Databricks-DA-Assoc Understanding the Databricks Platform Practice Question
A data analyst is troubleshooting a performance issue in a notebook. The query runs slowly when processing a large table. Which approach should the analyst take to improve performance?
⚠ Common exam trap
Candidates often confuse the 'OPTIMIZE' command for compaction with 'VACUUM' for old file removal or cluster-level scaling options, missing that small files directly degrade query performance on Delta tables.
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
✓
Run the 'OPTIMIZE' command on the Delta table.
Performance tuning in Databricks often involves examining how data is stored and accessed. Identifying bottlenecks like small files or lack of partitioning is key. By using built-in optimization commands, analysts can improve query speed significantly. This process is a core skill for any Databricks analyst, as it directly impacts the cost of compute resources and the user experience when working with massive, real-world datasets.
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 number of users connected to the workspace.
Why it's wrong here
Increasing the number of users adds load to the platform and does not improve individual query performance. In fact, it might lead to resource contention, slowing down all users. Performance tuning should focus on optimizing data storage and query logic, not increasing the number of active users.
- ✓
Run the 'OPTIMIZE' command on the Delta table.
Why this is correct
The OPTIMIZE command compacts small files into larger, more efficient files, significantly improving read performance. It is a standard procedure for data analysts to maintain high performance in Delta Lake tables. This simple action can drastically reduce I/O overhead for analytical queries scanning large volumes of data.
- ✗
Delete the table and re-create it without Delta Lake.
Why it's wrong here
Removing Delta Lake would forfeit critical features like ACID compliance and time travel while potentially reducing query performance. Delta Lake is specifically designed to provide superior performance over standard Parquet. Reverting to basic Parquet files would make the data harder to manage and likely slower to query.
- ✗
Force the notebook to run on a single-node cluster.
Why it's wrong here
Single-node clusters are meant for small datasets and development. For large tables, they lack the distributed computing power required to process data in parallel. Forcing a large-scale query onto a single node will lead to memory errors and extremely slow processing speeds, defeating the purpose of using Databricks.
About these practice questions
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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-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.