Databricks-ML-Pro Model Deployment Practice Question
A team is deploying a model to Databricks Model Serving that requires a specific version of a Python library that conflicts with the version pre-installed in the serving environment. They include the library version in the model's requirements.txt. However, upon deployment, the endpoint fails to start, and logs indicate a dependency conflict. What is the most likely cause of this failure?
⚠ Common exam trap
The trap here is assuming that any library version can be installed, when in fact the serving environment has fixed dependencies that can cause conflicts.
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
✓
The library version conflicts with a pre-installed library that is required by the serving infrastructure, causing a dependency resolution failure.
Model Serving environments come with pre-installed libraries that support the serving infrastructure. If a model's requirements.txt specifies a version that conflicts with these, the dependency resolver may fail, preventing the endpoint from starting. The solution is to align the requested version with the pre-installed one or use a custom container image to isolate dependencies.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The library version conflicts with a pre-installed library that is required by the serving infrastructure, causing a dependency resolution failure.
Why this is correct
Databricks Model Serving has a set of pre-installed libraries that are critical for the serving runtime. If your requirements.txt specifies a version that conflicts with these, pip may fail to resolve dependencies, or the endpoint may crash at runtime. This is a common cause of deployment failures when custom dependencies clash with the base environment.
- ✗
The model's requirements.txt is not being parsed correctly due to a syntax error.
Why it's wrong here
A syntax error in requirements.txt would cause a different error, typically a parsing error during the build phase. The scenario describes a dependency conflict, which is a resolution issue, not a syntax error. If there were a syntax error, the build would fail immediately with a clear message about the malformed line.
- ✗
The serving environment ignores requirements.txt and uses only the pre-installed libraries.
Why it's wrong here
Databricks Model Serving does respect requirements.txt and will attempt to install the specified packages. However, if there is a conflict with pre-installed packages that are essential for the serving infrastructure, the installation may fail or cause runtime errors. The environment does not simply ignore requirements.txt.
- ✗
The specified library version is incompatible with the Python version used by the serving environment.
Why it's wrong here
While Python version incompatibility can cause deployment failures, the scenario specifies a conflict with a pre-installed library, not the Python version. The error logs would typically indicate a version conflict between packages rather than a Python version mismatch.
Visual reference
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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-Pro 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-Pro exam.