SnowPro Core Certification COF-C02 Release Date

The purpose of an External Function in Snowflake is to call code that executes outside of the Snowflake environment. This allows Snowflake to interact with external services and leverage functionalities that are not natively available within Snowflake, such as calling APIs or running custom code hosted on cloud services3.

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

B. The use of warehouse auto scaling: This feature allows Snowflake to automatically adjust the compute resources allocated to a virtual warehouse in response to the workload. If there is an increase in concurrent queries, Snowflake can scale up the resources to maintain performance.

D. Use of multi-clustered warehouses: Multi-clustered warehouses enable Snowflake to run multiple clusters of compute resources simultaneously. This allows for the distribution of queries across clusters, thereby reducing the load on any single cluster and improving the system’s ability to handle a high number of concurrent queries.

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

References:

Snowflake Documentation on Virtual Warehouses

SnowPro® Core Certification Study Guide

 Snowflake supports several methods for authenticating users, including federated authentication, key-pair authentication, and OAuth. Federated authentication allows users to authenticate using their organization’s identity provider. Key-pair authentication uses a public-private key pair for secure login, and OAuth is an open standard for access delegation commonly used for token-based authentication. References: Authentication policies | Snowflake Documentation, Authenticating to the server | Snowflake Documentation, External API authentication and secrets | Snowflake Documentation.

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.

References:

Snowflake Documentation on Data Loading Considerations

[COF-C02] SnowPro Core Certification Exam Study Guide