Databricks-ML-Pro Model Development Practice Question
You need to perform cross-validation on a large dataset while ensuring that your model remains performant. What is the Databricks-recommended approach?
⚠ Common exam trap
Candidates often suggest manual loops or single-node Scikit-Learn cross-validation, which fails to utilize the distributed computing power of the Databricks cluster for large-scale dataset processing.
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
✓
Use a Spark-based cross-validator or distribute folds across the cluster.
Leveraging Spark's distributed nature for cross-validation allows you to train folds in parallel, maximizing cluster utilization. By using libraries like Spark MLlib or distributed cross-validation wrappers, you can scale the evaluation process to datasets that would otherwise be impossible to handle on a single node. This ensures robust model evaluation while respecting the operational constraints of large-scale distributed data processing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Collect all data to the driver node and use Scikit-Learn's cross_val_score.
Why it's wrong here
Collecting data to the driver for cross-validation will cause memory overflow on large datasets. This approach forces single-threaded execution on the driver, ignoring the massive compute power available in the rest of the cluster and making it impossible to perform validation on datasets larger than the driver's RAM.
- ✓
Use a Spark-based cross-validator or distribute folds across the cluster.
Why this is correct
Distributing folds across the cluster allows for parallel processing of cross-validation, which is crucial for large-scale Databricks workloads. This approach ensures that the model is thoroughly validated using the full capacity of the cluster, maintaining high efficiency while managing the computational load of training multiple model variations concurrently.
- ✗
Perform cross-validation only on a small, sampled subset of the data.
Why it's wrong here
Sampling significantly risks biasing the model validation results, as the subset may not represent the distribution of the full dataset. Relying on samples can lead to selecting a model that performs well on the sample but fails on the full production dataset, which is a major model development error.
- ✗
Hardcode the fold split indices to ensure reproducibility.
Why it's wrong here
While reproducibility is important, hardcoding fold indices is not a scalable strategy and doesn't solve the memory issues associated with large-scale data processing. Using standard, distributed validation strategies is better, as these frameworks handle randomness and partitioning automatically while supporting large datasets without manual, error-prone index management.
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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.