Databricks-ML-Assoc ML Workflows Practice Question
Which THREE actions are essential when preparing a machine learning model for deployment using the Databricks Model Registry?
⚠ Common exam trap
Candidates often overlook the importance of the input signature, incorrectly assuming that code alone is sufficient for deployment without defining the expected data schema for downstream inference.
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
✓
Transitioning the model version to the appropriate stage.
Preparing a model involves validating its performance, documenting its lineage, and assigning it to the appropriate registry stage. By logging the model with its signature, you ensure that the input/output schema is preserved. Transitioning through stages (Staging, Production) allows for controlled releases, while adding metadata via tags and descriptions provides necessary context for other stakeholders to understand the model's purpose, limitations, and performance characteristics in a production environment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Transitioning the model version to the appropriate stage.
Why this is correct
Transitions allow for the formal advancement of a model from experimentation to deployment. Using stages like 'Staging' and 'Production' enables teams to control which versions are used in downstream inference applications, ensuring only tested and approved artifacts are exposed to live data and production environments.
- ✓
Logging the model with an input signature.
Why this is correct
The model signature defines the schema of the inputs and outputs. This is crucial for deployment because it allows the serving infrastructure to validate incoming data requests, ensuring that they match the data types and structure the model was trained on, thus preventing runtime inference errors.
- ✓
Adding metadata and descriptions to the model version.
Why this is correct
Metadata, such as training data versions, performance metrics, and descriptions, provides vital context for model governance. When team members audit models, this information helps them understand the model's background, why it was created, and its expected behavior, which is a fundamental requirement for responsible AI practices.
- ✗
Manually downloading the model pickle file to a local machine.
Why it's wrong here
Manual handling of model artifacts outside the Databricks ecosystem violates the principles of reproducible MLOps. The registry provides centralized, secure storage and versioning, and manual downloads introduce security risks and break the automatic lineage tracking provided by the platform, making the deployment process less reliable.
- ✗
Hard-coding the model URI in the application inference code.
Why it's wrong here
Hard-coding URIs is a bad practice as it prevents dynamic updates. If the model version changes, the code must be refactored and redeployed. Instead, code should reference the model stage or an alias, allowing the application to pull the latest approved model version without requiring code changes.
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.