Databricks-ML-Assoc Model Deployment Practice Question
A data scientist registers a model in the Databricks Model Registry and wants to record the model's intended input and output schema so that a serving endpoint can validate incoming requests. Which action accomplishes this when logging the model?
⚠ Common exam trap
The trap here is assuming registry stages or endpoint tags carry schema information, when only the logged MLflow model signature drives request 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
✓
Log the model with an inferred or explicit signature using MLflow
A model signature is part of the logged MLflow model artifact and is what the serving runtime uses to validate request payloads against expected input and output types. Registry stages, endpoint tags, and external documentation are metadata that the runtime does not interpret as a schema, so only logging the model with a signature achieves request validation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add the schema to the endpoint's tags in the serving configuration
Why it's wrong here
Endpoint tags are arbitrary metadata for organization and search; they are not parsed as a schema and do not drive request validation. Putting schema details in tags has no effect on how the endpoint interprets payloads, so this approach fails to achieve the intended validation behavior.
- ✗
Store the schema as a separate Delta table and reference it in the model's README
Why it's wrong here
Documentation and external tables are not consumed by the serving runtime. The endpoint validates requests against the signature embedded in the logged MLflow model, not against a Delta table or README. This method leaves requests unvalidated and is not how the feature operates.
- ✓
Log the model with an inferred or explicit signature using MLflow
Why this is correct
Logging the model with a signature records the expected input and output schema in the MLflow model artifact. Model Serving reads that signature to validate request payloads, so this is the correct way to enable schema validation and prevent malformed inputs from reaching the model.
- ✗
Set the model version's stage to Production in the Model Registry
Why it's wrong here
Stage assignment is a lifecycle label and does not encode any schema information. It cannot tell the endpoint what input columns or types to expect, so it does nothing to enable request validation. Relying on stages here confuses registry governance with artifact metadata.
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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.