Databricks-ML-Assoc Databricks Machine Learning Practice Question
A user is experiencing 'Out of Memory' (OOM) errors during the evaluation phase of a large XGBoost model on Databricks. What is the most effective way to address this while utilizing the distributed nature of Databricks?
⚠ Common exam trap
Candidates frequently try to increase driver memory or optimize local code, failing to realize that OOM errors on large datasets are solved by distributing the workload via Spark.
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-native implementation of the algorithm.
Using Spark-based distributed training, such as the `sparkdl` or native XGBoost spark estimator, allows the data to be partitioned across the cluster nodes. This prevents the driver from attempting to hold the entire dataset in memory. By distributing the workload, the system can handle datasets that exceed the memory capacity of a single machine, which is fundamental for scaling machine learning applications on Databricks.
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 indefinitely.
Why it's wrong here
Continuously increasing driver memory is a short-term workaround that does not solve the fundamental scalability issue. It is cost-inefficient and will eventually hit hardware limits. Proper architecture involves distributing the data processing and training tasks across multiple worker nodes to handle memory-intensive workloads more effectively and reliably.
- ✓
Use a Spark-native implementation of the algorithm.
Why this is correct
Spark-native implementations, such as the XGBoost classifier in the Spark-MLlib compatible library, distribute the dataset and the training process across the cluster. This allows for parallel processing of data partitions, preventing memory overflows on a single node and enabling the training of models on large-scale datasets efficiently.
- ✗
Downsample the data until it fits in memory.
Why it's wrong here
Downsampling reduces the training data, often resulting in lower model accuracy and loss of information. It does not utilize the cluster's distributed capabilities. Instead of sacrificing data quality, the correct approach is to leverage distributed training techniques that allow the entire dataset to be used for model training.
- ✗
Convert the data to a single JSON file.
Why it's wrong here
Storing data as a single JSON file does not solve memory issues and may actually exacerbate them, as JSON is often less memory-efficient than binary formats like Parquet or Delta. Additionally, a single file must still be loaded, which will likely cause an OOM error if the data is large.
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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-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.