Databricks-ML-Assoc Model Deployment Practice Question
A team deploys a model to a Databricks Model Serving endpoint and enables inference tables. After a week, they notice that the inference table contains request and response payloads but the payload columns are empty for many rows, while status codes are 200. What is the most likely explanation?
⚠ Common exam trap
The trap here is assuming that any inference table row includes payloads, when payload logging is a separate setting and large payloads may be truncated or omitted.
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 inference table's payload logging was disabled or the payload size exceeded the logging limit, so only metadata was recorded.
Inference tables have a payload logging setting that can be enabled or disabled, and payloads above a size threshold are not fully recorded. When status codes are successful but payload columns are empty, the cause is almost always that payload logging is off or the requests exceeded the size limit. Reviewing the endpoint's inference table configuration and the payload sizes resolves the issue.
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 endpoint was serving a different model version than the one with the inference table enabled.
Why it's wrong here
Inference table configuration is attached to the endpoint, not to a specific model version. Changing the served version does not disable payload logging. If the endpoint is writing rows at all, the logging configuration is active; the empty payload columns point to a payload-specific setting, not to a model version mismatch.
- ✗
The model signature was not logged, so Databricks could not map payloads to columns.
Why it's wrong here
The model signature defines input and output schema for the model, not the inference table payload logging. Inference tables log raw request and response payloads independently of the signature. A missing signature would affect request validation and the serving UI, but it would not cause empty payload columns in an otherwise populated inference table.
- ✗
The endpoint's autoscaling scaled to zero, so requests were not logged.
Why it's wrong here
Scale-to-zero affects availability and cold starts, not payload logging. If the endpoint had scaled to zero, requests would either trigger a cold start or fail, and successful responses would still be logged with payloads. Empty payload columns with status 200 indicate a logging configuration issue rather than a scaling event, so this explanation does not fit the observation.
- ✓
The inference table's payload logging was disabled or the payload size exceeded the logging limit, so only metadata was recorded.
Why this is correct
Inference tables can be configured to log metadata only, and payloads that exceed the maximum logged size are truncated or omitted. When status codes are 200 but payload columns are empty, the most likely cause is that payload logging is disabled in the endpoint configuration or that requests exceeded the size threshold, leaving only metadata rows.
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.