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Snowflake Certification
COF-C03 Exam
Snowflake.COF-C03.v2026-06-01.q489 Dumps

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Question 256

Which ACCOUNT_USAGE view can be used to identify the masking policy assigned to an object?

A.ACCESS_HISTORY
B.OBJECT_DEPENDENCIES
C.TAG_REFERENCES
D.POLICY_REFERENCES

Correct Answer: D

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Question 257

In Snowpipe, what is the default behavior of the ON_ERROR copy option?

A.CONTINUE
B.SKIP_FILE
C.SKIP_FILE_ < num >
D.ABORT_STATEMENT

Correct Answer: B

The correct answer is B. SKIP_FILE .
Important correction:
The provided answer A. CONTINUE is not correct for Snowpipe.
For regular bulk loading with COPY INTO < table > , the default ON_ERROR behavior is commonly associated with ABORT_STATEMENT. However, Snowpipe has different behavior. In Snowpipe, the default ON_ERROR option is SKIP_FILE .
Why B is correct:
With ON_ERROR = SKIP_FILE, if Snowpipe encounters an error while loading a file, the file is skipped instead of causing the entire continuous loading process to fail.
Why the other options are incorrect:
A). CONTINUE continues loading and skips individual error rows, but it is not the default Snowpipe behavior.
C). SKIP_FILE_ < num > skips a file after a specified number of errors, but it is not the default.
D). ABORT_STATEMENT is associated with standard COPY INTO < table > behavior, not the default Snowpipe behavior.
Official Snowflake documentation reference:
Snowflake documentation for Snowpipe explains that the default ON_ERROR copy option for Snowpipe is SKIP_FILE.
Reference: Snowflake Documentation - Snowpipe; Snowflake Documentation - CREATE PIPE; Snowflake Documentation - COPY INTO < table > copy options; SnowPro Core Study Guide - Data Loading and Unloading.

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Question 258

Which column data type is supported when defining a clustering key on a table?

A.VARCHAR
B.VARIANT
C.OBJECT
D.GEOGRAPHY

Correct Answer: A

The correct answer is A. VARCHAR .
A clustering key can be defined on supported table columns or expressions to help Snowflake organize micro- partitions and improve pruning for certain query patterns. Standard scalar data types such as VARCHAR are supported.
Why A is correct:
VARCHAR is a standard scalar data type and can be used in a clustering key.
Example:
CREATE TABLE customer_orders (
customer_id VARCHAR,
order_date DATE,
amount NUMBER
)
CLUSTER BY (customer_id);
Why the other options are incorrect:
B). VARIANT stores semi-structured data. Clustering directly on raw semi-structured columns is not the best answer here; clustering keys should use supported scalar columns or expressions.
C). OBJECT is a semi-structured data type and is not the correct choice for a clustering key column in this question.
D). GEOGRAPHY is not the correct supported clustering key data type in this context.
Official Snowflake documentation reference:
Snowflake documentation explains that clustering keys can be defined using table columns or expressions, and standard scalar columns such as string, number, and date/time columns are commonly used for clustering.
Reference: Snowflake Documentation - Clustering keys and clustered tables; Snowflake Documentation - Table data types; SnowPro Core Study Guide - Performance Optimization.

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Question 259

Credit charges for Snowflake virtual warehouses are calculated based on which of the following considerations? (Choose two.)

A.The number of queries executed
B.The number of active users assigned to the warehouse
C.The size of the virtual warehouse
D.The length of time the warehouse is running
E.The duration of the queries that are executed

Correct Answer: C,D

Credit charges for Snowflake virtual warehouses are calculated based on the size of the virtual warehouse and the length of time the warehouse is running. The size determines the compute resources available, and charges are incurred for the time these resources are utilized

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Question 260

Which command should be used to load data incrementally based on column values that are specified in the source table or subquery?

A.MERGE
B.COPY INTO
C.GET
D.INSERT INTO

Correct Answer: A

The MERGE command in Snowflake is used for incremental loading based on column values in a source table or subquery. It enables the insertion, updating, or deletion of records in a target table depending on whether matching rows are found, making it ideal for loading data that changes incrementally, such as daily updates or modifications.

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