Databricks-ML-Assoc Model Deployment Practice Question
A team deploys an MLflow pyfunc model to a Databricks Model Serving endpoint. During pre-deployment testing they call the endpoint with a small batch of records and receive an HTTP 400 error stating the request payload does not match the model signature. The model was logged with an inferred signature from a pandas DataFrame. Which action most directly resolves the mismatch?
⚠ Common exam trap
The trap here is treating a signature validation error as a capacity or networking problem instead of a schema mismatch that must be fixed by re-logging 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
✓
Re-log the model with an explicit input_example and signature, then register the new version and update the served entity to that version.
A signature mismatch means the logged model's expected schema differs from the payload the endpoint receives. The reliable fix is to re-log the model with an explicit signature and input example, register the new version, and repoint the served entity. Compute scaling, content-type changes, and inference tables address different concerns and do not alter schema 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.
- ✓
Re-log the model with an explicit input_example and signature, then register the new version and update the served entity to that version.
Why this is correct
Re-logging with an explicit signature and input_example ensures the logged schema matches the payload the endpoint expects, and registering a new version plus updating the served entity points the endpoint at the corrected model. This directly addresses the signature mismatch reported in the 400 error.
- ✗
Enable inference tables on the endpoint so failed requests are logged and the signature is auto-corrected.
Why it's wrong here
Inference tables log requests and responses for observability, not to mutate the model signature. Enabling them would capture the failing request but would not change the schema validation, so the 400 error would persist until the model or payload is corrected.
- ✗
Increase the endpoint's workload size from Small to Large so the request payload can be accepted.
Why it's wrong here
Workload size controls compute capacity, not schema validation. A signature mismatch is a payload-shape problem, so scaling the endpoint would still return the same 400 error because the model would still reject the incoming columns or types.
- ✗
Add a `Content-Type: text/plain` header to the request so the endpoint parses the payload differently.
Why it's wrong here
Databricks Model Serving expects JSON payloads for dataframe-split or records-oriented inputs; switching to text/plain would not make the payload conform to the model signature and would likely produce a different error rather than resolving the mismatch.
Visual reference
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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.