Snowflake COF-C02 - SnowPro Core Certification Exam

In Snowflake, a Pipe is 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’s COPY command to load data and can be associated with a Stage object to monitor for new files. When new data files appear in the stage, the pipe automatically loads the data into the target table.

 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.

 In all Snowflake editions, two key capabilities are universally available:

B. Automatic encryption of all data: Snowflake automatically encrypts all data stored in its platform, ensuring security and compliance with various regulations. This encryption is transparent to users and does not require any configuration or management.

D. Object-level access control: Snowflake provides granular access control mechanisms that allow administrators to define permissions at the object level, including databases, schemas, tables, and views. This ensures that only authorized users can access specific data objects.

These features are part of Snowflake’s commitment to security and governance, and they are included in every edition of the Snowflake Data Cloud.

Snowflake’s Continuous Data Protection (CDP) encompasses a set of features that help protect data stored in Snowflake against human error, malicious acts, and software failure. Time Travel allows users to access historical data (i.e., data that has been changed or deleted) for a defined period, enabling querying and restoring of data. Fail-safe is an additional layer of data protection that provides a recovery option in the event of significant data loss or corruption, which can only be performed by Snowflake.

When a CLUSTER BY clause is added to a Snowflake table, it specifies one or more columns to organize the data within the table’s micro-partitions. This clustering aims to colocate data with similar values in the same or adjacent micro-partitions. By doing so, it enhances the efficiency of query pruning, where the Snowflake query optimizer can skip over irrelevant micro-partitions that do not contain the data relevant to the query, thereby improving performance.

 The COPY INTO command is used in Snowflake to unload data from a table into a file in a stage. This command allows for the export of data from Snowflake tables into flat files, which can then be used for further analysis, processing, or storage in external systems.

In Snowflake, Cloud Services costs are incurred when the Cloud Services usage exceeds 10% of the compute usage (measured in credits). Therefore, scenarios A and E would result in Cloud Services charges because the Cloud Services usage is more than 10% of the compute credits used.

 The STRIP_OUTER_ARRAY file format option in Snowflake is used when loading JSON documents that are composed of a large array containing multiple records. When this option is enabled, it removes the outer array structure, which allows each record within the array to be loaded as a separate row in the table. This is particularly useful for efficiently loading JSON data that is structured as an array of records1.

 Materialized views in Snowflake are a feature that allows for the pre-computation and storage of query results for faster query performance. This feature is available starting from the Enterprise Edition of Snowflake. It is not available in the Standard Edition, and while it is also available in higher editions like Business Critical and Virtual Private Snowflake, the Enterprise Edition is the minimum requirement.

Parquet is a columnar storage file format that is optimized for performance in Snowflake. It is designed to be efficient for both storage and query performance, particularly for complex queries onlarge datasets. Parquet files support efficient compression and encoding schemes, which can lead to significant savings in storage and speed in query processing, making it the most performant file format for loading data into Snowflake.

 Snowflake’s default Multi-Factor Authentication (MFA) service is powered by Duo Security. Users are required to install the Duo Mobile application on their devices to use MFA for increased login security when connecting to Snowflake. This service is managed entirely by Snowflake, and users donot need to sign up separately with Duo1.

 External functions in Snowflake are a special type of User-Defined Function (UDF) that call code executed outside of Snowflake, typically through a remote service. Unlike traditional UDFs, external functions do not contain SQL code within Snowflake; instead, they interact with external services to process data2.

https://docs.snowflake.com/en/sql-reference/external-functions.html#:~:text=External %20functions%20are%20user%2Ddefined,code%20running%20outside%20of%20Snowflake.

Secure sharing in Snowflake allows users to share specific objects with other Snowflake accounts without physically copying the data, thus not consuming additional storage. Tables and Secure User Defined Functions (UDFs) are among the objects that can be shared using this feature. Materialized views, sequences, and procedures are not shareable objects in Snowflake.

 The COPY INTO command in Snowflake can be configured to output data into a single file by setting the MAX_FILE_NUMBER option to 1. This option limits the number of files generated by the command, ensuring that only one file is created regardless of the amount of data being exported.

A VARIANT column in Snowflake can store semi-structured data types. This includes:

B. OBJECT: An object is a collection of key-value pairs in JSON, and a VARIANT column can store this type of data structure.

D. ARRAY: An array is an ordered list of zero or more values, which can be of any variant-supported data type, including objects or other arrays.

The VARIANT data type is specifically designed to handle semi-structured data like JSON, Avro, ORC, Parquet, or XML, allowing for the storage of nested and complex data structures.

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.

The default file format for the COPY command in Snowflake, when not specified, is CSV (Comma-Separated Values). This format is widely used for data exchange because it is simple, easy to read, and supported by many data analysis tools.

The result set cache in Snowflake is invalidated and no longer available when the underlying data of the query results has changed, ensuring that queries return the most current data. Additionally, the cache expires after 24 hours to maintain the efficiency and accuracy of data retrieval1.

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.