Databricks-ML-Assoc Model Development Practice Question
When logging a model in Databricks, why is it recommended to specify the `pip_requirements` or `conda_env` explicitly instead of relying on the environment's current state?
⚠ Common exam trap
Candidates often assume that the current notebook environment is sufficient for deployment, failing to account for 'dependency bloat' where unnecessary packages cause conflicts in production.
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 ensures the model remains portable and reproducible.
Relying on the current notebook environment is risky because it captures all installed packages, not just those required for the model. Specifying dependencies explicitly ensures that the environment is minimal and reproducible, which is vital for preventing environment conflicts in production. This practice ensures that the model can be deployed successfully in clean, isolated environments, a core requirement for stable production model serving and CI/CD pipelines.
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 forces the model to use the latest version of all libraries.
Why it's wrong here
Explicit dependencies do the exact opposite; they lock the model to the versions tested during development. Relying on the latest version would be dangerous, as it could introduce breaking changes or compatibility issues, undermining the reproducibility and stability of the model deployment process.
- ✓
It ensures the model remains portable and reproducible.
Why this is correct
Explicitly defined dependencies create a portable model artifact that carries its own environment specification. This ensures that regardless of where the model is deployed—whether on a different Databricks cluster or a separate containerized environment—it will always have the exact libraries required to execute predictions correctly.
- ✗
It significantly reduces the model artifact's memory usage.
Why it's wrong here
Dependencies define the software runtime environment, not the memory usage of the model during execution. The memory footprint is determined by the size of the model object and the input data processed during inference, not by the list of libraries or requirements saved alongside the model file.
- ✗
It allows the model to run without any Python libraries.
Why it's wrong here
Machine learning models rely heavily on framework libraries like scikit-learn, TensorFlow, or PyTorch. It is impossible to run these models without the corresponding Python environment. Explicitly defining requirements ensures those libraries are installed, but it does not remove the necessity of having those runtime libraries present in the system.
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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.