Snowflake SnowPro-Core - SnowPro Core Certification Exam

The correct command to view all roles granted to a specific user in Snowflake is SHOW GRANTS TO USER ;. This command lists all access control privileges that have been explicitly granted to the specified user. References: SHOW GRANTS | Snowflake Documentation

 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

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 statement GRANT ROLE MYROLE TO USER USER1; grants the specified role to the user, allowing them to assume that role and exercise its privileges. The subsequent statement CREATE MATERIALIZED VIEW ON SCHEMA MYDB.MYSCHEMA TO MYROLE; grants the privilege to create a materialized view within the specified schema to the role MYROLE. Any user who has been granted MYROLE can then create materialized views in MYDB.MYSCHEMA.

References:

Snowflake Documentation on Roles

Snowflake Documentation on Materialized Views

 The command list @mystage; is used to view the list of files that have been uploaded to an internal stage in Snowflake. The list command displays the metadata for all files in the specified stage, which in this case is mystage. This command is particularly useful for verifying that files have been successfully unloaded from a Snowflake table to the stage and for managing the files within the stage.

References:

Snowflake Documentation on Stages

SnowPro® Core Certification Study Guide

Semi-structured

JSON, Parquet

Snowflake supports unloading data in several semi-structured file formats, including Parquet and JSON. These formats allow for efficient storage and querying of semi-structured data, which can be loaded directly into Snowflake tables without requiring a predefined schema12.

https://docs.snowflake.com/en/user-guide/data-unload-prepare.html#:~:text=Supported%20File%20Formats,-The%20following%20file &text=Delimited%20(CSV%2C%20TSV%2C%20etc.)

The list command in Snowflake is used to validate and display the list of files in a specified stage. When a user has unloaded data to a stage, running the list @file__stage command will show all the files that have been uploaded to that stage, allowing the user to verify the data that was unloaded.

References:

Snowflake Documentation on Stages

SnowPro® Core Certification Study Guide

 When an external stage is dropped in Snowflake, the reference to the external storage location is removed, but the actual files within the external storage (like Amazon S3, Google Cloud Storage, or Microsoft Azure) are not deleted. This means that the data remains intact in the external storage location, and only the stage object in Snowflake is removed.

On the other hand, when an internal stage is dropped, any files that were uploaded to the stage are deleted along with the stage itself. These files are not recoverable once the internal stage is dropped, as they are permanently removed from Snowflake’s storage.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Stages

 In a CREATE PIPE ... AS COPY ... FROM (....) statement, the supported transformations include filtering data using an optional WHERE clause and omitting columns. The WHERE clause allows for the specification of conditions to filter the data that is being loaded, ensuring only relevant data is inserted into the table. Omitting columns enables the exclusion of certain columns from the data load, which can be useful when the incoming data contains more columns than are needed for the target table.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Simple Transformations During a Load1

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.

References: The information is verified according to the SnowPro Core Certification Study Guide and Snowflake documentation

 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.

References:

Snowflake Documentation on Semi-Structured Data

[COF-C02] SnowPro Core Certification Exam Study Guide

When a Snowflake virtual warehouse is resized, the data cached in the warehouse is not lost. This is because the cache is maintained independently of the warehouse size. Resizing a warehouse, whether scaling up or down, does not affect the cached data, ensuring that query performance is not impacted by such changes.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Virtual Warehouse Performance1

 Snowflake is designed to optimize query performance by utilizing metadata for certain types of queries. When executing a COUNT query, 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 like FCT_SALES with 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 a COUNT query without the need to perform a full scan.

References:

Snowflake Documentation on Metadata Management

SnowPro® Core Certification Study Guide

 When a query in Snowflake is too large to fit into the available memory, it will start spilling to remote storage. This is an indication that the memory allocated for the query is insufficient for its execution, and as a result, Snowflake uses remote disk storage to handle the overflow. This spill to remote storage can lead to slower query performance due to the additional I/O operations required.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Query Profile1

Snowpro Core Certification Exam Flashcards2

 In Snowflake, a reader account is a type of account that is intended for consuming shared data rather than performing any data management or DDL operations. The CREATE SHARE command is used to share data from your account with another account, which is not a capability provided to reader accounts. Reader accounts are typically restricted from creating shares, as their primary purpose is to read shared data rather than to share it themselves.

References:

Snowflake Documentation on Reader Accounts

SnowPro® Core Certification Study Guide

The Snowflake storage layer is responsible for storing data in an optimized, compressed, columnar format. This includes micro-partitions, which are the fundamental storage units that contain the actual data stored in Snowflake. Additionally, persisted query results, which are the results of queries that have been materialized and stored for future use, are also kept within this layer. This design allows for efficient data retrieval and management within the Snowflake architecture1.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Key Concepts & Architecture | Snowflake Documentation2