Pass the Snowflake SnowPro Core Certification COF-C03 Questions and answers with CertsForce

The correct answer is B. Iceberg table .

Snowflake Secure Data Sharing allows providers to share supported database objects with other Snowflake accounts. Supported objects include tables, secure views, secure materialized views, secure UDFs, and certain other supported table types. Snowflake-managed Iceberg tables can be shared in supported configurations.

Why B is correct:

An Iceberg table is a supported table object in Snowflake and can be shared using Secure Data Sharing, subject to Snowflake’s supported Iceberg table sharing requirements.

Why the other options are incorrect:

A. Event tables are used to store logging and tracing event data. They are not the expected shareable object in this question.

C. Temporary tables exist only within the session that created them and cannot be shared with other accounts.

D. Stored procedures are not standard objects shared through Secure Data Sharing in the same way as tables and secure views.

Official Snowflake documentation reference:

Snowflake documentation lists supported objects for Secure Data Sharing and describes sharing supported table objects, including Iceberg tables where supported.

Snowsight, Snowflake’s user interface for executing and analyzing queries, supports various types of visualizations to help users understand their data better. Among the supported types, area charts and bar charts are two common options. Area charts are useful for representing quantities through the use of filled areas on the graph, often useful for showing volume changes over time. Bar charts, on the other hand, are versatile for comparing different groups or categories of data. Both chart types are integral to data analysis, enabling users to visualize trends, patterns, and differences in their data effectively.

In Snowflake, the ACCOUNTADMIN role is required to create resource monitors. Resource monitors are used to manage and monitor the consumption of compute resources. The ACCOUNTADMIN role has the necessary privileges to create, configure, and manage resource monitors across the account.

Snowflake Documentation: Resource Monitors

The type of Snowflake table that persists until it is explicitly dropped, is available for all users with relevant privileges across sessions, and does not have a Fail-safe period, is a Transient table. Transient tables are designed to provide temporary storage similar to permanent tables but with some reduced storage costs and without the Fail-safe feature, which provides additional data protection for a period beyond the retention time. Transient tables are useful in scenarios where data needs to be temporarily stored for longer than a session but does not require the robust durability guarantees of permanent tables.

The correct answer is B. Directory table .

A directory table stores file-level metadata for files in a Snowflake stage. It can be used to see a list of files available in an internal or external stage.

Why B is correct:

Directory tables allow users to query staged file metadata, including file path, file size, last modified timestamp, and file URL information.

Example:

SELECT *

FROM DIRECTORY(@my_stage);

This returns metadata for files stored in the stage.

Why the other options are incorrect:

A. A semantic view is used to define business-friendly semantic relationships for analytics, not to list staged files.

C. A materialized view stores precomputed query results, not stage file metadata.

D. Information Schema contains metadata about Snowflake objects, but the Snowflake object specifically designed to list files in a stage is a directory table.

Official Snowflake documentation reference:

Snowflake documentation describes directory tables as objects that store a catalog of staged files in cloud storage.

Direct Shares allow secure data sharing between Snowflake accounts in the same region . No data is copied; Snowflake provides access to the original data using metadata and access control.

When a Snowflake user increases the size of a suspended virtual warehouse, the changes to compute resources are queued but do not take immediate effect. The provisioning of additional compute resources occurs only when the warehouse is resumed. This ensures that resources are allocated efficiently, aligning with Snowflake ' s commitment to cost-effective and on-demand scalability.

The VARIANT data type in Snowflake is used to store JSON key-value pairs along with other semi-structured data formats like AVRO, BSON, and XML. The VARIANT data type allows for flexible and dynamic data structures within a single column, accommodating complex and nested data. This data type is crucial for handling semi-structured data in Snowflake, enabling users to perform SQL operations on JSON objects and arrays directly.

To find out which numeric values are present in both tables, the INTERSECT set operator should be used. This operator returns rows from one query’s result set which also appear in another query’s result set, effectively finding the common elements between the two tables45.

The parameter that prevents streams on tables from becoming stale is MAXDATAEXTENSIONTIMEINDAYS. This parameter specifies the maximum number of days for whichSnowflake can extend the data retention period for the table to prevent streams on the table from becoming stale4.

The ORGADMIN role in Snowflake is an organizational-level role that provides administrative capabilities across the entire organization, rather than being limited to a single Snowflake account. Users with this role can:

A. Create an account for an organization: The ORGADMIN role has the privilege to create new Snowflake accounts within the organization, allowing for the expansion and management of the organization ' s resources.

D. View usage information for all accounts in an organization: This role also has access to comprehensive usage and activity data across all accounts within the organization. This is crucial for monitoring, cost management, and optimization at the organizational level.

The default value for auto-suspending a Virtual Warehouse in the Snowflake Web Interface (UI) is 10 minutes. This setting helps manage compute costs by automatically suspending warehouses that are not in use, ensuring that compute resources are efficiently allocated and not wasted on idle warehouses.

The Virtual Private Snowflake (VPS) edition offers the highest level of security for organizations with the strictest security requirements. This edition provides a dedicated andisolated instance of Snowflake, including enhanced security features and compliance certifications to meet the needs of highly regulated industries or any organization requiring the utmost in data protection and privacy.

Snowflake supports a variety of file formats for unloading semi-structured data, among which Parquet and Newline Delimited JSON (NDJSON) are two widely used formats.

B. Binary file in Parquet: Parquet is a columnar storage file format optimized for large-scale data processing and analysis. It is especially suited for complex nested data structures.

D. Newline Delimited JSON (NDJSON): This format represents JSON records separated by newline characters, facilitating the storage and processing of multiple, separate JSON objects in a single file.

These formats are chosen for their efficiency and compatibility with data analytics tools and ecosystems, enabling seamless integration and processing of exported data.

 The TO_JSON function is used to convert a VARIANT value into a string containing the JSON representation of the value. This function is suitable for unloading data from a relational table to JSON format. References: [ COF-C03 ] SnowPro Core Certification Exam Study Guide

The correct answer is A. To return a tabular value for each input row.

A User-Defined Table Function, or UDTF, returns a set of rows instead of a single scalar value. This is different from a scalar UDF, which returns one value for each input row.

Why A is correct:

A UDTF produces tabular output. It can return multiple rows and multiple columns, depending on how the function is defined.

Example concept:

SELECT *

FROM TABLE(my_udtf(input_column));

This syntax shows that the function result is treated like a table.

Why the other options are incorrect:

B. Returning a pandas Series is associated with vectorized Python UDF behavior, not the general definition of a UDTF.

C. Returning one output row as a single column or value describes a scalar UDF, not a table function.

D. Processing input rows as pandas DataFrames is associated with Snowpark Python and vectorized processing patterns, but it is not the basic Snowflake definition of a UDTF.

Official Snowflake documentation reference:

Snowflake documentation defines a UDTF as a user-defined function that returns tabular results. It can return multiple rows for each input row.

Snowflake’s architecture allows for the independent scaling of compute resources, meaning you can increase or decrease the computational power as needed without affecting storage. This separation also means that storage can grow independently of compute usage, allowing for more flexible and cost-effective data management. 

 Snowflake supports several semi-structured data formats for loading data. The supported formats include JSON, Avro, and Parquet12. These formats allow for efficient storage and querying of data that does not conform to a traditional relational database schema.