Snowflake COF-C02 Practice Test - Questions Answers, Page 48

Snowflake's architecture is unique in that it combines elements of both traditional shared-disk and shared-nothing database architectures. This hybrid approach allows Snowflake to offer the scalability and performance benefits of a shared-nothing architecture (with compute and storage separated) while maintaining the simplicity and flexibility of a shared-disk architecture in managing data across all nodes in the system. This results in an architecture that provides on-demand scalability, both vertically and horizontally, without sacrificing performance or data cohesion.

References:

Snowflake Documentation: Snowflake Architecture

The search optimization service in Snowflake supports use cases involving conjunctions (AND) of multiple equality predicates. This service enhances the performance of queries that include multiple equality conditions by utilizing search indexes to quickly filter data without scanning entire tables or partitions. It's particularly beneficial for improving the response times of complex queries that rely on specific data matching across multiple conditions.

References:

Snowflake Documentation: Search Optimization Service

Transient tables in Snowflake are designed for temporary or intermediate workloads with the following characteristics:

B . Transient tables can be cloned to permanent tables: This feature allows users to create copies of transient tables for permanent use, providing flexibility in managing data lifecycles.

C . Transient tables persist until they are explicitly dropped: Unlike temporary tables that exist for the duration of a session, transient tables remain in the database until explicitly removed by a user, offering more durability for short-term data storage needs.

References:

Snowflake Documentation: Transient Tables

When a Snowflake user increases the size of a suspended virtual warehouse, the changes to compute resources are queued but do not take immediate effect. The provisioning of additional compute resources occurs only when the warehouse is resumed. This ensures that resources are allocated efficiently, aligning with Snowflake's commitment to cost-effective and on-demand scalability.

References:

Snowflake Documentation: Virtual Warehouses

To obtain the most detailed information about individual table storage details in Snowflake, the TABLE STORAGE METRICS view is the recommended option. This view provides comprehensive metrics on storage usage, including data size, time travel size, fail-safe size, and other relevant storage metrics for each table. This level of detail is invaluable for monitoring, managing, and optimizing storage costs and performance.

References:

Snowflake Documentation: Information Schema

Even if a developer is granted ownership of a table with a masking policy, they will not be able to modify the table to read the masked data if their role does not have the necessary permissions. Ownership of a table does not automatically confer the ability to alter masking policies, which are designed to protect sensitive data.Masking policies are applied at the schema level and require specific privileges to modify12.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Masking Policies

Clustering keys in Snowflake work by sorting the designated columns over time. This process is done in the background and does not block data manipulation language (DML) operations, allowing for normal database operations to continue without interruption.The purpose of clustering keys is to organize the data within micro-partitions to optimize query performance1.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Clustering1

Data Robot is recognized as a machine learning and data science partner within the Snowflake Partner Ecosystem. It provides an enterprise AI platform that enables users to build and deploy accurate predictive models quickly.As a partner, Data Robot integrates with Snowflake to enhance data science capabilities2.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Machine Learning & Data Science Partners

https://docs.snowflake.com/en/user-guide/ecosystem-analytics.html

In Snowflake, when the account is located on Microsoft Azure, a valid source for an external stage can be an Azure container or a folder path within an Azure container. This includes Azure Blob storage which is accessible via theazure://endpoint. A Windows server file share on Azure, if configured properly, can be a valid source for staging data files for Snowflake.Options A, B, and C are not supported as direct sources for an external stage in Snowflake on Azure12.References:[COF-C02] SnowPro Core Certification Exam Study Guide