DAA-C01 Question #60: Real Exam Question with Answer & Explanation

Question

What role does leveraging native data types play in Snowflake while working with different datasets?

Options

• ALimits data type conversion capabilities
• BReduces query flexibility and optimization possibilities
• CImproves query performance and storage efficiency
• DImpedes data compatibility with other platforms

Explanation

Using native data types in Snowflake (such as VARIANT for semi-structured data, or properly typed numeric/date columns) allows Snowflake's query engine to apply internal optimizations like micro-partition pruning, columnar compression, and vectorized execution - all of which directly boost query performance and reduce storage footprint. This makes C correct.

Why the distractors are wrong:

• A is the opposite of reality - native types expand conversion capabilities by giving the engine precise type information to work with.
• B is also backwards - proper typing increases flexibility for the optimizer, enabling more efficient execution plans.
• D confuses internal optimization with external interoperability; Snowflake's native types map well to standard SQL types and common data formats, not against them.

Memory tip: Think of native data types as "fitting the right key in the right lock" - Snowflake knows exactly how to store and retrieve data when types match, so it works faster and wastes less space. If you squeeze the wrong shape in, everything slows down.

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