Databricks-ML-Assoc Model Development Practice Question
A machine learning engineer is using Hyperopt with SparkTrials on Databricks to tune a gradient boosting model. They notice that the tuning process is taking longer than expected and want to optimize resource utilization. They have a cluster with 8 worker nodes. Which configuration should they adjust to allow SparkTrials to run more trials in parallel?
⚠ Common exam trap
A common mix-up: candidates confuse max_evals with parallelism; max_evals controls total trials, not concurrent trials, and increasing it won't speed up tuning if parallelism is low.
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
✓
Set the parallelism parameter in SparkTrials to a higher value, up to the number of Spark task slots
SparkTrials parallelism determines how many trials run concurrently. By default, it equals the number of Spark executors. Increasing it up to the number of task slots allows more trials to run in parallel, better utilizing the cluster and reducing tuning time.
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 max_evals parameter to run more evaluations sequentially
Why it's wrong here
max_evals specifies the total number of trials to run, not the number in parallel. Increasing it will run more trials overall but won't change parallelism, so it won't reduce wall-clock time if resources are underutilized. It may actually increase total time. The issue is parallelism, not total evaluations.
- ✗
Set the spark.task.cpus configuration to a higher value to allocate more CPUs per task
Why it's wrong here
spark.task.cpus controls the number of CPU cores per task. Increasing it would allocate more cores to each task, potentially reducing the number of tasks that can run in parallel. This is counterproductive for increasing parallelism. The goal is to run more trials concurrently, not to give each trial more resources.
- ✗
Use the Trials class instead of SparkTrials to enable distributed tuning
Why it's wrong here
The Trials class is for single-machine tuning and does not distribute trials across the cluster. SparkTrials is specifically designed for distributed tuning on Spark. Switching to Trials would actually reduce parallelism and utilize only the driver node, making tuning slower. This is the opposite of what is needed.
- ✓
Set the parallelism parameter in SparkTrials to a higher value, up to the number of Spark task slots
Why this is correct
The parallelism parameter in SparkTrials controls the maximum number of trials to run concurrently. By default, it is set to the number of Spark executors, but you can increase it up to the total number of task slots. With 8 workers, increasing parallelism can better utilize the cluster and speed up tuning. This is the correct adjustment for resource utilization.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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