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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 31

Which data formats are supported by Snowflake when unloading semi-structured data? (Select TWO).

Correct Answer: B,D
Snowflake supports a variety of file formats for unloading semi-structured data, among which Parquet and Newline Delimited JSON (NDJSON) are two widely used formats.
* B. Binary file in Parquet: Parquet is a columnar storage file format optimized for large-scale data processing and analysis. It is especially suited for complex nested data structures.
* D. Newline Delimited JSON (NDJSON): This format represents JSON records separated by newline characters, facilitating the storage and processing of multiple, separate JSON objects in a single file.
These formats are chosen for their efficiency and compatibility with data analytics tools and ecosystems, enabling seamless integration and processing of exported data.
References:
* Snowflake Documentation: Data Unloading
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Question 32

Which file function generates a SnowFlake-hosted URL that must be authenticated when used?

Correct Answer: D
Purpose: The BUILD_STAGE_FILE_URL function generates a temporary, pre-signed URL that allows you to access a file within a Snowflake stage (internal or external). This URL requires authentication to use.
Key Points:
Security: The URL has a limited lifespan, enhancing security.
Use Cases: Sharing staged data with external tools or applications, or downloading it directly.
Snowflake Documentation (BUILD_STAGE_FILE_URL): https://docs.snowflake.com/en/sql-reference/functions/build_stage_file_url.html
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Question 33

What MINIMUM permissions are required to create a pipe in Snowflake? (Select TWO).

Correct Answer: A,C
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Question 34

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.
References:
* Snowflake Documentation: Clustering Keys
* Snowflake Documentation: Query Profile
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Question 35

Which is the MINIMUM required Snowflake edition that a user must have if they want to use AWS/Azure Privatelink or Google Cloud Private Service Connect?

Correct Answer: D
https://docs.snowflake.com/en/user-guide/privatelink-azure.html
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