Databricks-ML-Assoc ML Workflows Practice Question
An ML engineer has a scikit-learn model trained locally and wants to log it to MLflow with a signature so that Databricks Model Serving can enforce input schema validation. The engineer calls mlflow.sklearn.log_model(model, 'model') but does not use infer_signature. What is the most accurate consequence when the model is later served on Databricks Model Serving?
⚠ Common exam trap
The trap here is assuming that Databricks Model Serving can infer or enforce a schema from live traffic even when no signature was logged with the model.
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 model will be served, but the endpoint cannot validate incoming request schemas against a stored signature, so malformed inputs may reach the model.
Logging a model without infer_signature leaves the MLflow model metadata without an input schema. Databricks Model Serving uses that stored signature to validate request payloads, so the endpoint will accept requests but cannot reject malformed ones. To gain schema enforcement, the engineer should call infer_signature on a sample DataFrame and pass it to log_model.
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 will be served with a default signature that expects a single double column named 'features'.
Why it's wrong here
Databricks Model Serving does not fabricate a default signature. If no signature is logged, no signature exists in the model metadata. Assuming a generic 'features' column would be incorrect and could break real inference. The engineer must explicitly infer and log the signature to get schema enforcement.
- ✗
Databricks Model Serving will automatically infer the signature from the first request payload and cache it for later validation.
Why it's wrong here
Model Serving does not learn a signature from live traffic; the signature must be captured at log time from a sample input. Without it, the endpoint cannot perform schema validation on incoming requests, and the first request will not establish a permanent signature. This option misstates how MLflow signatures are persisted.
- ✗
Databricks Model Serving will reject the model version because a signature is mandatory for all Unity Catalog registered models.
Why it's wrong here
A signature is recommended but not mandatory for registering a model in Unity Catalog. Model Serving can load a model artifact without a signature; it simply cannot enforce input schema checks. Rejecting the version would be overly strict and does not reflect the actual registration or serving behaviour in Databricks.
- ✓
The model will be served, but the endpoint cannot validate incoming request schemas against a stored signature, so malformed inputs may reach the model.
Why this is correct
MLflow signatures are recorded only when log_model is called with a signature argument or infer_signature. Without it, the logged model has no stored input schema, so Databricks Model Serving cannot enforce request validation and will pass payloads directly to the model, where unexpected columns or types may cause runtime errors.
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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.