Databricks-ML-Pro Model Development Practice Question
Exhibit
MLflow.log_param('learning_rate', 0.01)
MLflow.log_metric('accuracy', 0.92)
MLflow.log_artifact('/dbfs/ml/models/weights.pt')
MLflow.log_model(model, 'model_artifact')Refer to the exhibit. A data scientist is logging their model training process. Which statement accurately describes the storage location of the artifacts referenced in the code snippet?
⚠ Common exam trap
Candidates often confuse log_model with log_artifact, assuming that model artifacts are dumped into a generic user-specified folder rather than structured automatically within MLflow's internal tracking store directory.
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 model artifact created by log_model is stored in the MLflow tracking store's internal directory structure.
MLflow handles artifacts differently based on the log function used. While 'log_artifact' takes a explicit path (often DBFS or local filesystem), 'log_model' packages the model with its metadata and dependencies into a standard MLflow directory structure. Understanding this distinction is essential for production deployments, as model registry and deployment tools rely on the specific internal structure generated by the 'log_model' function, not just raw file paths.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
All items are stored in the same local directory on the driver node.
Why it's wrong here
MLflow artifacts are typically stored in the configured MLflow tracking server location, which is usually an S3 bucket or Azure Blob Storage in Databricks. The driver node's local storage is ephemeral and not suitable for persistent storage of models or large training artifacts across cluster restarts.
- ✓
The model artifact created by log_model is stored in the MLflow tracking store's internal directory structure.
Why this is correct
The 'log_model' function creates a standardized structure containing the model binary, a MLmodel file, and a conda.yaml file. This allows MLflow to maintain environment parity during deployment. The tracking store automatically manages these artifacts, abstracting the underlying storage layer, which ensures consistency across different stages of the ML lifecycle.
- ✗
The log_artifact command uploads the weights directly to the Model Registry.
Why it's wrong here
The 'log_artifact' command adds files to the run's metadata, not the Model Registry. The Model Registry is a separate entity that tracks model versions and lifecycle stages. Artifacts are associated with a run, whereas a registry entry points to a specific URI where the model is stored.
- ✗
The log_model function is equivalent to copying the file directly to DBFS.
Why it's wrong here
The 'log_model' function does much more than a file copy; it performs serialization, environment capturing, and metadata generation. It creates a complete package that includes dependencies required for inference. Copying a raw file to DBFS would lack the necessary MLflow metadata required for seamless deployment and model versioning tracking.
About these practice questions
Courseiva writes every Databricks-ML-Pro question from scratch — 300 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-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.