Snowflake COF-C02 Practice Test - Questions Answers, Page 4

In Snowflake, aPipeis the object designed to enable the continuous, near-real-time loading of data from files as soon as they are available in a cloud storage location. Pipes use Snowflake'sCOPYcommand to load data and can be associated with aStageobject to monitor for new files. When new data files appear in the stage, the pipe automatically loads the data into the target table.

References:

Snowflake Documentation on Pipes

SnowPro Core Certification Study Guide

https://docs.snowflake.com/en/user-guide/data-load-snowpipe-intro.html

In Snowflake, to create a materialized view, the user must have the necessary privileges on the schema where the view will be created. These privileges are granted through roles, not directly to individual users. Therefore, the correct process is to grant the role to the user and then grant the privilege to create the materialized view to the role itself.

The statementGRANT ROLE MYROLE TO USER USER1;grants the specified role to the user, allowing them to assume that role and exercise its privileges. The subsequent statementCREATE MATERIALIZED VIEW ON SCHEMA MYDB.MYSCHEMA TO MYROLE;grants the privilege to create a materialized view within the specified schema to the roleMYROLE. Any user who has been grantedMYROLEcan then create materialized views inMYDB.MYSCHEMA.

References:

Snowflake Documentation on Roles

Snowflake Documentation on Materialized Views

https://docs.snowflake.com/en/user-guide/intro-summary-loading.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, seeSupported Character Sets for Delimited Files(in this topic).

Snowflake is designed to optimize query performance by utilizing metadata for certain types of queries. When executing aCOUNTquery, Snowflake can often fulfill the request by accessing metadata about the table's row count, rather than scanning the entire table or micro-partitions. This is particularly efficient for large tables likeFCT_SALESwith a significant number of records. The metadata layer maintains statistics about the table, including the row count, which enables Snowflake to quickly return the result of aCOUNTquery without the need to perform a full scan.

References:

Snowflake Documentation on Metadata Management

SnowPro Core Certification Study Guide

TheResult cacheis 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.

References:

Snowflake Documentation on Query Results Cache

SnowPro Core Certification Study Guide

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.

References:

Snowflake Documentation on Virtual Warehouses

SnowPro Core Certification Study Guide

Materialized Views in Snowflake are designed to store the result of a query and can be refreshed to maintain up-to-date data. However, they have certain limitations, one of which is that they cannot be defined using a JOIN clause. This means that a Materialized View can only be created based on a single source table and cannot combine data from multiple tables using JOIN operations.

References:

Snowflake Documentation on Materialized Views

SnowPro Core Certification Study Guide

Snowflake Time Travel is a powerful feature that allows users to access historical data within a defined period. It enables two key capabilities:

B . Restoring data-related objects that have been deleted within the past 90 days: Time Travel can be used to restore tables, schemas, and databases that have been accidentally or intentionally deleted within the Time Travel retention period.

C . Conducting point-in-time analysis for BI reporting: It allows users to query historical data as it appeared at a specific point in time within the Time Travel retention period, which is crucial for business intelligence and reporting purposes.

While Time Travel does allow querying of past data, it is limited to the retention period set for the Snowflake account, which is typically 1 day for standard accounts and can be extended up to 90 days for enterprise accounts. It does not enable querying or restoring objects created or deleted beyond the retention period, nor does it provide analysis over all periods of time.

References:

Snowflake Documentation on Time Travel

SnowPro Core Certification Study Guide

Snowflake credits are the unit of measure for the compute resources used in Snowflake. The number of credits consumed depends on the size of the virtual warehouse and the time it is running. Larger warehouses consume more credits per hour than smaller ones, and credits are billed for the time the warehouse is active, regardless of the actual usage within that time.

References: [COF-C02] SnowPro Core Certification Exam Study Guide