COF-C03 Practice Question: Performance Optimization, Querying, and Transformation
A data engineer is building a transformation pipeline that processes semi-structured JSON data. The pipeline needs to extract values from nested objects and arrays and output a relational table. The engineer wants to minimize manual coding and ensure the transformation is maintainable. Which Snowflake feature should the engineer use?
⚠ Common exam trap
The trap here is assuming that PARSE_JSON or OBJECT_CONSTRUCT can flatten nested JSON, when they only parse or construct semi-structured values without relational expansion.
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
✓
The FLATTEN function with LATERAL joins to explode arrays and extract nested fields.
FLATTEN is designed to transform semi-structured data by expanding arrays and objects into rows. When combined with LATERAL, it can be applied to each row of a base table, producing a relational output that includes the exploded elements. This approach handles nested structures and is more maintainable than manual string parsing or repeated path expressions. PARSE_JSON only parses text into VARIANT, while OBJECT_CONSTRUCT and TRY_CAST serve different purposes. FLATTEN is the correct feature for relational transformation of JSON.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The PARSE_JSON function to convert the JSON into a relational table automatically.
Why it's wrong here
PARSE_JSON converts a string containing JSON into a VARIANT value; it does not flatten or transform nested structures into relational columns. It is a parsing function, not a transformation tool. The engineer would still need to manually extract and flatten the data after parsing, so this does not minimize coding or provide a relational output directly.
- ✗
The OBJECT_CONSTRUCT function to build a relational schema from the JSON keys.
Why it's wrong here
OBJECT_CONSTRUCT creates a JSON object from key-value pairs. It is used to build semi-structured data, not to transform it into a relational format. Using it here would move in the opposite direction, creating more semi-structured data rather than extracting values into columns. It does not address the need to flatten nested arrays and objects.
- ✗
The TRY_CAST function to coerce the entire JSON column into a relational table.
Why it's wrong here
TRY_CAST attempts to cast a value to a specified data type, returning NULL on failure. It cannot transform an entire JSON column into a relational table with multiple columns and rows. It operates on scalar values, not on nested structures. Using it here would not extract nested fields or explode arrays, so it fails to meet the requirement.
- ✓
The FLATTEN function with LATERAL joins to explode arrays and extract nested fields.
Why this is correct
FLATTEN is a table function that takes a VARIANT column and produces one row per element in an array or per key-value pair in an object. Used with LATERAL, it can explode nested arrays and objects into relational rows. This is the standard Snowflake approach for transforming semi-structured data into a relational format, and it is maintainable and flexible for nested structures.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Snowflake exam blueprint
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