Databricks-ML-Assoc Model Development Practice Question
A data scientist is training a Random Forest model on a 500GB dataset using Databricks. They notice the training process is slow and memory-intensive on a single worker node. Which approach should they take to optimize the training process?
⚠ Common exam trap
Candidates often assume that standard Scikit-Learn code will automatically scale by simply running it on a larger Databricks cluster, failing to recognize that single-node libraries cannot parallelize across Spark workers.
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 the Spark MLlib Random Forest implementation which natively supports distributed training.
Utilizing Spark MLlib's distributed training capabilities allows the Random Forest algorithm to partition data across the cluster nodes rather than relying on a single executor. This significantly reduces the memory overhead per node and parallelizes the tree-building process. Understanding this mechanism is vital for scaling ML workflows on Databricks, as it shifts the bottleneck from local memory constraints to cluster-wide compute resources, facilitating the processing of large-scale datasets efficiently.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the driver node memory to accommodate the full dataset during training.
Why it's wrong here
Expanding driver memory is ineffective because Spark MLlib is designed for distributed execution. The driver node coordinates the tasks rather than storing the entire dataset in its memory. Relying on the driver for data processing would eventually cause an OutOfMemoryError regardless of how much RAM is allocated.
- ✗
Convert the dataset to a single massive CSV file stored on DBFS to improve I/O speeds.
Why it's wrong here
CSV files are not optimized for large-scale distributed reading compared to formats like Parquet or Delta. Reading a single massive file often forces a single task to handle the entire load, negating the benefits of parallel processing and likely resulting in significant performance degradation during the data ingestion stage.
- ✓
Use the Spark MLlib Random Forest implementation which natively supports distributed training.
Why this is correct
Spark MLlib Random Forest is specifically built to handle large datasets by distributing both the data and the computation across the cluster. It parallelizes the construction of decision trees, allowing the model to be trained on datasets that exceed the memory capacity of any individual worker node in the cluster.
- ✗
Enable local caching for all input features to minimize data shuffling.
Why it's wrong here
While caching can improve performance for iterative algorithms, it does not resolve the memory bottleneck inherent in training on a single node. Caching is a temporary optimization; the core issue remains the attempt to process large datasets without distributing the workload across the available cluster's computational resources.
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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.