Databricks-DA-Assoc Analyzing Queries Practice Question
An analyst is optimizing a dashboard that queries a Delta table partitioned by 'event_date'. The query filters on 'event_date' and 'user_id', but is still slow. What is the most effective next step to improve query speed?
⚠ Common exam trap
Candidates often suggest adding more compute or changing partition columns. They fail to realize that Z-Ordering or Liquid Clustering is required to optimize high-cardinality columns not used in partitioning.
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
✓
Enable Liquid Clustering on the 'user_id' column.
Partitioning by date is good for coarse pruning, but if 'user_id' is the primary filter, the engine still scans all files within the selected dates. Z-Ordering by 'user_id' creates data skipping metadata that allows the engine to skip files not containing the specific user, drastically reducing data read volume. This is a critical optimization for high-cardinality columns used in frequent point-lookups.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable Liquid Clustering on the 'user_id' column.
Why this is correct
Liquid Clustering allows the table to automatically organize data based on query patterns, including 'user_id'. It replaces traditional partitioning and Z-Ordering with a more flexible, adaptive approach that improves performance for high-cardinality filters by clustering relevant data together without requiring manual management of partition columns or Z-Order schemes.
- ✗
Change the table format to Parquet to improve scan performance.
Why it's wrong here
Delta Lake is built on top of Parquet, so switching to Parquet would lose the ACID, schema enforcement, and time-travel benefits of Delta. Furthermore, raw Parquet files lack the specialized indexing and metadata skipping features that Delta tables use to optimize query performance in the Databricks environment.
- ✗
Increase the cluster's driver node memory.
Why it's wrong here
Increasing driver memory rarely helps with query performance unless the query is experiencing 'Out of Memory' errors during the planning or result collection phases. Query speed is primarily determined by worker node performance and data access patterns, which are addressed by optimizing the storage layout of the data.
- ✗
Rewrite the query to use a subquery instead of a JOIN.
Why it's wrong here
Query rewrite rarely addresses the root cause of slow scans. If the engine is reading too much data from disk because the data is not physically organized to support the 'user_id' filter, changing the SQL syntax will not reduce the amount of I/O required to satisfy the request.
About these practice questions
This Databricks-DA-Assoc question is part of Courseiva's 291-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.