the serving infrastructure to validate every incoming request against the expected format. If the request is malformed, the system rejects it, preventing potential failures inside the model logic and ensuring that the serving endpoint maintains its stability under various load conditions.
- ✗
It automatically scales the number of serving nodes based on the prompt size.
Why it's wrong here
Scaling is determined by the endpoint compute configuration and traffic metrics, not by the model signature. The signature is a static definition of the API interface; it provides no information or control mechanisms to the auto-scaler regarding how to allocate infrastructure resources based on input length or request complexity.
- ✗
It forces the model to use a specific version of Python for execution.
Why it's wrong here
Python versioning is controlled by the Databricks Runtime and the environment definition, not by the model signature. The signature is solely concerned with the data schema. Including it does not dictate the execution environment or runtime dependencies, which are managed separately through the model's conda or requirements files.
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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-GenAI-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-GenAI-Assoc exam.