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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 661

Which type of workload is recommended for Snowpark-optimized virtual warehouses?

Correct Answer: B
Snowpark-optimized virtual warehouses in Snowflake are designed to efficiently handle workloads with large memory requirements. Snowpark is a developer framework that allows users to write code in languages like Scala, Java, and Python to process data in Snowflake. Given the nature of these programming languages and the types of data processing tasks they are typically used for, having a virtual warehouse that can efficiently manage large memory-intensive operations is crucial.
* Understanding Snowpark-Optimized Virtual Warehouses:
* Snowpark allows developers to build complex data pipelines and applications within Snowflake using familiar programming languages.
* These virtual warehouses are optimized to handle the execution of Snowpark workloads, which often involve large datasets and memory-intensive operations.
* Large Memory Requirements:
* Workloads with large memory requirements include data transformations, machine learning model training, and advanced analytics.
* These operations often need to process significant amounts of data in memory to perform efficiently.
* Snowpark-optimized virtual warehouses are configured to provide the necessary memory resources to support these tasks, ensuring optimal performance and scalability.
* Other Considerations:
* While Snowpark can handle other types of workloads, its optimization for large memory tasks makes it particularly suitable for scenarios where data processing needs to be done in-memory.
* Snowflake's ability to scale compute resources dynamically also plays a role in efficiently managing large memory workloads, ensuring that performance is maintained even as data volumes grow.
References:
* Snowflake Documentation: Introduction to Snowpark
* Snowflake Documentation: Virtual Warehouses
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Question 662

What does the TableScan operator represent in the Query Profile?

Correct Answer: A
In the Query Profile of Snowflake, the TableScan operator represents the access to a single table. This operator indicates that the query execution involved reading data from a table stored in Snowflake. TableScan is a fundamental operation in query execution plans, showing how the database engine retrieves data directly from tables as part of processing a query.
Reference:
Snowflake Documentation: Understanding the Query Profile
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Question 663

Which of the following are benefits of micro-partitioning? (Select TWO)

Correct Answer: B,C
Micro-partitions in Snowflake are immutable objects, which means once they are written, they cannot be modified. This immutability supports the use of Time Travel, allowing users to access historical data within a defined period. Additionally, micro-partitions can significantly reduce the amount of I/O from object storage to virtual warehouses. This is because Snowflake's query optimizer can skip over micro-partitions that do not contain relevant data for a query, thus reducing the amount of data that needs to be scanned and transferred.
Reference:
https://docs.snowflake.com/en/user-guide/tables-clustering-micropartitions.html
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Question 664

If 3 size Small virtual warehouse is made up of two servers, how many servers make up a Large warehouse?

Correct Answer: B
In Snowflake, each size increase in virtual warehouses doubles the number of servers. Therefore, if a size Small virtual warehouse is made up of two servers, a Large warehouse, which is two sizes larger, would be made up of eight servers (2 servers for Small, 4 for Medium, and 8 for Large)2.
Size specifies the amount of compute resources available per cluster in a warehouse. Snowflake supports the following warehouse sizes:

https://docs.snowflake.com/en/user-guide/warehouses-overview.html
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Question 665

Which command is used to remove files from either external cloud storage or an internal staged

Correct Answer: B
The REMOVE command in Snowflake is used to delete files from either external cloud storage locations or internal stages. This command helps manage staged files by removing them when they are no longer needed, which is useful for maintaining organization and managing storage costs.
Reference:
Snowflake Documentation: REMOVE Command
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