Databricks-ML-Assoc Model Deployment Practice Question
A data scientist has registered a scikit-learn model in Unity Catalog and now wants to serve it behind a Databricks Model Serving endpoint. The model's MLflow signature records a pandas DataFrame input with three named columns. The team wants the endpoint to reject malformed requests automatically rather than silently scoring them. Which action should the data scientist take?
⚠ Common exam trap
The trap here is assuming that schema validation requires custom code or inference tables, when Model Serving already enforces the logged MLflow signature automatically.
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
✓
Create the endpoint with the registered model version; Model Serving enforces the logged MLflow signature and schema validation by default.
When a model is logged with an MLflow signature, Databricks Model Serving uses that signature to validate incoming requests. A pandas DataFrame signature with named columns causes the endpoint to check column names and types, rejecting payloads that do not conform. No custom preprocessing or inference table configuration is required, because validation happens at the serving layer before the model is invoked.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable inference tables on the endpoint so malformed records are flagged in the Delta table and excluded from scoring.
Why it's wrong here
Inference tables log request and response payloads for monitoring and auditing, but they do not validate or block bad input. Malformed requests would still reach the model and produce scores; the table simply records what happened after the fact, so it cannot satisfy the requirement to reject malformed requests before scoring.
- ✗
Register the model with a new signature that uses a tensor spec instead of a DataFrame spec so the endpoint validates each element.
Why it's wrong here
Changing to a tensor spec does not add per-element validation; tensor specs describe fixed-shape numeric arrays and are intended for frameworks like TensorFlow or PyTorch. For a scikit-learn model expecting named columns, the pandas DataFrame signature is the correct representation, and altering it would misrepresent the model's real interface.
- ✗
Add a Python preprocessing script that parses the raw JSON and raises an error for unexpected fields, then deploy that script as the endpoint's model.
Why it's wrong here
Writing a custom validator is possible but unnecessary here. It adds maintenance overhead and can drift from the logged signature. Since the model already carries a signature that the platform enforces automatically, implementing bespoke parsing logic duplicates existing behavior and risks rejecting valid requests due to coding mistakes.
- ✓
Create the endpoint with the registered model version; Model Serving enforces the logged MLflow signature and schema validation by default.
Why this is correct
Model Serving reads the MLflow signature stored with the registered model version and uses it to validate incoming payloads against the expected DataFrame schema. Requests whose columns or types do not match are rejected before inference runs, satisfying the requirement without extra configuration.
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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.