Databricks-DE-Assoc Data Transformation and Modeling Practice Question
Exhibit
{
"table": "orders",
"partition_columns": ["order_date"],
"clustering": "none",
"zorder": "none",
"file_size": "small"
}Refer to the exhibit. A data engineer is reviewing the configuration of a Delta table that is frequently queried by 'customer_id'. Given the current metadata, which action will provide the most significant improvement to query performance?
⚠ Common exam trap
Candidates often choose partitioning or indexing, but in Delta Lake, Z-Ordering is the specific technique to improve data skipping for high-cardinality columns like 'customer_id'.
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 OPTIMIZE with ZORDER BY (customer_id)
The table is currently suffering from poor data layout due to the small file size and lack of clustering on the frequently queried 'customer_id' column. Performing an OPTIMIZE operation with Z-Ordering on 'customer_id' will cluster the data physically on disk based on that key. This enables the engine to perform data skipping, reducing the amount of data scanned and significantly speeding up point lookups and aggregations by customer.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Execute VACUUM to remove old files
Why it's wrong here
VACUUM is used to clean up expired data files that are no longer referenced by the Delta log. While it helps with storage costs and compliance, it does not improve query performance or data layout, as it only removes files that are already obsolete and not part of the active table state.
- ✓
Run OPTIMIZE with ZORDER BY (customer_id)
Why this is correct
Z-Ordering by 'customer_id' reorganizes the data into files that share similar customer IDs. This allows the query engine to skip entire files that do not contain the requested ID, making the 'orders' table queries significantly faster, especially when combined with the file compaction that occurs during the optimization process.
- ✗
Re-partition the table by 'customer_id'
Why it's wrong here
Re-partitioning by 'customer_id' is generally a bad practice because 'customer_id' typically has very high cardinality. This would create a massive number of partitions, leading to metadata overhead and the small file problem, which would severely degrade the performance of any write or read operation on the Delta table.
- ✗
Increase the 'order_date' partition size
Why it's wrong here
Changing partition sizes for 'order_date' does not address the query performance bottleneck related to filtering by 'customer_id'. Partitioning is effective for filters on the partition column itself, but it does nothing to help with queries filtering on non-partition columns like 'customer_id', which require different optimization strategies like Z-Ordering.
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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.