Multi-cluster virtual warehouses in Snowflake are designed to manage user and query concurrency needs. They allow for the allocation of additional clusters of compute resources, either statically or dynamically, to handle increased loads and reduce or eliminate the queuing of concurrent queries2.
https://docs.snowflake.com/en/user-guide/warehouses-multicluster.html#:~:text=Multi%2Dcluster%
20warehouses%20enable%20you,during%20peak%20and%20off%20hours.

The correct answers are C. Bracket notation and E. Dot notation .
Snowflake supports querying semi-structured data stored in columns such as VARIANT, OBJECT, and ARRAY. When working with JSON objects, users can access elements using either dot notation or bracket notation .
Why C is correct:
Bracket notation is used to access JSON object elements by enclosing the key name in brackets.
Example:
SELECT src[ ' customer ' ][ ' name ' ]
FROM json_table;
Bracket notation is especially useful when JSON keys contain special characters, spaces, or case-sensitive names.
Why E is correct:
Dot notation is used to access JSON object elements using a period between object levels.
Example:
SELECT src:customer.name
FROM json_table;
This notation is commonly used for simple JSON key paths.
Why the other options are incorrect:
A). CIDR notation is used for IP address ranges, not JSON element access.
B). Vertical bar notation is not a Snowflake method for accessing JSON object elements.
D). Slash notation is not a Snowflake method for accessing JSON object elements.
Official Snowflake documentation reference:
Snowflake documentation explains that semi-structured data elements in VARIANT columns can be accessed using dot notation and bracket notation.
Reference: Snowflake Documentation - Querying semi-structured data; Snowflake Documentation - Dot notation and bracket notation; SnowPro Core Study Guide - Working with Semi-Structured Data.
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Snowflake recommends considering adding a clustering key to a table when its size reaches1 Terabyte (TB)or larger. Clustering keys help optimize the storage and query performance by organizing the data in a table based on the specified columns. This is particularly beneficial for large tables where data retrieval can become inefficient without proper clustering.
Why Clustering Keys Are Important:Clustering keys ensure that data stored in Snowflake is physically ordered in a way that aligns with the most frequent access patterns, thereby reducing the amount of scanned data during queries and improving performance.
Recommendation Basis:The recommendation for tables of size 1 TB or larger is based on the observation that smaller tables generally do not benefit as much from clustering, given Snowflake ' s architecture. However, as tables grow in size, the benefits of clustering become more pronounced.
Implementing Clustering Keys:
To set a clustering key for a table, you can use theCLUSTER BYclause during table creation or alter an existing table to add it:
CREATETABLEmy_table (... ) CLUSTERBY(column1, column2);
Or for an existing table:
ALTERTABLEmy_table CLUSTERBY(column1, column2);
Reference:For additional information on clustering in Snowflake and recommendations on when to use clustering keys, consult the Snowflake documentation on managing and optimizing data clustering:
https://docs.snowflake.com/en/user-guide/tables-clustering-keys.html

Resource monitors in Snowflake can perform actions such as suspending a virtual warehouse when its credit usage reaches a defined limit and triggering a notification to account administrators when credit usage reaches a specified threshold. These actions help manage and control resource usage and costs within Snowflake.
Snowflake Documentation: Resource Monitors