Databricks-DE-Assoc Troubleshooting, Monitoring, and Optimization Practice Question
Exhibit
spark.conf.set("spark.databricks.io.cache.enabled", "true")
spark.conf.set("spark.databricks.io.cache.maxDiskUsage", "50g")Refer to the exhibit. An engineer applies these configurations to a cluster. What is the primary benefit of enabling the Databricks IO Cache for a workload that involves repeatedly reading the same Delta tables?
⚠ Common exam trap
Candidates often confuse the Databricks IO Cache with Spark RDD caching (cache() or persist()). They assume it applies to memory-based caching of DataFrames rather than disk-based caching of storage files.
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
✓
It speeds up repeated reads by caching data on local SSDs.
The Databricks IO Cache (also known as the disk cache) accelerates data reads by caching remote data on the local SSDs of the worker nodes. For workloads that frequently query the same tables, this eliminates the latency and network overhead of repeatedly fetching data from cloud object storage. This is particularly effective for read-heavy analytical workloads, enabling significantly faster query execution times by leveraging high-speed local disk I/O.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It enables ACID transactions for non-Delta tables.
Why it's wrong here
The IO cache is strictly for performance optimization and does not provide ACID transactional capabilities. ACID guarantees are provided by the Delta Lake protocol and the transaction log, regardless of whether the IO cache is enabled or which storage format is currently being utilized by the Spark job.
- ✓
It speeds up repeated reads by caching data on local SSDs.
Why this is correct
The Databricks IO cache stores frequently accessed data on the local SSDs of the worker nodes. When the same data is needed for subsequent queries, Spark retrieves it from the local cache rather than the cloud storage, drastically reducing latency and increasing overall query throughput for repeated read workloads.
- ✗
It automatically scales the cluster based on disk usage.
Why it's wrong here
The IO cache configuration parameters relate to the local storage allocation for caching, not the cluster's auto-scaling logic. Auto-scaling is governed by cluster-level policies that monitor CPU or memory usage to add or remove nodes, and it remains independent of the settings for the local data cache.
- ✗
It forces the cluster to store all data in memory.
Why it's wrong here
The IO cache uses local disk (SSD), not RAM. While Spark has an RDD caching mechanism that uses RAM, the Databricks IO cache specifically targets local disk storage to persist data across query executions. Confusing disk-based caching with memory-based caching leads to incorrect expectations regarding performance and memory usage.
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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.