Databricks-ML-Assoc Model Development Practice Question
Which of the following is the recommended workflow for developing a scalable model on Databricks?
⚠ Common exam trap
Candidates frequently select workflows that skip distributed training or register models outside of MLflow, breaking standard enterprise deployment patterns.
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
✓
Perform EDA, feature engineering, distributed training, and register with MLflow.
The iterative cycle of data exploration, feature engineering, distributed training, and model registry management is the standard Databricks practice. By utilizing MLflow throughout, scientists ensure reproducibility. Scaling is handled by Spark, and the Feature Store ensures the same data definitions are used for both training and inference. This workflow bridges the gap between ad-hoc experimentation and production-ready machine learning, ensuring that the model is robust, documented, and easily deployable via standard enterprise pipelines.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Train a model on a local machine, then upload the final artifact to Databricks.
Why it's wrong here
This approach is not scalable and hinders reproducibility. Large datasets cannot be handled on local machines, and moving artifacts between environments often leads to dependency conflicts. Developing directly in Databricks ensures that the environment, data, and compute are unified, enabling larger datasets and better collaboration within the workspace.
- ✓
Perform EDA, feature engineering, distributed training, and register with MLflow.
Why this is correct
This is the best-practice workflow on Databricks. It leverages the platform's capabilities for every stage of the lifecycle: distributed data processing (Spark), consistent feature management (Feature Store), efficient training, and standardized deployment via the MLflow Model Registry, resulting in a robust, reproducible, and production-ready machine learning pipeline.
- ✗
Use a single-node cluster for all stages to avoid network latency.
Why it's wrong here
While single-node clusters are useful for small projects, they prevent scaling to large datasets, which is the primary value proposition of Databricks. Using a single node restricts the user to the memory and compute of one machine, defeating the purpose of distributed computing when facing large-scale ML workloads.
- ✗
Skip logging metrics to save space and improve training speed.
Why it's wrong here
Logging metrics is critical for identifying the best model. Without metric tracking, one cannot perform hyperparameter tuning or compare experiment performance. Space saved by skipping logs is negligible, while the loss of transparency and the inability to validate model performance make this an unacceptable practice in any professional development environment.
About these practice questions
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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.