Databricks-ML-Assoc Databricks Machine Learning Practice Question
Exhibit
2023-10-12 14:22:10,115 ERROR mlflow.pyfunc: Encountered an unexpected error while evaluating model signature. Expected input column 'user_age' of type long, but received type double.
Refer to the exhibit. A machine learning engineer deployed an MLflow model to Databricks Model Serving, but inference requests are failing with the error shown in the exhibit. How should the engineer resolve this issue?
⚠ Common exam trap
Users often try to fix type mismatch errors by changing the model code itself, rather than adjusting the inference payload to match the strict schema enforced by the model 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
✓
Update the inference client payload to explicitly cast the 'user_age' field to an integer or long type before sending the request.
Databricks Model Serving strict signature validation enforces data types defined during model logging. When incoming payloads contain mismatched types, such as double instead of long, requests fail. The engineer must re-log the model with a correct signature or cast incoming payload data types to match the expected schema.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the timeout threshold parameter in the Databricks Model Serving endpoint configuration settings.
Why it's wrong here
Increasing timeout thresholds only helps when inference requests take too long to compute. The error shown in the exhibit is a strict type validation failure during schema checking, which is unrelated to execution latency or network timeout limits.
- ✓
Update the inference client payload to explicitly cast the 'user_age' field to an integer or long type before sending the request.
Why this is correct
Model serving validates input data against the logged MLflow model signature. Ensuring the client payload casts 'user_age' to the expected long type resolves the schema mismatch and allows the serving container to successfully process the request.
- ✗
Restart the underlying model serving worker nodes to clear stale type caches in the serving runtime container.
Why it's wrong here
Model type expectations are hardcoded into the MLflow model signature artifact and conda environment. Restarting worker nodes will not alter the strict schema validation rules established when the model was originally logged.
- ✗
Switch the serving endpoint scaling policy from automatic to manual provisioning to bypass schema validation checks.
Why it's wrong here
Scaling policies govern compute resource allocation and autoscaling behavior. They have no influence over MLflow model signature enforcement or input payload data type validation logic within the serving endpoint runtime.
Visual reference
About these practice questions
One of 319 original Databricks-ML-Assoc practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
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.