Databricks-ML-Assoc Databricks Machine Learning Practice Question
Exhibit
{
"model_signature": {
"inputs": [{"name": "age", "type": "integer"}, {"name": "income", "type": "double"}],
"output": {"type": "double"}
},
"environment": "conda.yaml"
}Refer to the exhibit. A model is failing to deploy to Databricks Model Serving. The error log indicates 'Schema Mismatch'. Based on the JSON signature provided, what is the most likely cause of the deployment failure?
⚠ Common exam trap
Candidates often confuse model deployment issues with infrastructure errors, failing to realize that Schema Mismatch is specifically caused by a discrepancy between the input data types and the signature.
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
✓
The input data types do not match the expected integer/double types.
The error occurs because the input data sent to the serving endpoint does not match the defined signature. The signature strictly enforces types; if the incoming request sends a string instead of an integer for 'age', or if a field is missing, the serving layer rejects the payload. Validating the schema against the expected MLflow signature is critical for preventing runtime inference errors when integrating with external client applications.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model environment is missing.
Why it's wrong here
While the environment is referenced in the signature, the specific error 'Schema Mismatch' points directly to input data validation. If the environment were missing, the error would likely be related to container initialization or dependency resolution rather than a discrepancy between input data and the expected model signature.
- ✓
The input data types do not match the expected integer/double types.
Why this is correct
Databricks Model Serving validates incoming requests against the MLflow model signature. If the 'age' field is provided as a string or if the 'income' field is missing, the request fails immediately. This protection mechanism ensures that the model only receives inputs that conform to the format used during training.
- ✗
The output type is too large to process.
Why it's wrong here
The output type definition in the signature is for validation of the model's return value, not for restricting the size of the result. Schema mismatch errors are triggered by input data issues, not by the size or precision of the double-typed numerical result returned by the model after inference.
- ✗
The model version is not registered.
Why it's wrong here
If a model version was not registered, the deployment would fail to locate the model artifacts entirely. The system would raise an error related to model URI resolution, not a schema mismatch. Schema mismatch specifically indicates that the model was found, but the input data is incompatible with it.
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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.