Databricks-ML-Pro Model Deployment Practice Question
A financial institution deploys a credit scoring model using Databricks Model Serving. The model must log all incoming requests and outgoing responses to a Delta table for auditing. The ML engineer needs to enable this logging with minimal performance impact. Which solution should they implement?
⚠ Common exam trap
The trap here is assuming that access logs contain full payloads or that custom logging is necessary, when Databricks provides a native asynchronous logging feature.
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
✓
Enable inference logging on the serving endpoint, specifying a Delta table location for logs.
Databricks Model Serving includes a built-in inference logging feature that asynchronously logs request and response data to a Delta table. This is the most efficient and least intrusive method. Custom wrappers or external systems add latency and complexity. Access logs do not contain payloads. Therefore, enabling inference logging directly is the correct solution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable inference logging on the serving endpoint, specifying a Delta table location for logs.
Why this is correct
Databricks Model Serving provides built-in inference logging that captures request and response payloads and writes them to a Delta table. This feature is designed for auditing and monitoring, and it operates asynchronously to minimize impact on inference latency. Configuring the endpoint with a Delta table path enables this logging.
- ✗
Implement a custom logging wrapper in the model's predict method that writes each request to a Delta table.
Why it's wrong here
Adding synchronous logging within the predict method would increase inference latency significantly, as each request would wait for the write to complete. This approach also complicates the model code and is not recommended for high-throughput scenarios. It does not leverage the built-in asynchronous logging.
- ✗
Use a Databricks job to periodically query the endpoint's access logs and write them to a Delta table.
Why it's wrong here
Endpoint access logs contain metadata about requests but not the full request and response payloads. They are also not stored in a queryable format for this purpose. This method would not capture the required payloads and would introduce delays.
- ✗
Configure the model to send logs to an external Kafka topic, then use a Databricks job to ingest from Kafka into Delta Lake.
Why it's wrong here
This introduces unnecessary complexity and additional infrastructure. Databricks Model Serving already provides native integration with Delta Lake for inference logging. Using Kafka would add latency and require additional maintenance, and it is not the recommended approach for this use case.
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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-Pro 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-Pro exam.