Databricks-Spark-Assoc Using Spark SQL Practice Question
What is the purpose of the 'ANALYZE TABLE' command in Databricks?
⚠ Common exam trap
Many test-takers think ANALYZE TABLE actually cleans up storage, optimizes file sizes, or runs data quality validation checks on the table.
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
✓
To generate statistics used by the optimizer for query planning.
The ANALYZE TABLE command collects table statistics, such as row counts and column distributions. This information is crucial for the Spark Catalyst optimizer to make informed decisions about join strategies, such as when to broadcast a table or how to order joins. Without accurate statistics, the optimizer might choose inefficient execution plans, leading to degraded performance in complex multi-join queries.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
To remove unused files and clean up storage.
Why it's wrong here
That is the purpose of the VACUUM command. VACUUM is a data management tool for storage optimization, while ANALYZE TABLE is a metadata optimization tool. Confusing these two commands can lead to mismanaging cluster resources or failing to improve query performance as intended for the specific task.
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To update the table schema after adding a column.
Why it's wrong here
Schema evolution is handled automatically by Delta Lake when you use the ALTER TABLE or write with schema enforcement enabled. ANALYZE TABLE does not modify the table structure or schema; it only reads the data to compute statistics used for query planning, not for structural metadata management.
- ✓
To generate statistics used by the optimizer for query planning.
Why this is correct
ANALYZE TABLE computes statistics like count, min, max, and null counts for columns. The cost-based optimizer uses this data to decide the best join strategy. This is a best practice in Databricks for any table that is frequently used in complex SQL queries to ensure optimal execution paths.
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To verify the integrity of the Delta log.
Why it's wrong here
The RESTORE command or the Delta consistency check utilities are used for validating and repairing the Delta log. ANALYZE TABLE does not perform any validation or repair operations; it is strictly an informational command intended to improve query planning, not to ensure or restore transactional data integrity.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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