SnowCD (Snowflake Connectivity Diagnostic Tool) is used to diagnose and troubleshoot network

connections to Snowflake. It runs a series of connection checks to evaluate the network connection

to Snowflake

https://docs.snowflake.com/en/user-guide/intro-summaryloading.html#:~:text=For%20delimited%20files%20(CSV%2C%20TSV,encoding%20to%20use%20for%

20loading.

For delimited files (CSV, TSV, etc.), the default character set is UTF-8. To use any other characters sets,

you must explicitly specify the encoding to use for loading. For the list of supported character sets,

see Supported Character Sets for Delimited Files (in this topic).

The Information Schema and Account Usage Share in Snowflake provide metadata and historical

usage data for various objects within a Snowflake account. Specifically, they offer storage information

for Tables, Databases, and Internal Stages. These schemas contain views and table functions that

allow users to query object metadata and usage metrics, such as the amount of data stored and

historical activity.

Tables: The storage information includes data on the daily average amount of data in database

tables.

Databases: For databases, the storage usage is calculated based on all the data contained within the

database, including tables and stages.

Internal Stages: Internal stages are locations within Snowflake for temporarily storing data, and their

storage usage is also tracked.

Reference: The information is verified according to the SnowPro Core Certification Study Guide and

Snowflake documentation

One of the key features of Snowflake’s architecture is its unique approach to eliminating resource

contention through the use of virtual warehouses. This is achieved by separating storage and

compute resources, allowing multiple virtual warehouses to operate independently on the same

data without affecting each other. This means that different workloads, such as loading data, running

queries, or performing complex analytics, can be processed simultaneously without any performance

degradation due to resource contention.

Reference:

Snowflake Documentation on Virtual Warehouses

SnowPro® Core Certification Study Guide

Snowpipe data load history is retained for 64 days. This retention period allows users to review and

audit the data load operations performed by Snowpipe over a significant period of time, which can

be crucial for troubleshooting and ensuring data integrity.

Reference:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Snowpipe1

Snowflake supports querying semi-structured data using specific data types that are capable of

handling the flexibility and structure of such data. The data types supported for this purpose are:

A . VARIANT: This is a universal data type that can store values of any other type, including structured

and semi-structured types. It is particularly useful for handling JSON, Avro, ORC, Parquet, and XML

data formats1.

B . ARRAY: An array is a list of elements that can be of any data type, including VARIANT, and is used

to handle semi-structured data that is naturally represented as a list1.

These data types are part of Snowflake’s built-in support for semi-structured data, allowing for the

storage, querying, and analysis of data that does not fit into the traditional row-column format.

Reference:

Snowflake Documentation on Semi-Structured Data

[COF-C02] SnowPro Core Certification Exam Study Guide

The size of the virtual warehouse for a task can be configured to handle concurrency automatically

using a Multi-cluster warehouse (MCW). This is because tasks are designed to run their body on a

schedule, and MCW allows for scaling compute resources to match the task’s execution needs

without manual intervention. Reference: [COF-C02] SnowPro Core Certification Exam Study Guide

The Snowflake Cloud Services layer manages various services, including authentication and

metadata management. This layer ties together all the different components of Snowflake to process

user requests, manage sessions, and control access3.

The Result cache is used in Snowflake to cache the data output from SQL queries. This feature is

designed to improve performance by storing the results of queries for a period of time. When the

same or similar query is executed again, Snowflake can retrieve the result from this cache instead of

re-computing the result, which saves time and computational resources.

Reference:

Snowflake Documentation on Query Results Cache

SnowPro® Core Certification Study Guide

When loading data into Snowflake, it is recommended to organize the data into single files with 100-

250 MB of compressed data per file. This size range is optimal for parallel processing and can help in

achieving better performance during data loading operations. Reference: [COF-C02] SnowPro Core

Certification Exam Study Guide