Snowflake COF-C02 Practice Test - Questions Answers, Page 43

first run SHOW FILE FORMATS

then SELECT * FROM TABLE(RESULT_SCAN(LAST_QUERY_ID(-1)))

https://docs.snowflake.com/en/sql-reference/functions/result_scan#usage-notes

The function that provides access to download unstructured data from a Snowflake stage is:

C . GET_PRESIGNED_URL: This function generates a presigned URL for a single file within a stage. The generated URL can be used to directly access or download the file without needing to go through Snowflake. This is particularly useful for unstructured data such as images, videos, or large text files, where direct access via a URL is needed outside of the Snowflake environment.

Example usage:

SELECT GET_PRESIGNED_URL('stage_name', 'file_path');

This function simplifies the process of securely sharing or accessing files stored in Snowflake stages with external systems or users.

References:

Snowflake Documentation: GET_PRESIGNED_URL Function at Snowflake Documentation

A multi-cluster virtual warehouse in Snowflake is designed to handle high concurrency and workload demands by allowing multiple clusters of compute resources to operate simultaneously. The correct scenario to use a multi-cluster virtual warehouse is:

A . When queuing is delaying query execution on the warehouse: Multi-cluster warehouses are ideal when the demand for compute resources exceeds the capacity of a single cluster, leading to query queuing. By enabling additional clusters, you can distribute the workload across multiple compute clusters, thereby reducing queuing and improving query performance.

This is especially useful in scenarios with fluctuating workloads or where it's critical to maintain low response times for a large number of concurrent queries.

References:

Snowflake Documentation: Multi-Cluster Warehouses at Snowflake Documentation

In Snowflake, when accessing elements within a JSON object stored in a variant column, the correct syntax involves using a colon (:) to navigate the JSON structure. The BIKEID attribute, which is a child of the BIKE root node in the JSON object, is accessed using data:BIKE.BIKEID. This syntax correctly references the path through the JSON object, utilizing the colon for JSON field access and dot notation to traverse the hierarchy within the variant structure. References: Snowflake documentation on accessing semi-structured data, which outlines how to use the colon and dot notations for navigating JSON structures stored in variant columns.

For data batch processing in Snowflake, the recommended tool is:

B . SnowSQL: SnowSQL is the command-line client for Snowflake. It allows for executing SQL queries, scripts, and managing database objects. It's particularly suitable for batch processing tasks due to its ability to run SQL scripts that can execute multiple commands or queries in sequence, making it ideal for automated or scheduled tasks that require bulk data operations.

SnowSQL provides a flexible and powerful way to interact with Snowflake, supporting operations such as loading and unloading data, executing complex queries, and managing Snowflake objects from the command line or through scripts.

References:

Snowflake Documentation: SnowSQL (CLI Client) at Snowflake Documentation

The Snowflake Search Optimization Service is designed to enhance the performance of specific types of queries on large tables. The correct answer is:

Snowpipe is Snowflake's continuous data ingestion service that allows for loading data as soon as it's available in a cloud storage stage. Snowpipe uses compute resources managed by Snowflake, separate from the virtual warehouses that users create for querying data. This means that Snowpipe operations do not consume the computational credits of user-created virtual warehouses, offering an efficient and cost-effective way to continuously load data into Snowflake.

References:

Snowflake Documentation: Understanding Snowpipe

Data sharing in Snowflake allows for live, read-only access to data across different Snowflake accounts without the need to copy or transfer the data. One of the characteristics of data shares is the ability to use secure views. Secure views are used within data shares to restrict the access of shared data, ensuring that consumers can only see the data that the provider intends to share, thereby preserving privacy and security.

References:

Snowflake Documentation: Understanding Secure Views in Data Sharing

In Snowflake, an inbound data share refers to the data shared with an account by another account. The only DDL/DML operation allowed on an inbound data share is SELECT. This restriction ensures that the shared data remains read-only for the consuming account, maintaining the integrity and ownership of the data by the sharing account.

References:

Snowflake Documentation: Using Data Shares