Databricks-ML-Pro Model Deployment Practice Question
A machine learning engineer has deployed a model to a Databricks Model Serving endpoint. The model requires a custom Python package that is not available in the default environment. The engineer has already logged the model with MLflow and included the package in the conda environment. However, upon deployment, the endpoint fails to start. What is the most likely cause?
⚠ Common exam trap
The trap here is assuming that Model Serving automatically has access to all Python packages, but it only installs what is specified in the model's conda environment, including any required index URLs.
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 custom package is not available in a public PyPI repository, and no additional index URL was specified.
When deploying a model with custom dependencies, Databricks Model Serving uses the conda environment logged with the MLflow model. If the package is not on PyPI or requires a private index, you must specify the index URL in the conda environment. Without it, the installation fails, and the endpoint cannot start. Ensuring the environment specification includes all necessary sources is critical for successful deployment.
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 model's MLflow flavor is not supported by Model Serving.
Why it's wrong here
Databricks Model Serving supports all standard MLflow flavors, including Python function, PyTorch, TensorFlow, etc. A custom package does not change the flavor. Unless the model uses an unsupported flavor (which is rare), this is unlikely. The error is more specific to dependency resolution, not the flavor itself.
- ✓
The custom package is not available in a public PyPI repository, and no additional index URL was specified.
Why this is correct
Databricks Model Serving installs dependencies from the conda environment specified in the MLflow model. If the custom package is hosted in a private repository or requires a specific index URL, that must be included in the conda environment's pip section. Without it, the installation fails, causing the endpoint to fail to start. This is a common pitfall when using private packages.
- ✗
The custom package is not installed on the cluster used for serving.
Why it's wrong here
Databricks Model Serving builds the environment from the MLflow model's conda environment specification. You do not need to pre-install packages on a cluster; the serving infrastructure handles dependency installation. The failure is not due to missing installation on a cluster, but rather a misconfiguration in the model's environment specification or an incompatible package.
- ✗
The endpoint's workload size is too small to install the package.
Why it's wrong here
Workload size affects compute resources like memory and CPU, but package installation occurs during environment setup, which has its own resources. While memory constraints could cause failures, a custom package typically does not require excessive memory. The primary issue is usually the package source, not the workload size, making this option less likely.
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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.