ARA-C01 Performance Optimization Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  Storing all numeric values as VARCHAR.

  Why it's wrong here

  Storing numbers as strings is a significant anti-pattern. It makes range comparisons more computationally expensive, requires more storage, and prevents the engine from leveraging efficient numeric statistics for pruning. This leads to slower scan times and increased I/O compared to using native integer or decimal data types.

• ✓

  Using the smallest appropriate data type.

  Why this is correct

  Smaller, native data types require less space, which means fewer micro-partitions to read. This reduces I/O and speeds up query execution. By choosing the most efficient type, you maximize the amount of data that can be processed per unit of compute, directly enhancing performance for filtering and scan operations.

• ✗

  Storing dates as integers in a single column.

  Why it's wrong here

  While integers are efficient, using native DATE or TIMESTAMP types allows Snowflake to use specialized internal optimizations for date-based pruning. Storing them as custom integers sacrifices the built-in temporal functions and optimizations provided by the engine, which are generally superior to manual integer-based date handling techniques.

• ✗

  Using VARIANT for all columns.

  Why it's wrong here

  VARIANT is meant for semi-structured data. For relational data, it is significantly slower and less efficient than using typed columns. It incurs overhead for parsing and extracting values, which makes filtering much slower. It should never be used as a replacement for structured types in analytical tables.