Databricks-ML-Assoc Databricks Machine Learning Practice Question
A data science team is preparing to deploy a custom machine learning model to Databricks Model Serving. Which TWO steps are required to ensure the model can successfully load and serve predictions using MLflow? Choose 2 answers.
⚠ Common exam trap
Examinees often forget that logging the model with a signature and registering it with a production stage or alias are both mandatory dual prerequisites for serving.
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
✓
Log the model using mlflow.sklearn.log_model() or equivalent flavor with a defined input example and model signature.
Deploying models to Databricks Model Serving requires logging the model with a valid signature and environment specification, and registering it to the Unity Catalog or Workspace Model Registry. These steps ensure the serving infrastructure can automatically provision the correct scoring environment, handle incoming JSON payloads, and validate data types against expected schemas.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Log the model using mlflow.sklearn.log_model() or equivalent flavor with a defined input example and model signature.
Why this is correct
Logging models with explicit signatures and input examples allows Databricks Model Serving to automatically validate incoming REST API request payloads against the expected schema, preventing runtime type errors and ensuring reliable scoring execution in production.
- ✗
Manually install all Python dependencies on every serving endpoint worker node via a custom startup script.
Why it's wrong here
MLflow captures dependencies in the model's conda environment, which Model Serving restores automatically per endpoint version. Startup scripts are tempting because they do install packages on clusters, but serving endpoints are managed and do not expose worker nodes for custom bootstrapping.
- ✓
Register the logged model version to the Unity Catalog or Workspace Model Registry and transition it to the Production stage or alias.
Why this is correct
Model serving endpoints source their deployed models directly from the Unity Catalog or Workspace Model Registry. Registering the model and assigning appropriate aliases or stage tags makes the model version available for selection in the serving endpoint configuration UI.
- ✗
Export the model artifact into a raw Hadoop Distributed File System directory without using the MLflow format.
Why it's wrong here
Model Serving loads models through the MLflow format, which records the flavour, signature and dependency metadata needed at load time. Raw HDFS artefacts are tempting because Databricks storage is HDFS-backed, but without MLflow's metadata the serving container cannot reconstruct the model.
- ✗
Configure Apache Spark streaming jobs to continuously push payload batches into the model serving container endpoint.
Why it's wrong here
Model Serving exposes a REST endpoint that scores requests synchronously; no Spark streaming job is involved in loading or serving the model. Streaming is tempting because Databricks handles streaming workloads, but it addresses data pipelines, not the MLflow deployment requirement.
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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.