Databricks-DE-Assoc Databricks Intelligence Platform Practice Question
A data engineer is configuring a Databricks cluster to run a Spark job that processes large datasets. The job requires high memory and will run for several hours. The engineer wants to minimize costs while ensuring the job completes successfully. Which cluster configuration should the engineer choose?
⚠ Common exam trap
The trap here is assuming that all-purpose clusters are always better because they can be reused, but for a single long-running job, a job cluster with autoscaling is more cost-effective.
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 job cluster with autoscaling enabled, high-memory instance types, and a minimum of 2 workers and a maximum of 10 workers.
For long-running batch jobs, job clusters are more cost-effective than all-purpose clusters. Autoscaling allows the cluster to scale up or down based on workload, ensuring resources are used efficiently. High-memory instances meet the memory requirement. Setting a minimum and maximum worker count provides boundaries for autoscaling, preventing over-provisioning. This combination minimizes cost while ensuring the job has sufficient resources.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use an all-purpose cluster with a fixed number of high-memory workers and enable autoscaling to reduce costs during peak hours.
Why it's wrong here
All-purpose clusters are not cost-optimal for batch jobs, and enabling autoscaling on a fixed-size cluster is contradictory; autoscaling requires a minimum and maximum worker count. This option misconfigures autoscaling and uses a more expensive cluster type, leading to higher costs without ensuring job completion.
- ✗
Use a job cluster with a fixed number of high-memory workers and enable auto-termination after 30 minutes of inactivity.
Why it's wrong here
A job cluster is cost-effective for batch jobs, but a fixed number of workers may not handle varying loads efficiently. Auto-termination is useful for interactive clusters but for a job that runs for several hours, it may not trigger until after completion. The scenario does not indicate idle periods, so this setting may not provide cost savings. Autoscaling would better match resource needs.
- ✓
Use a job cluster with autoscaling enabled, high-memory instance types, and a minimum of 2 workers and a maximum of 10 workers.
Why this is correct
Job clusters are designed for automated workloads and are more cost-effective than all-purpose clusters. Autoscaling dynamically adjusts the number of workers based on workload, optimizing resource usage and cost. High-memory instances address the memory requirement. This configuration balances performance and cost for a long-running job with varying demands.
- ✗
Use an all-purpose cluster with autoscaling enabled and a minimum of 2 workers and a maximum of 10 workers, with high-memory instance types.
Why it's wrong here
All-purpose clusters are typically used for interactive analysis and are more expensive than job clusters. While autoscaling can help with variable workloads, for a long-running batch job, a job cluster is more cost-effective. High-memory instances are appropriate, but the cluster type is not optimal for cost minimization in this scenario.
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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-DE-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-DE-Assoc exam.