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  1. Home
  2. Snowflake Certification
  3. COF-C02 Exam
  4. Snowflake.COF-C02.v2026-01-13.q618 Dumps
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Question 496

While unloading data into a stage, how can the user ensure that the output will be a single file?

Correct Answer: B
To ensure that the output will be a single file when unloading data into a stage, you should use the COPY option SINGLE=TRUE. This option specifies that the result of the COPY INTO command should be written to a single file, rather than multiple files.
:
Snowflake Documentation: COPY INTO <location>
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Question 497

Which commands are supported by the query acceleration service? (Select TWO).

Correct Answer: A,E
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Question 498

How can a user change which columns are referenced in a view?

Correct Answer: C
In Snowflake, to change the columns referenced in a view, the view must be recreated with the required changes. The ALTER VIEW command does not allow changing the definition of a view; it can only be used to rename a view, convert it to or from a secure view, or add, overwrite, or remove a comment for a view. Therefore, the correct approach is to drop the existing view and create a new one with the desired column references.
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Question 499

True or false: Snowflake enforces unique, primary key, and foreign key constraints during DML operations.

Correct Answer: B
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Question 500

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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