Databricks-DE-Assoc Data Transformation and Modeling Practice Question
Exhibit
{
"table": "orders",
"zorder_columns": ["customer_id", "order_date"],
"frequency": "daily",
"status": "PENDING"
}Refer to the exhibit. An engineer notices that queries filtering by 'customer_id' are running slowly on the 'orders' table. Based on the exhibit, what is the most appropriate action to resolve this?
⚠ Common exam trap
Candidates often think that simply defining the Z-Ordering configuration is enough, forgetting that the 'OPTIMIZE' command must be explicitly run to physically reorganize the data 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
✓
Execute 'OPTIMIZE orders ZORDER BY (customer_id, order_date)'.
The exhibit shows that the Z-Ordering configuration is present but the status is 'PENDING', suggesting that an optimization has been defined but not executed. Running the 'OPTIMIZE' command is a standard maintenance task to physically reorganize the data files based on the specified columns. This improves query performance by enabling data skipping for queries filtering on 'customer_id', which is essential for responsive dashboard performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Run the 'VACUUM' command on the 'orders' table immediately.
Why it's wrong here
Vacuuming deletes old data files but does not optimize the physical layout of the data for faster queries. It is a cleanup operation, not a performance optimization command. Running vacuum will not address the slow query performance, as the data layout remains unoptimized for the 'customer_id' filter.
- ✓
Execute 'OPTIMIZE orders ZORDER BY (customer_id, order_date)'.
Why this is correct
The OPTIMIZE command physically reorganizes the table's data files to enable efficient data skipping. By including the ZORDER BY clause, the engine co-locates data with similar values for the specified columns, significantly speeding up queries that filter by these columns, such as the 'customer_id' filter currently causing performance issues.
- ✗
Drop the table and re-ingest the data with a different partition strategy.
Why it's wrong here
Dropping and re-ingesting data is an extreme, expensive, and unnecessary action. The Delta Lake architecture is designed to handle performance tuning on existing data using the OPTIMIZE command. Re-ingestion would cause significant downtime and requires unnecessary compute resources, which is not an appropriate engineering practice for performance tuning.
- ✗
Update the 'spark.sql.shuffle.partitions' setting to 2000.
Why it's wrong here
Increasing shuffle partitions might help with join performance but does not affect the physical layout of the data files on storage. The slow performance here is likely due to the need for data skipping, which can only be achieved by reorganizing the files using the OPTIMIZE command, not by changing shuffle settings.
Visual reference
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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.