Databricks-ML-Assoc Databricks Machine Learning Practice Question
A data scientist needs to prepare a model for deployment in a highly regulated environment. Which TWO tasks must they complete to ensure the model meets auditability requirements?
⚠ Common exam trap
Candidates often select general workspace backup features or standard model performance metrics, forgetting that auditability strictly requires formal model registration and data lineage tracking.
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
✓
Registering the model in the MLflow Model Registry.
Auditability in regulated industries requires proof of origin and transparency. Registering the model in the MLflow Model Registry provides a clear version history and change logs, while linking the model back to the specific run, training data, and notebook ensures full lineage. These steps are mandatory for compliance, ensuring that any model prediction can be traced back to its training source, which is critical for legal and business accountability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Registering the model in the MLflow Model Registry.
Why this is correct
The Model Registry provides a centralized, versioned history of the model's evolution. It acts as the system of record for auditors to verify when models were created, who approved them, and what version is currently deployed, which is essential for meeting compliance standards in highly regulated industries and project environments.
- ✗
Deleting all training logs after deployment.
Why it's wrong here
Deleting logs is catastrophic for auditability. Regulators require access to the entire history of training runs to ensure that models were developed using approved, unbiased processes. Deleting these logs would not only break the lineage but also likely trigger a compliance violation during any audit of the model development pipeline.
- ✓
Documenting the lineage from the model to the training data.
Why this is correct
Tracing a model back to its training dataset is a fundamental requirement for regulatory audits. It allows organizations to prove that the data used for training was appropriate, representative, and free from bias, ensuring that the model's performance can be justified and validated against the original, governed data sources used.
- ✗
Hard-coding model weights in the inference script.
Why it's wrong here
Hard-coding weights is a dangerous practice that makes it impossible to track or version the model. It hides the model's structure and training history, making it completely unauditable. This practice is fundamentally incompatible with the requirements of a regulated environment and would likely fail any standard security or compliance review.
- ✗
Using a local Git repository for versioning.
Why it's wrong here
A local Git repository is not suitable for enterprise-grade auditability in Databricks. It lacks the integrated governance and access control features required to prove compliance. Using the native Databricks tools ensures that version control is tied into the platform's overall security and governance framework, providing a more robust audit trail.
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.