COF-C03 Practice Question: Performance Optimization, Querying, and Transformation
A data engineer needs to transform semi-structured JSON data stored in a VARIANT column into a relational table with separate columns for each attribute. The JSON contains nested objects and arrays. Which Snowflake feature should the engineer use to flatten the arrays and extract the nested attributes in a single SQL statement?
⚠ Common exam trap
It's easy for candidates to confuse PARSE_JSON, which only converts strings to VARIANT, with FLATTEN, which actually explodes arrays into rows.
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
✓
LATERAL FLATTEN with the INPUT => column and PATH => 'nested_array' arguments, combined with the VALUE and THIS keywords.
LATERAL FLATTEN is the primary Snowflake construct for expanding arrays within a VARIANT column. It produces one row per element in the array, and by using the VALUE or THIS keyword, the engineer can access the element's attributes. Combining this with a SELECT that references the original table's columns and the flattened output allows a single SQL statement to transform nested JSON into a relational result set.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create a materialized view over the VARIANT column and query it with standard SQL.
Why it's wrong here
Materialized views in Snowflake cannot be created directly over VARIANT columns with complex nested arrays, and they do not automatically flatten data. They are used for pre-computed aggregations or filters on relational data. While a materialized view can improve performance for repeated queries, it does not transform semi-structured data into a relational format or handle array flattening.
- ✗
Use the TRY_CAST function to convert the VARIANT to a string and then use string functions to parse the JSON.
Why it's wrong here
TRY_CAST converts a VARIANT to a string, but then the engineer would need to manually parse the JSON string using string functions, which is error-prone and does not handle nested arrays or objects natively. Snowflake provides native semi-structured functions like LATERAL FLATTEN and the colon operator for this purpose. Using string functions would lose the benefits of Snowflake's optimized VARIANT handling and is not recommended.
- ✓
LATERAL FLATTEN with the INPUT => column and PATH => 'nested_array' arguments, combined with the VALUE and THIS keywords.
Why this is correct
LATERAL FLATTEN is specifically designed to explode arrays and nested objects within a VARIANT column into multiple rows. Using INPUT to specify the VARIANT column, PATH to target the nested array, and VALUE or THIS to reference the exploded elements allows the engineer to join the flattened output back to the original row and extract attributes with the colon operator. This is the standard Snowflake approach for relationalizing semi-structured data.
- ✗
Use the PARSE_JSON function to convert the VARIANT into a relational table automatically.
Why it's wrong here
PARSE_JSON converts a string into a VARIANT, but it does not flatten arrays or create a relational table. It is used to ingest JSON strings into Snowflake, not to transform nested structures into columns. The engineer would still need to manually extract each attribute and handle arrays separately. This function does not provide the row explosion needed for nested arrays.
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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 Snowflake exam blueprint
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