Databricks-ML-Assoc Model Deployment Practice Question
A machine learning engineer is preparing to deploy a scikit-learn model as a Databricks Model Serving endpoint. The model expects a pandas DataFrame with specific column names and types. Which two actions should the engineer take to ensure the endpoint correctly validates and processes inference requests? (Choose two.)
⚠ Common exam trap
The trap here is assuming that endpoint sizing or inference tables can fix schema mismatches, when schema enforcement comes from the logged MLflow 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
✓
Provide a representative `input_example` when logging the model so MLflow can infer and store the input schema.
Logging the model with an explicit `ModelSignature` and a representative `input_example` ensures the Serving endpoint knows the exact column names and types to expect. The signature drives request validation, while the input example documents and helps infer the schema. Together they prevent malformed requests from reaching the model and keep training and serving schemas aligned.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Convert the model to the `mlflow.pyfunc` flavor using a custom wrapper that hardcodes the expected column order.
Why it's wrong here
Hardcoding column order in a custom wrapper is fragile and does not replace a proper signature. The `pyfunc` flavor is useful for custom logic, but the requirement here is schema validation at the endpoint. A wrapper that hardcodes order can break if upstream data changes and does not provide the type enforcement the endpoint needs. The signature is the correct mechanism for capturing schema expectations.
- ✗
Set the endpoint's `workload_size` to Large so the endpoint can coerce incoming data types automatically.
Why it's wrong here
`workload_size` determines the compute resources allocated to the endpoint, affecting throughput and concurrency, not schema handling. A larger workload does not enable automatic type coercion or validate column names. Relying on workload size to fix schema issues misattributes a configuration concern to capacity. Schema correctness must be addressed in the MLflow model artifact, not in endpoint sizing.
- ✓
Provide a representative `input_example` when logging the model so MLflow can infer and store the input schema.
Why this is correct
Passing an `input_example` gives MLflow a concrete sample to infer the input schema and store it alongside the model. Even when an explicit signature is used, an input example helps document expected payloads and can be used to validate the schema. Together with the signature, it ensures the endpoint knows the correct column names and types. This is a recommended practice for reliable deployments.
- ✓
Log the model with an explicit `ModelSignature` that captures the training DataFrame's column names and data types.
Why this is correct
An explicit `ModelSignature` records the expected input columns and types in the MLflow model artifact. Databricks Model Serving uses this signature to build the request schema and validate incoming payloads, rejecting mismatched columns or types early. Without it, the endpoint may accept malformed requests and fail at inference time. Providing the signature during logging ensures consistent behavior between training and serving.
- ✗
Enable inference tables on the endpoint so that incoming requests are automatically reformatted to match the model's schema.
Why it's wrong here
Inference tables capture request and response payloads for logging and monitoring. They do not transform or reformat incoming data to fit the model's schema. Enabling them is valuable for observability and debugging, but they operate after the request has been accepted. This option confuses a monitoring feature with schema enforcement, which is handled by the MLflow signature at request validation time.
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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.