Databricks-ML-Assoc Model Deployment Practice Question
What is the purpose of the 'Champion' model version in the Model Registry?
⚠ Common exam trap
Candidates often confuse the 'Champion' version with the latest trained model version, assuming newest always means default, whereas 'Champion' must be explicitly designated for production traffic.
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
✓
To serve as the model used by all users by default
The 'Champion' model is the designated version that currently handles the primary traffic for a deployment. By explicitly labeling a version as the Champion, teams can clearly identify the production-ready model. This provides a single source of truth for deployment automation, ensuring that when an endpoint is configured to serve the 'Champion', it is always running the intended, validated model without manual configuration changes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
To serve as the model used by all users by default
Why this is correct
The Champion model is intended to be the standard version that applications and users should interact with. It serves as the primary version for production workloads, ensuring that all traffic is routed to the most stable and performant model version currently vetted by the organization's governance processes.
- ✗
To identify the model that took the longest to train
Why it's wrong here
Training duration is not a criterion for becoming the Champion. The status is assigned based on performance metrics, validation results, and stakeholder approval. Identifying a model based on training time would be an arbitrary measure that provides no value to the business or the operational lifecycle of the model.
- ✗
To represent a model that is currently being tested
Why it's wrong here
Models being tested are typically labeled as 'Staging' or 'Challenger'. The 'Champion' label is reserved for models that have already passed all testing and performance benchmarks. Using a Champion designation for a model in testing would be dangerous, as it could accidentally push untested code into production.
- ✗
To store the source code for the model
Why it's wrong here
The Champion designation is a metadata flag, not a storage location for source code. Source code is usually managed in version control systems like Git or via MLflow tracking, separate from the registration status. The label simply indicates the role of that specific version in the deployment pipeline.
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.