Databricks-ML-Assoc Databricks Machine Learning Practice Question
Which approach is most efficient for deploying a high-throughput, low-latency model in Databricks?
⚠ Common exam trap
Candidates often suggest using a standard Spark job or a notebook for low-latency serving. However, these lack the necessary infrastructure for production-grade, high-throughput REST API endpoints.
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
✓
Using Databricks Model Serving
Databricks Model Serving is the recommended service for production environments requiring low latency and high scalability. It automatically manages scaling and infrastructure, ensuring that models are served in a highly available manner. By abstracting away the server management and load balancing, it allows developers to focus on model performance rather than infrastructure maintenance, which is essential for maintaining service level agreements in production-grade ML applications.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Running a custom Flask app on a standard notebook
Why it's wrong here
Running a Flask app on a notebook is not suitable for production. It lacks scalability, proper load balancing, and the high-availability features required for production systems. Furthermore, notebooks are not designed to act as stable web servers, leading to performance instability under heavy concurrent request loads.
- ✓
Using Databricks Model Serving
Why this is correct
Databricks Model Serving provides a fully managed, low-latency API endpoint for models. It handles autoscaling, health monitoring, and infrastructure provisioning, making it the standard choice for production environments that require reliable performance and high throughput without the overhead of manually managing containerized inference servers or Kubernetes clusters.
- ✗
Batch processing using Spark streaming
Why it's wrong here
Batch processing is intended for high-throughput, latency-insensitive workloads. It is not suitable for low-latency requirements where responses must be returned in milliseconds. While efficient for large-scale data processing, it cannot meet the real-time requirements of interactive applications that demand immediate feedback from the machine learning model.
- ✗
Manual deployment to an external cloud VM
Why it's wrong here
Manual deployment introduces unnecessary complexity, security risks, and management overhead. It breaks the unified workflow offered by Databricks, moving the model outside the governance of the platform. This approach hinders auditing, monitoring, and the seamless CI/CD integration provided by native Databricks tools like MLflow and Unity Catalog.
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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.