Free SnowPro Core Certification 2026 Exam Practice Questions

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

B.  

D.  

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.

 To evaluate the details of dynamic data masking, the POLICY_REFERENCES and ACCESS_HISTORY views in the account_usage schema are used. The POLICY_REFERENCES view provides information about the objects to which a masking policy is applied, and the ACCESS_HISTORY view contains details about access to the masked data, which can be used to audit and verify the application of dynamic data masking policies.

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.

 To address a high volume of queries queuing in a warehouse, Snowflake recommends two best practices:

A.  

B.  

These strategies help to optimize the performance of the warehouse by ensuring that resources are scaled appropriately to meet demand.

 Micro-partitions in Snowflake are immutable objects, which means once they are written, they cannot be modified. This immutability supports the use of Time Travel, allowing users to access historical data within a defined period. Additionally, micro-partitions can significantly reduce the amount of I/O from object storage to virtual warehouses. This is because Snowflake’s query optimizer can skipover micro-partitions that do not contain relevant data for a query, thus reducing the amount of data that needs to be scanned and transferred.

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.

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.

 Snowflake’s architecture is designed to handle high concurrency workloads through several features, two of which are particularly effective:

B.  

D.  

These features ensure that Snowflake can manage varying levels of demand without manual intervention, providing a seamless experience even during peak usage.

 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.  

B.  

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.

To return results from the results cache in Snowflake, certain conditions must be met:

Privileges: The user must have the appropriate privileges on the objects associated with the query. This ensures that only authorized users can access cached data.

Time Frame: The query must have been run within 24 hours of the previously-run query. Snowflake’s results cache is designed to store the results of queries for a short period, typically 24 hours, to improve performance for repeated queries.