Databricks-ML-Assoc Model Deployment Practice Question
A data scientist registers an MLflow model whose `conda.yaml` lists several Python packages. When they create a Databricks Model Serving endpoint from this model, the deployment fails during environment build. Which action is most likely to resolve the failure while preserving the model's dependency requirements?
⚠ Common exam trap
The trap here is assuming that enlarging the endpoint's workload size will fix any deployment error, when environment build failures stem from the dependency specification rather than compute capacity.
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
✓
Add the missing or incompatible packages to the endpoint's environment by specifying them in the model's `requirements.txt` or adjusting the `conda.yaml` to versions available in the Databricks runtime.
Model Serving reconstructs the model's environment from the dependency files captured at logging time. When a required package is absent or a pinned version cannot be resolved, the build fails. Adjusting the `requirements.txt` or `conda.yaml` to include the necessary packages at versions compatible with the Databricks runtime lets the environment build succeed while keeping the dependencies the model genuinely needs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove all pinned package versions from the `conda.yaml` so the environment builder can pick any compatible release.
Why it's wrong here
Stripping version pins may let the build succeed, but it discards the exact dependency versions the model was trained and tested against. That can silently change numerical behavior or break the model at inference time, which is unacceptable for a production endpoint. The goal is to resolve the build failure while honoring the required dependencies, not to abandon the reproducibility that pinning provides. This trades correctness for convenience.
- ✗
Increase the endpoint's workload size to Large so the environment builder has more memory to resolve the dependency graph.
Why it's wrong here
Workload size determines the compute resources of the serving containers, not the environment resolution process. A dependency conflict or a missing package will fail regardless of how much memory or CPU the endpoint has. Scaling up might mask an out-of-memory issue during build, but the scenario describes an environment build failure rooted in the dependency specification, so changing workload size does not address the cause.
- ✓
Add the missing or incompatible packages to the endpoint's environment by specifying them in the model's `requirements.txt` or adjusting the `conda.yaml` to versions available in the Databricks runtime.
Why this is correct
Model Serving builds the environment from the dependency specification captured with the model. If a package is missing or pinned to a version that cannot be resolved, the build fails. Correcting the `requirements.txt` or aligning `conda.yaml` entries with versions available in the Databricks runtime lets the builder resolve the environment while still installing the libraries the model needs, preserving the model's required dependencies.
- ✗
Convert the model to a different flavor, such as replacing scikit-learn with a PyTorch implementation, so the dependencies are no longer needed.
Why it's wrong here
Rewriting the model in a different framework is an enormous change that has nothing to do with fixing an environment build problem. It would require retraining and revalidation, and there is no guarantee the new implementation would behave identically. The failure is about dependency resolution during packaging, not about the modeling framework itself, so switching flavors is a disproportionate and incorrect response to the actual error.
Visual reference
About these practice questions
Courseiva writes every Databricks-ML-Assoc question from scratch — 319 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. 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.