Databricks-ML-Assoc Databricks Machine Learning Practice Question
Which Databricks feature should be used to provide a managed, secure, and scalable endpoint for real-time inference of models logged in the Model Registry?
⚠ Common exam trap
Candidates often confuse workspace notebooks or MLflow tracking servers with deployment endpoints, incorrectly choosing tools used for development instead of production serving.
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
✓
Databricks Model Serving endpoints.
Databricks Model Serving provides a fully managed, low-latency API endpoint for model inference. It abstracts away the infrastructure requirements, enabling horizontal scaling and high availability. It is the standard service for deploying models in production, as it integrates directly with the Model Registry, allowing teams to push production-ready models to endpoints with minimal configuration and secure access controls.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Databricks Jobs running the model in a scheduled notebook.
Why it's wrong here
Jobs are designed for batch processing, not real-time inference. While they can run models on a schedule, they do not provide the HTTP API endpoint required for real-time request-response patterns, leading to high latency and inability to handle concurrent requests efficiently in a production web application context.
- ✓
Databricks Model Serving endpoints.
Why this is correct
Model Serving endpoints are purpose-built for low-latency, real-time model inference. They manage the containerization and infrastructure deployment automatically, ensuring that models are accessible via secure REST APIs. This is the optimal Databricks-native solution for deploying models into production environments that require immediate, scalable prediction capabilities.
- ✗
A standard interactive cluster running a Flask server.
Why it's wrong here
Manually running a web server on a cluster is inefficient and insecure. It requires managing infrastructure, handling scaling, and ensuring the server is always running. Databricks Model Serving replaces this manual effort with a managed service that handles these operational burdens, providing a more reliable and secure deployment path.
- ✗
Delta Live Tables pipelines.
Why it's wrong here
Delta Live Tables is an ETL framework for data pipeline creation and management. It is designed for data transformation, not for hosting machine learning models or providing real-time inference. Using DLT for model serving is outside the scope of its intended function and lacks the necessary API capabilities.
About these practice questions
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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.