Snowflake COF-C02 - SnowPro Core Certification Exam

The LOGIN_HISTORY view in the ACCOUNT_USAGE schema provides information about login attempts, including both successful and failed logins. This view can be used to review the failed login attempts of a specific user for the past 30 days. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 Scaling out in Snowflake involves adding clusters of the same size to a virtual warehouse, which allows for handling more concurrent queries without affecting the performance of individual queries. Scaling up refers to resizing a virtual warehouse to increase its compute resources, enabling it to handle more complex workloads and larger queries more efficiently.

 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 graphical representation of warehouse utilization indicates periods of significant queuing, suggesting that the current single cluster cannot efficiently handle all incoming queries. Configuring the warehouse to a multi-cluster warehouse will distribute the load among multiple clusters, reducing queuing times and improving overall performance1.

References = Snowflake Documentation on Multi-cluster Warehouses1

The SQL command used to delete files from a Snowflake internal stage is REMOVE. This command can be used to remove files from either an internal or external stage within Snowflake

 Snowpipe retains load history for 14 days. This allows users to view and audit the data that has been loaded into Snowflake using Snowpipe within this time frame3.

A multi-cluster warehouse should be used in auto-scaling mode when there is a need to handle a large number of concurrent queries. Auto-scaling allows Snowflake to automatically add or remove compute clusters to balance the load, ensuring that performance remains consistent during varying levels of demand

 Snowflake’s architecture is described as a multi-cluster, shared data architecture. This design combines the simplicity of a shared-disk architecture with the performance and scale-out benefits of a shared-nothing architecture, using a central repository accessiblefrom all compute nodes2.

References = [COF-C02] SnowPro Core Certification Exam Study Guide, Snowflake Documentation

An explain plan is beneficial because it allows for the evaluation of how a query will be processed without the need to actually execute the query. This can help in understanding the query’s performance implications and potential bottlenecks without consuming resources that would be used if the query were run

 In Snowflake, a schema is a logical grouping of database objects, which can include streams and external tables. A stream is an object that allows users to query data that has changed in specified tables or views, and an external table is a table that references data stored outside of Snowflake. Roles, warehouses, users, and shares are not contained within a schema. References: SHOW OBJECTS, Database, Schema, & Share DDL

In Snowflake, the maximum number of clusters that can be provisioned within a multi-cluster virtual warehouse is 10. This allows for significant scalability and performance management by enabling Snowflake to handle varying levels of query load by adjusting the number of active clusters within the warehouse.

To get the most detailed information about individual table storage details in Snowflake, theTABLE STORAGE METRICSview should be used. This Information Schema view provides granular storage metrics for tables within Snowflake, including data related to the size of the table, the amount of data stored, and storage usage over time. It's an essential tool for administrators and users looking to monitor and optimize storage consumption and costs.

Snowflake bills virtual warehouse usage in one-minute increments, rounding up to the nearest minute for any partial minute of compute time used. If a virtual warehouse runs for 61 seconds and then, after being shut down, restarts and runs for an additional 30 seconds, the total time billed would be 120 seconds or 2 minutes. The first 61 seconds are rounded up to 2 minutes, and the subsequent 30 seconds are within a new minute, which is also rounded up to the nearest minute.

When querying semi-structured data in Snowflake, the behavior towards case sensitivity is distinct between column names and the names of elements within the semi-structured data. Column names follow the general SQL norm of being case-insensitive, meaning you can reference them in any case without affecting the query. However, element names within JSON, XML, or other semi-structured data are case-sensitive. This distinction is crucial for accurate data retrieval and manipulation in Snowflake, especially when working with JSON objects where the case of keys can significantly alter the outcome of queries.

Temporary tables in Snowflake do not consume storage costs. They are designed for transient data that is needed only for the duration of a session. Data stored in temporary tables is held in the virtual warehouse's cache and does not persist beyond the session's lifetime, thereby not incurring any storage charges.

A clustering key can be defined when a table is created by appending a CLUSTER Where each clustering key consists of one or more table columns/expressions, which can be of any data type, except GEOGRAPHY, VARIANT, OBJECT, or ARRAYhttps://docs.snowflake.com/en/user-guide/tables-clustering-keys

TheOBJECT_CONSTRUCTfunction in Snowflake is used to convert rows in a relational table into a single VARIANT column that represents each row as a JSON object. This function dynamically creates a JSON object from a list of key-value pairs, where each key is a column name and each value is the corresponding column value for a row. This is particularly useful for aggregating and transforming structured data into semi-structured JSON format for further processing or analysis.

Transient tables in Snowflake are designed for temporary or intermediate workloads with the following characteristics:

B. Transient tables can be cloned to permanent tables: This feature allows users to create copies of transient tables for permanent use, providing flexibility in managing data lifecycles.

C. Transient tables persist until they are explicitly dropped: Unlike temporary tables that exist for the duration of a session, transient tables remain in the database until explicitly removed by a user, offering more durability for short-term data storage needs.

The Cloud Services layer in Snowflake is responsible for a wide range of services that facilitate the management and use of Snowflake, including:

D. User authentication: This service handles identity and access management, ensuring that only authorized users can access Snowflake resources.

E. Infrastructure management: This service manages the allocation and scaling of resources to meet user demands, including the management of virtual warehouses, storage, and the orchestration of query execution.

These services are part of Snowflake's fully managed, cloud-based architecture, which abstracts and automates many of the complexities associated with data warehousing.

Object Name Resolution: When unqualified object names (e.g., table name without schema) are used in a UDF, Snowflake follows a specific hierarchy to resolve them. Here's the order:

Current Schema: Snowflake first checks if an object with the given name exists in the schema currently in use for the session.

PUBLIC Schema: If the object isn't found in the current schema, Snowflake looks in the PUBLIC schema of the current database.

Note: TheSEARCH_PATHparameter influences object resolution for queries, not within UDFs.