Databricks-DA-Assoc Analyzing Queries Practice Question
A data analyst runs an Apache Spark SQL query against a Delta Lake table and notices that partition pruning is not occurring despite filtering on the partitioned column 'region'. The table definition shows 'region' is stored as a string, but the query passes an integer value. How does this type mismatch impact query execution?
⚠ Common exam trap
Candidates overlook data types in filter predicates, assuming implicit casting always preserves partition pruning and optimization efficiencies.
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
✓
Catalyst fails to match directory paths to filter values, forcing a full table scan across all partitions.
Type mismatches between predicate filters and partition columns prevent catalyst optimization from pushing down partition filters. Spark attempts implicit casting, which often invalidates static partition pruning mechanisms, forcing the engine to scan every single data file across all partitions, drastically increasing input data volume and slowing down query execution performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Spark automatically casts the column to match the filter type without impacting performance.
Why it's wrong here
Implicit casting during predicate evaluation occurs at the row level after files are read, meaning partition metadata cannot be leveraged beforehand. The query engine still performs a full table scan of all underlying data files because the directory pruning phase already completed without matching.
- ✗
The query fails immediately with a TypeMismatch analysis exception before any jobs are launched.
Why it's wrong here
Spark SQL and Delta Lake are designed with lenient type coercion rules to facilitate flexible data integration. Instead of failing during the analysis phase, the engine converts the filter expression, allowing execution to proceed but silently disabling vital optimizations like partition pruning.
- ✓
Catalyst fails to match directory paths to filter values, forcing a full table scan across all partitions.
Why this is correct
When data types do not align precisely between the filter predicate and the partition schema definition, the Catalyst optimizer cannot safely evaluate directory paths against static partition filters. Consequently, the query engine bypasses partition pruning and reads all files, leading to significantly degraded performance.
- ✗
Delta Lake automatically rewrites the underlying table schema to match the incoming query filter type.
Why it's wrong here
Delta Lake enforces strict schema enforcement and evolution rules to protect production datasets from accidental corruption. An ad-hoc query filter cannot alter the persistent table schema definition stored in the transaction log, meaning the underlying storage layout and types remain completely unchanged.
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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
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