Databricks-ML-Pro ML Ops Practice Question
When deploying a model as a real-time REST API endpoint on Databricks, which service should be used to manage the serving infrastructure, scaling, and availability?
⚠ Common exam trap
Candidates confuse Databricks Model Serving with standard batch jobs or cluster deployments, failing to choose the managed real-time endpoint service.
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
Databricks Model Serving is the managed service designed specifically to deploy models as low-latency, high-availability REST endpoints. It abstracts the underlying infrastructure, providing automatic scaling, version management, and health monitoring. This service is essential for production workloads where reliability and ease of maintenance are paramount, as it removes the burden of manual server administration and infrastructure configuration from the data science team.
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 SQL Warehouse
Why it's wrong here
SQL Warehouses are optimized for running SQL queries and BI workloads, not for hosting model inference services. Attempting to use them for real-time model serving would be ineffective, as they lack the necessary endpoints and API gateway integration for serving machine learning models to applications.
- ✓
Databricks Model Serving
Why this is correct
Model Serving is the dedicated Databricks product for deploying models as REST APIs. It handles the complexities of scaling, security, and availability, allowing teams to focus on model performance rather than infrastructure management. This is the industry-standard way to expose models within the Databricks ecosystem.
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Apache Spark Streaming
Why it's wrong here
Spark Streaming is designed for real-time data processing, not for serving model predictions via REST APIs. While it can consume data and call models, it does not provide the infrastructure needed to host a model endpoint that external applications can call through HTTP requests.
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Databricks File System (DBFS)
Why it's wrong here
DBFS is a distributed file system used for storing data and artifacts. It is not a compute or serving service. Storing model files on DBFS is a prerequisite for deployment, but it does not perform the active task of serving those models to end users via API.
About these practice questions
This Databricks-ML-Pro question is part of Courseiva's 300-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.