20+ practice questions focused on Analyzing Queries — one of the most tested topics on the Databricks Certified Data Analyst Associate exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Analyzing Queries PracticeAn analyst notices that a specific query is slow because it performs a full table scan instead of using the intended index. Which command should the analyst check to ensure the optimizer has the necessary information to choose the correct plan?
Explanation: In Databricks, the command to collect statistics for the cost-based optimizer is 'ANALYZE TABLE table_name COMPUTE STATISTICS'. However, the stem's premise regarding 'indexes' is misleading for Databricks, as it relies on Z-Ordering or Data Skipping rather than traditional indexes. Furthermore, the command 'ANALYZE TABLE' is often deprecated in favor of 'ANALYZE TABLE table_name COMPUTE STATISTICS FOR COLUMNS'.
A data analyst is investigating a slow Databricks SQL query that joins two large Delta tables. The query profile shows a SortMergeJoin with two Exchange nodes, and each Exchange shuffles over 1 TB of data. The analyst wants to reduce shuffle. Which approach is most likely to improve performance?
Explanation: Bucketing both tables on the join key with the same number of buckets allows Spark to perform a sort-merge join without exchanging data across the network. This eliminates the two Exchange nodes and the associated 1 TB shuffles, significantly improving performance. Other options either do not eliminate shuffle or are not applicable when both tables are large.
A data analyst uses a Databricks SQL dashboard that refreshes every 15 minutes. One query reads from a large Delta table, and the analyst notices in the Query Profile that the scan operator reads far more bytes than the rows the final result returns, even though the WHERE clause filters on a low-cardinality status column. The analyst wants to reduce bytes scanned without changing the result. Which action is most appropriate?
Explanation: Because the filter targets a low-cardinality column, file-level skipping and partitioning both leave large amounts of data to scan. A materialized view precomputes the filtered or aggregated result so the recurring dashboard query reads a compact table, preserving the result while sharply reducing bytes scanned on each refresh.
A data analyst runs a query that includes a subquery in the WHERE clause: SELECT * FROM orders WHERE customer_id IN (SELECT customer_id FROM customers WHERE region = 'North'). The query is slow. The analyst wants to rewrite the query to improve performance. Which approach is most likely to be more efficient?
Explanation: Rewriting the IN subquery as an INNER JOIN with a filter on region makes the join explicit, enabling Spark to apply optimizations like broadcast join if the customers table is small. This often results in a more efficient execution plan than a subquery, which may be evaluated separately.
A data analyst is examining the Databricks SQL Query Profile for a query that is slow due to a join. The analyst wants to identify whether the join is causing a shuffle and whether data skew is present. Which two metrics should the analyst examine? (Choose two.)
Explanation: Shuffle metrics (rows and bytes) directly show whether data is being exchanged and how much, confirming a shuffle-based join. Peak memory usage per task helps identify skew: if some tasks use much more memory, they are likely processing more data due to skewed keys. Together, these metrics diagnose the two issues.
+15 more Analyzing Queries questions available
Practice all Analyzing Queries questions1. Baseline your knowledge
Start with 10 questions to gauge your current understanding of Analyzing Queries. This tells you whether you need a concept refresher or just practice.
2. Review every explanation
For each question — right or wrong — read the full explanation. Understanding why an answer is correct is more valuable than knowing the answer itself.
3. Focus on exam traps
Analyzing Queries questions on the Databricks-DA-Assoc frequently use trap wording. Look for subtle differences in answers that test your precision, not just general knowledge.
4. Reach 80% consistently
Do repeated sessions until you score 80%+ three times in a row. Then move to mixed-mode practice to test cross-topic recall under realistic conditions.
The exact number varies per candidate. Analyzing Queries is tested as part of the Databricks Certified Data Analyst Associate blueprint. Practicing with targeted Analyzing Queries questions ensures you can handle any format or difficulty that appears.
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Difficulty is subjective, but Analyzing Queries is a high-priority exam concept tested in multiple ways — direct recall, scenario analysis, and command-output interpretation. Consistent practice is the best way to build confidence.
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