Free Practice Questions for Snowflake COF-C02 Exam

 In Snowflake, you can share several types of objects with other Snowflake accounts. These include schemas, secure views, and secure user-defined functions (UDFs). Sharing these objects allows for collaboration and data access across different Snowflake accounts while maintaining security and governance controls4.

 Snowflake maintains a result cache that stores the results of every query for 24 hours. If the same query is executed again within this time frame and the data has not changed, Snowflake will retrieve the data from the result cache instead of using a virtual warehouse to recompute the results2.

 The REST API used with unstructured data in Snowflake is GET /api/files/, which retrieves (downloads) a data file from an internal or external stage4.

In the Admin area of Snowsight, users can analyze query performance, manage Snowflake warehouses, set up and view details about resource monitors, manage users and roles, and administer Snowflake accounts in their organization2.

 To monitor data storage for individual tables, the commands and objects that can be used are ‘SHOW STORAGE BY TABLE;’ and the Information Schema view ‘TABLE_STORAGE_METRICS’. These tools provide detailed information about the storage utilization for tables. References: Snowflake Documentation

The minimum privilege required on an external stage for any role to access unstructured data files using a file URL in the GET REST API is READ. This allows the role to retrieve or download data files from the stage.

The semi-structured data function that interprets an input string as a JSON document and produces a VARIANT value is PARSE_JSON. This function is used to parse a JSON formatted string and return it as a VARIANT data type, which can then beused for further processing within Snowflake3.

 The OBJECT_CONSTRUCT function is used with the COPY INTO command to convert the rows in a relational table to a single VARIANT column, which can then be unloaded into a file. This is useful for transforming table data into a semi-structured JSON format

 In a standard multi-cluster warehouse with auto-scaling, a cluster will shut down when, after 2-3 consecutive checks, the system determines that the load on the least-loaded cluster could be redistributed to other clusters. This ensures efficient resource utilization and cost management. References: [COF-C02] SnowPro Core Certification Exam Study Guide

Efficient pruning in Snowflake is indicated by the number of partitions scanned out of the total available. If a small percentage of partitions are scanned, it suggests thatthe pruning process is effectively narrowing down the data, which can lead to improved query performance

With the create data EXCHANGE listing privilege, a Snowflake user can modify the properties of listings and submit them for approval or publishing on the Data Exchange. This allows them to manage and share data sets with consumers effectively. References: Based on general data exchange practices in cloud services as of 2021.

 In the Activity section in Snowsight, Snowflake users can explore the performance of executed queries. This includes monitoring queries, viewing details about queries, including performance data, and exploring each step of an executed query in the query profile1.

The Snowflake object that helps evaluate virtual warehouse performance impacted by query queuing is the Information_schema.warehouse_load_history. This view provides historical data about the load on a warehouse, including the average number of queries that were running or queued within a specific interval, which can be used to assess performance and identify potential issues with query queuing3.

Snowflake’s search optimization service supports tables and views that are not protected by row access policies. It is designed to improve the performance of certain types of queries on tables, including selective point lookup queries and queries on fields in VARIANT, OBJECT, and ARRAY (semi-structured) columns1.

When a database is cloned in Snowflake, it does not retain any privileges that were granted on the source object. The clone will need to have privileges reassigned as necessary for users to access it. References: [COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake integrates Duo Security to support Multi-Factor Authentication (MFA). This feature provides increased login security for users connecting to Snowflake, and it is managed completely by Snowflake without the need for users to sign up separately with Duo4.

 Automatic Clustering is a feature that allows users to control the organization of data within micro-partitions in Snowflake. By defining clustering keys, Snowflake can automatically reorganize the data in micro-partitions to optimize query performance1.

The SQL command GET_DDL can be used to retrieve the CREATE definition of a masking policy in Snowflake. This command generates the DDL statement required to recreatethe masking policy

One of the security features of Snowflake includes the periodic rekeying of encrypted data, which is available with the Snowflake Enterprise edition and higher2. This ensures that the encryption keys are rotated regularly to maintain a high level of security. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 Clustering in Snowflake refers to the organization of data within micro-partitions, which are contiguous units of storage within Snowflake tables. Clustering keys can be defined to co-locate similar rows in the same micro-partitions, improving scan efficiency and query performance12.