Sample Questions from SnowPro Core (COF-C02) by Snowflake

Preview a few questions below — answers are revealed when you take the exam.

1. A team needs to optimize data storage costs in Snowflake while maintaining query performance. They have a mix of frequently accessed and infrequently accessed data. What approach should they take?

   • Implement a multi-cluster warehouse setup with different sizes for handling varying query loads and use Snowflake's automatic clustering to optimize storage.
   • Migrate all data to a single large warehouse and use external storage solutions for infrequently accessed data to reduce costs.
   • Utilize Snowflake's time travel feature extensively to retain historical data versions and reduce the need for additional storage.
   • Apply row-level security policies to limit data access and reduce storage requirements by filtering out unnecessary data for users.

2. Consider the situation where a company wants to ensure data compliance with GDPR regulations using Snowflake. Which method solves it best?

   • Utilize Snowflake's data masking policies to obscure sensitive data fields and implement row access policies to restrict data access based on user roles.
   • Store all personal data in a separate database and use Snowflake's fail-safe feature to ensure data is recoverable for a specified period.
   • Encrypt all data at rest and in transit using Snowflake's built-in encryption features and maintain a detailed audit log of all data access.
   • Implement a data retention policy that automatically deletes personal data after a specified period and use Snowflake's secure views to control data access.

3. What strategy should be applied when designing a data pipeline in Snowflake to handle real-time data ingestion from multiple sources?

   • Use Snowflake's native integration with Kafka for streaming data ingestion, combined with Snowflake's stream feature to process and load data in real-time.
   • Implement a batch processing approach using Snowflake's Snowpipe for automated data loading from various sources at regular intervals.
   • Utilize external ETL tools to preprocess data before loading it into Snowflake, ensuring data quality and transformation are handled outside the platform.
   • Create a custom API within Snowflake to receive data from multiple sources and use stored procedures to process and store the data in real-time.

4. How would you decide the best method for scaling a Snowflake environment to accommodate a sudden increase in data volume and user concurrency?

   • Increase the size of the virtual warehouse to handle higher concurrency and use Snowflake's auto-scaling feature to adjust resources dynamically.
   • Partition the data across multiple tables and use Snowflake's clustering keys to optimize query performance on large datasets.
   • Implement a data archiving strategy to move older, less frequently accessed data to cheaper storage tiers and free up space in the main warehouse.
   • Use Snowflake's zero-copy cloning feature to create multiple clones of the database for parallel query execution and load balancing.

5. What configuration is most appropriate for ensuring high availability and disaster recovery in a Snowflake deployment?

   • Configure Snowflake to use a multi-region setup with automatic failover and replication of data across regions to ensure data durability and availability.
   • Set up a secondary Snowflake account in a different region and use cross-region replication to maintain a copy of the data for disaster recovery purposes.
   • Use Snowflake's time travel feature to retain historical versions of data and enable fail-safe to protect against data loss in case of disasters.
   • Implement a backup strategy using Snowflake's native backup and restore features and store backups in an external cloud storage service for added security.