Databricks-ML-Assoc Databricks Machine Learning Practice Question
Which THREE of the following are supported methods for serving machine learning models in Databricks?
⚠ Common exam trap
Candidates often overlook SQL user-defined functions (UDFs) as a valid model-serving method, incorrectly assuming serving is restricted solely to REST APIs or batch jobs.
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
✓
Real-time REST API endpoints
Databricks provides multiple pathways for model serving, catering to different latency and throughput requirements. Real-time serving provides low-latency REST endpoints for interactive applications. Batch inference allows for high-throughput processing on large datasets using Spark. Finally, user-defined functions (UDFs) allow for embedding model logic directly into SQL or DataFrame transformations, which is highly useful for integrating machine learning models within existing data engineering pipelines and analytics workflows.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Real-time REST API endpoints
Why this is correct
Real-time model serving provides a low-latency, scalable REST endpoint. It is the ideal method for serving predictions to web applications or mobile apps that require immediate responses, effectively managing the infrastructure and load balancing to ensure high availability and consistent performance for production-ready machine learning services.
- ✗
Manual model training in a local Excel file
Why it's wrong here
Excel is not a supported environment for model serving or training within the Databricks ecosystem. It lacks the distributed compute capabilities and integration with MLflow required to serve models. Using local tools outside the platform breaks the governance, monitoring, and scalability benefits that Databricks provides for machine learning.
- ✓
Batch inference via Spark jobs
Why this is correct
Batch inference is designed for high-throughput scenarios where predictions are calculated for large datasets offline. By using Spark, users can distribute the inference load across many nodes, making it efficient for large-scale tasks like generating recommendations or updating risk scores for millions of records overnight.
- ✓
SQL user-defined functions (UDFs)
Why this is correct
SQL UDFs allow data analysts and engineers to call machine learning models directly within SQL queries. This integration simplifies the application of models to data stored in Delta tables, making it incredibly easy to incorporate ML insights into dashboards or reporting workflows without needing complex custom code.
- ✗
Streaming model updates to public web hosting
Why it's wrong here
While models can be deployed to web endpoints, 'streaming updates to public web hosting' is not a native serving pattern supported by Databricks. Infrastructure is managed within the platform for security and governance, and arbitrary public hosting is not a standard or recommended method for model deployment.
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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
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