Vce SnowPro-Core Questions Latest

 Snowflake’s architecture is designed to handle high concurrency workloads through several features, two of which are particularly effective:

• B. The use of warehouse auto scaling: This feature allows Snowflake to automatically adjust the compute resources allocated to a virtual warehouse in response to the workload. If there is an increase in concurrent queries, Snowflake can scale up the resources to maintain performance.
• D. Use of multi-clustered warehouses: Multi-clustered warehouses enable Snowflake to run multiple clusters of compute resources simultaneously. This allows for the distribution of queries across clusters, thereby reducing the load on any single cluster and improving the system’s ability to handle a high number of concurrent queries.

These features ensure that Snowflake can manage varying levels of demand without manual intervention, providing a seamless experience even during peak usage.

References:

• Snowflake Documentation on Virtual Warehouses
• SnowPro® Core Certification Study Guide

 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

In Snowflake, Data Shares can be created and managed in two primary ways:

• Via the Snowflake Web Interface (UI): Users can create and manage shares through the graphical interface provided by Snowflake, which allows for a user-friendly experience.
• Via SQL commands: Snowflake also allows the creation and management of shares using SQL commands. This method is more suited for users who prefer scripting or need to automate the process.

 External stages can be altered and dropped in Snowflake. An external stage points to an external location, such as an S3 bucket, where data files are stored. Users can modify the stage’s definition or drop it entirely if it’s no longer needed. This is in contrast to table stages, which are tied to specific tables and cannot be altered or dropped independently.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Stages1