Pass the Snowflake SnowPro Core Certification COF-C02 Questions and answers with CertsForce

 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.

 In the query profiler view, the components that represent areas that can be used to help optimize query performance include ‘Bytes scanned’ and ‘Number of partitions scanned’. ‘Bytes scanned’ indicates the total amount of data the query had to read and is a direct indicator of the query’s efficiency. Reducing the bytes scanned can lead to lower data transfer costs and faster query execution. ‘Number of partitions scanned’ reflects how well the data is clustered; fewer partitions scanned typically means better performance because the system can skip irrelevant data more effectively.

A clustering key in Snowflake is used to co-locate similar data within the same micro-partitions to improve query performance, especially for large tables where data is not naturally ordered or has become fragmented due to extensive DML operations. The appropriate use of a clustering key can lead to improved scan efficiency and better column compression, resulting in faster query execution times.

The indicators that it may be appropriate to use a clustering key for a table include:

D. Queries on the table are running slower than expected: This can happen when the data in the table is not well-clustered, leading to inefficient scans during query execution.

E. The clustering depth for the table is large: A large clustering depth indicates that the table’s data is spread across many micro-partitions, which can degrade query performance as more data needs to be scanned.

 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.

 Clustering is a technique used in Snowflake to improve the performance of queries. It involves organizing the data in a table into micro-partitions based on the values of one or more columns. This organization allows Snowflake to efficiently prune non-relevant micro-partitions during a query, which reduces the amount of data scanned and improves query performance.

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.

 Clustering keys in Snowflake are used to reorganize large tables based on one or more columns. This feature optimizes the arrangement of data within micro-partitions to improve query performance, especially for large tables where efficient data retrieval is crucial. References: [COF-C02] SnowPro Core Certification Exam Study Guide

https://docs.snowflake.com/en/user-guide/tables-clustering-keys.html

When loading data into Snowflake, it is recommended to:

C. Load files that are approximately 100-250 MB (or larger): This size is optimal for parallel processing and can help to maximize throughput. Smaller files can lead to overhead that outweighs the actual data processing time.

D. Avoid using embedded characters such as commas for numeric data types: Embedded characters can cause issues during data loading as they may be interpreted incorrectly. It’s best to clean the data of such characters to ensure accurate and efficient data loading.

These best practices are designed to optimize the data loading process, ensuring that data is loaded quickly and accurately into Snowflake.

 To find information about query activity from 90 days ago, a Snowflake user should use the account_usage.query_history_archive view. This view is designed to provide access to historical query data beyond the default 14-day retention period found in the standard query_history view. It allows users to analyze and audit past query activities for up to 365 days after the date of execution, which includes the 90-day period mentioned.

The ACCOUNTADMIN role is the most powerful role in Snowflake and should be limited to a select number of users within an organization. It is responsible for account-level configurations and should not be used for day-to-day object creation or management. Granting a custom role to ACCOUNTADMIN could inadvertently give broad access to users with this role, which is not a recommended security practice.

Data Robot is recognized as a machine learning and data science partner within the Snowflake Partner Ecosystem. It provides an enterprise AI platform that enables users to build and deploy accurate predictive models quickly. As a partner, Data Robot integrates with Snowflake to enhance data science capabilities2.

 Snowflake applies egress charges in the case of database replication when data is transferred out of a Snowflake region to another region or cloud provider. This is because the data transfer incurs costs associated with moving data across different networks. Egress charges are not applied for data sharing within the same region, query result retrieval, or loading data into Snowflake, as these actions do not involve data transfer across regions.

SnowSQL is the command-line client for Snowflake that allows users to execute SQL queries and perform all DDL and DML operations, including staging files for bulk data loading. It is specifically designed for scripting and automating tasks.

 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.

 Snowflake’s architecture allows for the use of a query result cache that stores the results of queries for a period of time. If the same query is run again and the underlying data has not changed, Snowflake can retrieve the result from this cache without needing to re-run the query on an active warehouse, thus saving on compute resources.

Reader accounts in Snowflake are designed to allow users to read data shared with them but do not have the capability to extract data for use outside of Snowflake. They are intended for consuming shared data within the Snowflake environment only.

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 PUT command is used to load data into an internal stage in Snowflake. This command uploads data files from a local file system to a named internal stage, making the data available for subsequent loading into a Snowflake table using the COPY INTO command.

 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.

 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.