Databricks-ML-Assoc ML Workflows Practice Question
Which of the following is a core characteristic of 'Model Serving' in Databricks?
⚠ Common exam trap
Students often think manual cluster scaling or writing custom load-balancing scripts is required for production deployments, forgetting that Databricks Model Serving handles scaling automatically.
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 automatically scales to handle incoming request volume.
Model Serving in Databricks allows you to deploy models from the Model Registry as low-latency REST endpoints. These endpoints automatically scale based on demand and handle the underlying infrastructure. This enables data scientists to easily expose their models as services for applications, ensuring high availability and consistent performance without needing to manage the complexities of server configuration, manual load balancing, or capacity provisioning.
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 is used strictly for batch processing of large datasets.
Why it's wrong here
Model serving is designed for real-time or near-real-time inference via REST APIs, not for batch processing. Batch processing is typically handled by Databricks Jobs running on Spark clusters, which can process data at scale in parallel, whereas serving is optimized for individual or small-batch requests with minimal latency.
- ✓
It automatically scales to handle incoming request volume.
Why this is correct
Model serving is built to be auto-scaling. It monitors incoming traffic and adjusts the number of instances accordingly, ensuring that the endpoint remains responsive during spikes in demand while optimizing costs during periods of low activity, which is essential for maintaining a reliable production-grade machine learning service.
- ✗
It requires the user to manually manage Kubernetes clusters.
Why it's wrong here
Databricks Model Serving is a managed service that abstracts away the underlying infrastructure. Users do not need to manage Kubernetes, manage nodes, or configure container orchestration. This serverless approach allows teams to focus on model development and deployment rather than the operational overhead of maintaining infrastructure, which significantly improves developer productivity.
- ✗
It stores training data for the model to use at runtime.
Why it's wrong here
Serving endpoints do not store training data; they contain the model artifact and the necessary inference environment. If the model requires access to historical data for context (like feature values), it should retrieve them from a Feature Store or a database, not from the model serving endpoint itself.
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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.