Databricks-ML-Assoc Model Deployment Practice Question
Exhibit
{
"model_name": "fraud_detection",
"version": 10,
"stage": "Staging",
"serving_endpoint": {
"name": "fraud-api",
"auto_capture_request_payload": true
}
}Refer to the exhibit. What is the impact of setting 'auto_capture_request_payload' to true?
⚠ Common exam trap
Candidates often assume this setting automatically triggers model retraining or performance alerts. It only logs data; it does not perform automated analytical processing or model updates on the captured payload.
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
✓
It stores incoming request data in a table for analysis and monitoring.
Enabling 'auto_capture_request_payload' is a powerful feature for debugging, auditing, and continuous model improvement. It allows teams to log the exact data received by the endpoint, enabling them to analyze prediction inputs, perform retrospective bias analysis, and verify data quality. This is a critical component of a robust MLOps strategy, as it provides the raw data needed to identify the causes of poor predictions and justify model decisions for compliance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It automatically retrains the model whenever new data is captured.
Why it's wrong here
Capturing data is a logging operation and does not trigger retraining. Automated retraining requires a separate pipeline, such as a Databricks Job, which monitors the captured data and initiates the training process based on predefined criteria, ensuring that the model remains aligned with current data patterns over time.
- ✓
It stores incoming request data in a table for analysis and monitoring.
Why this is correct
This feature facilitates the collection of inference inputs and outputs into a structured format, such as a Delta table. This allows data scientists to monitor the model's performance in production, conduct drift analysis, and use the data for future fine-tuning or retraining efforts, which is vital for maintaining model accuracy.
- ✗
It limits the endpoint to only accept JSON payloads.
Why it's wrong here
The payload capture setting is independent of the input format. The endpoint accepts the formats defined by the MLflow model interface. Enabling payload capture simply instructs the serving infrastructure to log whatever data is received, it does not enforce any specific restrictions on the data format itself.
- ✗
It encrypts the payload before it reaches the model.
Why it's wrong here
Payload capture is a logging and visibility feature, not a security encryption layer. While Databricks secures data in transit and at rest, this setting specifically relates to data logging for observability and model maintenance, rather than the encryption of communication channels between the client and the endpoint.
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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.