Databricks-ML-Assoc Model Development Practice Question
A data scientist is training a Random Forest model on a 500GB dataset using Databricks. They notice that the model training process is consistently running out of memory on the driver node. Which approach should be taken to resolve this memory bottleneck while maintaining model performance?
⚠ Common exam trap
Candidates try to increase driver memory or use pandas-based libraries to process the entire dataset locally. They ignore the distributed nature of Spark, which is required for large datasets.
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 RandomForestRegressor implementation to distribute computation across workers.
Random Forest models often store large ensembles and intermediate metadata in memory. When training on distributed data, using Spark ML's native distributed algorithms is essential. By moving computation to the worker nodes and leveraging the Spark RDD-based implementation, the driver node is relieved from holding the entire dataset or model state, allowing for successful training on larger datasets without frequent OOM errors that occur when pulling data locally.
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 instance size to match the total dataset volume.
Why it's wrong here
Scaling the driver vertically is a temporary fix that does not address the underlying architectural issue of processing data in a distributed environment. Spark is designed to handle memory through cluster utilization, and relying on a massive driver node leads to inefficient resource allocation and potential bottlenecks elsewhere.
- ✗
Convert the dataset to a Pandas DataFrame and train using the Scikit-Learn library directly.
Why it's wrong here
Pandas DataFrames are loaded entirely into the memory of a single node. Attempting to load 500GB into a single node's memory will cause immediate failure. Scikit-learn is not natively distributed; therefore, it cannot process datasets that exceed the memory capacity of the single machine running the code.
- ✓
Use the Spark MLlib RandomForestRegressor implementation to distribute computation across workers.
Why this is correct
Spark MLlib is specifically designed for distributed machine learning. By utilizing Spark's parallel processing capabilities, the data and computation are spread across worker nodes. This prevents the driver node from becoming a bottleneck, allowing the algorithm to scale effectively as the dataset size increases beyond local memory limits.
- ✗
Enable broadcast joins on the feature table before training the model.
Why it's wrong here
Broadcast joins are used for optimizing join performance by sending smaller tables to all nodes. They do not resolve memory issues associated with model training algorithms. The training process itself requires distributing the input features and model state, which broadcast variables cannot optimize in this specific context.
Visual reference
About these practice questions
Courseiva writes every Databricks-ML-Assoc question from scratch — 319 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-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.