Databricks-DA-Assoc Analyzing Queries Practice Question
A data analyst is troubleshooting a slow-running SQL query against a massive Delta table in Databricks. The query frequently scans the entire table despite filtering on a high-cardinality timestamp column. Which approach will most effectively reduce the data scanned by eliminating full-table reads?
⚠ Common exam trap
Candidates often suggest partitioning by high-cardinality columns like timestamps, which is a major anti-pattern that creates too many small files and metadata overhead, drastically hurting performance.
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 an OPTIMIZE command with a ZORDER BY clause on the timestamp column to co-locate related data and improve data skipping.
Z-Ordering co-locates related data based on specified columns, significantly improving data skipping for queries with equality or range filters. When combined with correct partitioning, it minimizes the amount of data scanned from cloud storage, drastically reducing query latency and execution costs in Databricks environments.
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 an OPTIMIZE command with a ZORDER BY clause on the timestamp column to co-locate related data and improve data skipping.
Why this is correct
Z-Ordering clusters data with similar values into the same file spaces. This enables the Delta Lake file-skipping mechanism to bypass irrelevant files entirely when queries apply range filters on the target timestamp column, directly lowering overall scan volume and query duration.
- ✗
Increase the cluster size to a driver instance with more memory to hold the entire uncompressed Delta table in cache.
Why it's wrong here
Scaling up cluster memory does not address the fundamental I/O bottleneck caused by scanning unindexed and unclustered data files from cloud object storage. Query performance remains poor because the underlying data layout still requires reading all historical partitions into memory.
- ✗
Execute a VACUUM command with a retention threshold of zero hours to purge old data files immediately.
Why it's wrong here
VACUUM removes unreferenced old data files after the retention threshold; it does not reorganise data for read pruning, so full scans on the timestamp filter persist. It tempts because it reduces storage footprint, but the correct fix is liquid clustering or partitioning on that column to enable file skipping.
- ✗
Convert the Delta table format to standard Parquet files to take advantage of native Apache Spark partitioning.
Why it's wrong here
Delta Lake is built on top of Parquet files and adds transaction logs, statistics, and advanced optimization features like Z-Ordering. Reverting to standard Parquet removes these built-in metadata capabilities, degrading overall query planning and update performance.
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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.