Databricks-Spark-Assoc Using Spark SQL Practice Question
When executing a Spark SQL query, what does the Catalyst optimizer perform during the 'Analysis' phase?
⚠ Common exam trap
Candidates often confuse the Analysis phase with physical optimization or logical plan generation, forgetting that checking catalog existence happens first.
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
✓
It checks the existence of tables and columns in the catalog.
The Analysis phase is the first step in the query optimization process where Spark resolves identifiers and validates the schema. It checks if tables and columns exist in the catalog and resolves data types. Without this step, Spark would not be able to build a logical plan for the query, as it would not know which data to fetch or how to process the specified columns and tables correctly.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It translates the logical plan into a series of physical execution steps.
Why it's wrong here
This is the 'Planning' or 'Physical Planning' phase, not the 'Analysis' phase. Physical planning is where Spark decides between join strategies, scan methods, and parallelization techniques. The analysis phase is purely concerned with structural correctness and binding the query to the metastore objects before the plan is manipulated.
- ✓
It checks the existence of tables and columns in the catalog.
Why this is correct
The Analysis phase is explicitly responsible for verifying that all referenced objects, such as tables and columns, exist in the underlying metadata catalog. It resolves these names into concrete references, which is a prerequisite for all further optimization and execution steps in the Spark SQL pipeline.
- ✗
It pushes down predicates to the data source to minimize I/O.
Why it's wrong here
Predicate pushdown is an optimization technique applied during the 'Logical Optimization' phase. This phase happens after the initial analysis. While important for performance, it is not part of the initial analysis where the primary goal is validating the SQL code's structural integrity against the existing table schema definitions.
- ✗
It chooses the most efficient join strategy based on table size.
Why it's wrong here
Join strategy selection occurs during physical planning. The engine evaluates cost-based metrics and hints to decide whether to broadcast, shuffle, or sort. The analysis phase is too early for this; it simply confirms the query is valid and that the objects mentioned are actually available in the environment.
Visual reference
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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