Databricks-ML-Assoc Model Development Practice Question
A data scientist is using Hyperopt with SparkTrials on a Databricks cluster to tune a scikit-learn model. They set max_evals=100 and parallelism=4. After the tuning completes, they notice that some trials failed due to memory errors on the workers. What is the most likely cause of these failures?
⚠ Common exam trap
The trap here is assuming that SparkTrials distributes the training of a single model across workers, when it actually distributes independent trials, each on a single worker.
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
✓
Each trial is training on the full dataset, and the dataset is too large to fit in the memory of a single worker.
The correct answer is that each trial is training on the full dataset, and the dataset is too large to fit in the memory of a single worker. SparkTrials distributes trials, but each trial runs on one worker with the full dataset. If the data is large, it can cause out-of-memory errors. To mitigate, you can reduce the dataset size, increase worker memory, or use distributed algorithms. The other options misattribute the cause to model incompatibility, driver memory, or autoscaling.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The scikit-learn model is being trained on the driver, causing memory pressure on the driver instead of workers.
Why it's wrong here
With SparkTrials, each trial runs on a Spark worker, not the driver. The driver orchestrates the trials. Memory errors on workers indicate that the workers are running out of memory during training, not that the driver is overloaded.
- ✓
Each trial is training on the full dataset, and the dataset is too large to fit in the memory of a single worker.
Why this is correct
SparkTrials runs each trial on a single worker, and if the dataset is large, it may exceed the worker's memory. Unlike distributed training, each trial is independent and uses the full dataset unless you subsample or use Spark ML. This leads to out-of-memory errors when the data is too big for one worker.
- ✗
SparkTrials does not support scikit-learn models and should be used only with MLlib.
Why it's wrong here
SparkTrials is designed to distribute hyperparameter tuning for any Python model, including scikit-learn, by running trials on Spark workers. It does not restrict the model type. The memory errors are not due to incompatibility but likely due to resource contention or large data per trial.
- ✗
The cluster's autoscaling is reducing the number of workers, causing trials to be queued and eventually fail.
Why it's wrong here
Autoscaling adjusts the number of workers based on load, but it does not cause memory errors within running trials. Queued trials would wait, not fail with memory errors. The errors are more likely due to insufficient memory per worker for the trial's workload.
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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.