COF-C02 Exam Dumps - Snowflake SnowPro Core Certification Questions and Answers

The Snowflake feature that helps troubleshoot issues in SQL query expressions and commonly identify performance bottlenecks is theQuery Profile. The Query Profile provides a detailed breakdown of a query's execution plan, including each operation's time and resources consumed. It visualizes the steps involved in the query execution, highlighting areas that may be causing inefficiencies, such as full table scans, large joins, or operations that could benefit from optimization.

By examining the Query Profile, developers and database administrators can identify and troubleshoot performance issues, optimize query structures, and make informed decisions about potential schema or indexing changes to improve performance.

Cross-region replication ensures data availability across multiple cloud regions. In the event of a regional outage, Snowflake accounts with this feature can fail over to a different region.

In Snowflake, a tag can be associated with only one masking policy for each data type. This means that for a given data type, you can define a single masking policy to be applied when a tag is used. Tags and masking policies are part of Snowflake's data classification and governance features, allowing for data masking based on the context defined by the tags.

Snowflake Documentation: Tag-Based Masking Policies

Resource monitors in Snowflake are tools used to track and control the consumption of compute resources, ensuring that usage stays within defined limits. These monitors can be created at the account level, allowing administrators to set overall resource consumption limits for the entire Snowflake account. Additionally, resource monitors can be set at the virtual warehouse level, enabling more granular control over the resources consumed by individual warehouses. This dual-level capability allows organizations to manage their Snowflake usage efficiently, preventing unexpected costs and optimizing performance.

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

Secure data sharing in Snowflake allows users to share various objects between Snowflake accounts without physically copying the data, thus not consuming additional storage. Among the options provided, external tables can be shared through secure data sharing. External tables are used to query data directly from files in a stage without loading the data into Snowflake tables, making them suitable for sharing across different Snowflake accounts.

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.

To upload data files from a local directory or folder on a client machine to an internal stage in Snowflake, thePUTcommand is used. ThePUTcommand takes files from the local file system and uploads them to an internal Snowflake stage (or a specified stage) for the purpose of preparing the data to be loaded into Snowflake tables.

Syntax Example:

PUT file:// @;

This command is crucial for data ingestion workflows in Snowflake, especially when preparing to load data using theCOPY INTOcommand.

In Snowflake, when dealing with semi-structured data such as a JSON document stored in aVARIANTcolumn, the proper syntax to extract a value is to use the column name followed by the path to the specific element. Since arrays in JSON are zero-indexed, the first element is referenced with[0]. Therefore, to extract thenamefrom the first element of theelementsarray, the correct syntax isSource_column:elements[0].name.

 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.

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.