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  1. Home
  2. Snowflake Certification
  3. COF-C02 Exam
  4. Snowflake.COF-C02.v2026-03-16.q834 Dumps
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Question 591

Which Snowflake function will parse a JSON-null into a SQL-null?

Correct Answer: D
The STRIP_NULL_VALUE function in Snowflake is used to convert a JSON null value into a SQL NULL value1.
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Question 592

What situation is likely to cause data spillage when a query is run?

Correct Answer: A
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Question 593

When cloning a database, what is cloned with the database? (Choose two.)

Correct Answer: A,B
When cloning a database in Snowflake, the clone includes all privileges on the database as well as existing child objects within the database, such as schemas, tables, views, etc. However, it does not include future child objects or privileges on schemas within the database2.
References = [COF-C02] SnowPro Core Certification Exam Study Guide, Snowflake Documentation
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Question 594

Which function can be used with the copy into <location> statement to convent rows from a relational table to a single variant column, and to unload rows into a JSON file?

Correct Answer: D
The correct function to use with the COPY INTO <location> statement to convert rows from a relational table into a single variant column and to unload rows into a JSON file is TO VARIANT. The TO VARIANT function is used to explicitly convert a value of any supported data type into a VARIANT data type. This is particularly useful when needing to aggregate multiple columns or complex data structures into a single JSON-formatted string, which can then be unloaded into a file.
In the context of unloading data, the COPY INTO <location> statement combined with TO VARIANT enables the conversion of structured data from Snowflake tables into a semi-structured VARIANT format, typically JSON, which can then be efficiently exported and stored. This approach is often utilized for data integration scenarios, backups, or when data needs to be shared in a format that is easily consumed by various applications or services that support JSON.
Reference:
Snowflake Documentation on Data Unloading: Unloading Data
Snowflake Documentation on VARIANT Data Type: Working with JSON
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Question 595

A query containing a WHERE clause is running longer than expected. The Query Profile shows that all micro-partitions being scanned How should this query be optimized?

Correct Answer: B
When a query containing a WHERE clause is running longer than expected, and the Query Profile shows that all micro-partitions are being scanned, the query can be optimized by adding a clustering key to the table.
Understanding Micro-Partitioning in Snowflake:
Snowflake automatically partitions tables into micro-partitions for efficient storage and query performance.
Each micro-partition contains metadata about the range of values it holds, which helps in pruning irrelevant partitions during query execution.
Role of Clustering Keys:
A clustering key defines how data in a table is organized within micro-partitions.
By specifying a clustering key, you can control the physical layout of data, ensuring that related rows are stored together.
This organization improves query performance by reducing the number of micro-partitions that need to be scanned.
Optimizing Queries with Clustering Keys:
Adding a clustering key based on columns frequently used in WHERE clauses helps Snowflake quickly locate and scan relevant micro-partitions.
This minimizes the amount of data scanned and reduces query execution time.
Example:
ALTER TABLE my_table CLUSTER BY (column1, column2);
This command adds a clustering key to my_table using column1 and column2.
Future queries that filter on these columns will benefit from improved performance.
Benefits:
Reduced query execution time: Fewer micro-partitions need to be scanned.
Improved resource utilization: More efficient data retrieval leads to lower compute costs.
Reference:
Snowflake Documentation: Clustering Keys
Snowflake Documentation: Query Profile
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