Databricks-DA-Assoc Understanding the Databricks Platform Practice Question
A Databricks workspace administrator wants to optimize costs and manage resources effectively. Which TWO of the following capabilities allow for automatic cluster termination and scaling?
⚠ Common exam trap
Candidates often select 'Auto-scaling' and 'Auto-stop' as synonyms, or confuse them with 'Spot Instances'. They fail to recognize these as two distinct cost-saving mechanisms for cluster lifecycle management.
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
✓
Auto-termination
Databricks provides built-in mechanisms to manage compute costs through Auto-termination and Auto-scaling. Auto-termination automatically shuts down clusters after a period of inactivity, preventing wasteful billing. Auto-scaling allows the cluster to resize itself based on the current workload requirements, adding or removing workers dynamically. These features are fundamental for maintaining a cost-efficient data platform while ensuring sufficient performance for varying data volumes during analysis.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Auto-termination
Why this is correct
Auto-termination is a critical cost-saving feature that monitors cluster inactivity. When no jobs are running for a specified period, the cluster automatically terminates, ensuring that users are not billed for compute resources that are idle. This is essential for managing budgets effectively in a shared cloud environment.
- ✓
Auto-scaling
Why this is correct
Auto-scaling dynamically adjusts the number of worker nodes in a cluster based on the incoming workload. This ensures that the system provides optimal performance during peak data processing periods while shrinking the cluster size during quieter times to save on infrastructure costs, maximizing overall resource utilization.
- ✗
Cluster Tagging
Why it's wrong here
Cluster tagging is primarily used for resource organization, cost allocation, and metadata tracking. While it is vital for identifying which department or project incurred costs, it does not provide any automated operational control to scale resources or shut down clusters based on idle time or workload intensity.
- ✗
Delta Cache
Why it's wrong here
The Delta Cache is a performance optimization feature that stores local copies of remote data on worker nodes to reduce I/O latency. It significantly speeds up read-heavy workloads but does not influence the life cycle of the cluster or its dynamic scaling behavior during data processing tasks.
- ✗
Instance Pools
Why it's wrong here
Instance pools are collections of idle, ready-to-use virtual machine instances that reduce cluster start time. While they help in managing startup latency and resource availability, they do not manage the auto-termination of clusters or the dynamic scaling of worker nodes during an active processing job.
About these practice questions
This Databricks-DA-Assoc question is part of Courseiva's 291-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam 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-DA-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-DA-Assoc exam.