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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 601

How can a Data Exchange Administrator provide a user with account access to a Data Exchange?

Correct Answer: D
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Question 602

What metadata can be accessed through the INFORMATION_SCHEMA? (Select TWO).

Correct Answer: A,E
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Question 603

When used with a select statement in the copy into <location> command, what statement will unload data from a table as JSON?

Correct Answer: B
The COPY INTO <location> command in Snowflake allows you to export or unload data from a Snowflake table into an external stage (e.g., an Amazon S3 bucket) in a specific format like JSON, CSV, or Parquet.
When you want to unload data as JSON using a SELECT statement, Snowflake provides built-in functions to format the data properly into JSON structures. Among the options provided:
* TO_JSON:
* Converts a variant, object, or array data type into its JSON text representation.
* Commonly used when exporting JSON-formatted data with COPY INTO.
* This is the correct function for generating JSON-formatted output while unloading.
Example Usage:
sql
CopyEdit
COPY INTO @my_stage/unload_data.json
FROM (SELECT TO_JSON(data_column) FROM my_table)
FILE_FORMAT = (TYPE = JSON);
* Why the Other Options Are Incorrect:
* TO_OBJECT: This function does not exist in Snowflake.
* PARSE_JSON: This is used to parse a string into a variant data type representing a JSON object but is not used for exporting or creating JSON.
* OBJECT_CONSTRUCT: Creates a JSON object from key-value pairs but does not convert data into JSON format for unloading.
SnowPro References:
* COPY INTO Command Documentation
* TO_JSON Function
* Working with JSON Data in Snowflake
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Question 604

How long is a query visible in the Query History page in the Snowflake Web Interface (Ul)?

Correct Answer: A
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Question 605

What does a table with a clustering depth of 1 mean in Snowflake?

Correct Answer: C
In Snowflake, a table's clustering depth indicates the degree of micro-partition overlap based on the clustering keys defined for the table. A clustering depth of 1 implies that the table has no overlapping micro-partitions. This is an optimal scenario, indicating that the table's data is well-clustered according to the specified clustering keys. Well-clustered data can lead to more efficient query performance, as it reduces the amount of data scanned during query execution and improves the effectiveness of data pruning.
Reference:
Snowflake Documentation on Clustering: Understanding Clustering Depth
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