Databricks-ML-Assoc ML Workflows Practice Question
A data scientist needs to track parameters, metrics, and model artifacts during training on Databricks. Which approach is the industry-standard best practice to ensure reproducibility and lineage?
⚠ Common exam trap
Candidates often rely on manual logging or external databases, ignoring the built-in MLflow Tracking API which is the industry standard for maintaining lineage and reproducibility in Databricks.
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
✓
Use the MLflow Tracking API to log parameters and artifacts during the training run.
MLflow Tracking is the core component for logging parameters, metrics, and artifacts in Databricks. By integrating MLflow into the training script, data scientists create a centralized record of every experiment run. This allows for easy comparison between different model versions, facilitates team collaboration, and ensures that the exact code, environment, and hyperparameter configuration used to produce a specific model artifact can be audited and reproduced later in the MLOps lifecycle.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Manually save model artifacts to DBFS and document parameters in a spreadsheet.
Why it's wrong here
Manual tracking lacks the programmatic automation required for scalable MLOps. Spreadsheet documentation is prone to human error, lacks auditability, and does not provide the rich visualization capabilities required for comparing performance across dozens of concurrent model training experiments or tracking lineage back to the original training dataset.
- ✗
Write all model logs to a local temporary folder on the driver node.
Why it's wrong here
Data stored on the driver's local file system is ephemeral and will be lost once the cluster terminates or the node is decommissioned. Relying on local storage prevents team members from accessing historical logs, rendering it impossible to conduct collaborative model reviews or facilitate production deployments.
- ✓
Use the MLflow Tracking API to log parameters and artifacts during the training run.
Why this is correct
MLflow Tracking provides a robust, centralized API to record experiments. It automatically captures metadata, code versions, and artifacts, making them accessible via the Experiments sidebar. This ensures that every model training process is documented, searchable, and repeatable, which is foundational for maintaining high-quality machine learning model lifecycle management.
- ✗
Deploy the model directly to production without logging parameters to save compute costs.
Why it's wrong here
Deploying models without logging training metadata violates basic MLOps governance principles. Without captured parameters and metrics, you cannot debug model performance regressions or fulfill regulatory requirements for model transparency and lineage. This approach creates significant technical debt and prevents the ability to roll back to known-good versions.
About these practice questions
One of 319 original Databricks-ML-Assoc practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
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.