Free Practice Questions for Snowflake COF-C03 Exam

Answer : B

Answer : B

When using the SAMPLE clause in a query, if the seed is not set, Snowflake will use a different random seed for each execution of the query. This results in different rows being sampled each time, leading to different results. Setting a seed ensures that the same rows are sampled each time the query is run.

:

Snowflake Documentation: Sampling

Answer : D

The function used to determine the kind of value stored in a VARIANT column in Snowflake is TYPEOF.

Understanding VARIANT Data Type:

VARIANT is a flexible data type in Snowflake that can store semi-structured data, such as JSON, Avro, and XML.

This data type can hold values of different types, including strings, numbers, objects, arrays, and more.

Using TYPEOF Function:

The TYPEOF function returns the data type of the value stored in a VARIANT column.

It helps in identifying the type of data, which is crucial for processing and transforming semi-structured data accurately.

Example Usage:

SELECT TYPEOF(variant_column)

FROM my_table;

This query retrieves the type of data stored invariant_columnfor each row inmy_table.

Possible return values include 'OBJECT', 'ARRAY', 'STRING', 'NUMBER', etc.

Benefits:

Simplifies data processing: Knowing the data type helps in applying appropriate transformations and validations.

Enhances query accuracy: Ensures that operations on VARIANT columns are performed correctly based on the data type.

:

Snowflake Documentation: TYPEOF

Snowflake Documentation: VARIANT Data Type

Answer : C

When attempting to load large files, such as a 125 GB file, into Snowflake using SnowSQL, the process might encounter limitations related to the maximum execution time for queries or data loading operations. If the loading process exceeds this time limit (typically around 24 hours), it could be aborted without committing any part of the file to the database. This behavior is designed to prevent indefinite resource consumption and to maintain system stability, emphasizing the need for optimizing data load operations, possibly through file segmentation or parallel loading strategies.

Answer : A

The correct answer is A. The table data does not require Fail-safe.

Transient tables are designed for data that does not need the same level of data recovery protection as permanent tables. Unlike permanent tables, transient tables do not have a Fail-safe period.

Why A is correct:

Fail-safe storage can add cost because Snowflake retains historical data for recovery by Snowflake support after the Time Travel retention period ends. Since transient tables do not have Fail-safe, they can reduce storage costs for data that does not require this additional protection.

Common use cases include:

Temporary ETL data

Staging data

Intermediate transformation data

Reproducible data

Non-critical working data

Why the other options are incorrect:

B . Long-term critical storage is usually better suited for permanent tables because they support Fail-safe.

C . Frequent access and modification alone does not make a transient table the best cost choice. The key factor is whether Fail-safe is needed.

D . Sharing data with external accounts is not the primary cost-effective use case for transient tables.

Official Snowflake documentation reference:

Snowflake documentation explains that transient tables persist until explicitly dropped, support Time Travel with limited retention, and do not have a Fail-safe period.

========================