Databricks-ML-Assoc Model Development Practice Question
When logging a model, you decide to store a 'data_version' tag. What is the benefit of this practice?
⚠ Common exam trap
Candidates often confuse 'data_version' with 'model_version'. They incorrectly assume it is for version control of the model code itself rather than tracking the specific dataset snapshot used for training.
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 allows for easy filtering and tracking of model performance across different data versions.
Tagging models with data versions is a fundamental practice for data lineage. Since model performance is highly dependent on the underlying training data, tracking the exact version of the dataset used allows for better reproducibility and debugging. This enables engineers to trace a model back to the specific raw data state that produced it, which is essential for compliance and impact analysis when retraining models on updated datasets.
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 automatically triggers a new training job whenever the underlying Delta table is updated.
Why it's wrong here
Tags are passive metadata and do not trigger any automated workflows. While they are useful for documentation, they do not have any inherent functional connection to the data pipeline. Automation requires setting up Databricks Workflows or similar job scheduling, not simply tagging the model with a version string.
- ✓
It allows for easy filtering and tracking of model performance across different data versions.
Why this is correct
Tags are indexed in MLflow, allowing you to filter runs and model versions by specific criteria. If you tag models with a data version, you can quickly group and compare performance across different snapshots of the data, which is crucial for understanding how data drift affects model accuracy over time.
- ✗
It compresses the model artifact size by referencing the data instead of embedding it.
Why it's wrong here
Tags have no impact on the size of the model artifact. They are simple key-value pairs stored in the MLflow backend database. Reducing model size is typically achieved by using model-specific quantization or pruning techniques, which are completely separate from the metadata tagging process used for lineage tracking.
- ✗
It creates an immutable copy of the dataset within the model registry.
Why it's wrong here
Tags do not create copies of data. They are purely metadata. To achieve data immutability, you must use features like Delta Lake's time-travel or snapshot capabilities. A tag simply provides a label that points to the version, it does not physically store or protect the data from deletion or modification.
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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.