1) In Snowflake, what is the impact of a zero-copy clone on storage costs and performance?

2) Which of the following techniques can be used to reduce the query execution time in Snowflake for large, frequently queried tables?

3) If you need to continuously load large volumes of semi-structured data (e.g., JSON files) into Snowflake and minimize query latency, which Snowflake features would you use?

4) How can you improve query performance on semi-structured data (like JSON or Avro) stored in a Snowflake table?

5) What role does micro-partitioning play in Snowflake’s architecture, and how does it affect query performance?

6) You are tasked with creating a data pipeline that ingests streaming data into Snowflake for real-time analytics. Which combination of Snowflake features would be most suitable?

7) What is the primary advantage of using Snowflake's Multi-cluster Warehouses over single-cluster warehouses?

8) How can Snowflake’s "Task" feature be used in combination with streams to ensure that no data is lost or processed twice in an event-driven architecture?

9) What happens when you run a MERGE statement in Snowflake on a table with clustering enabled, and how can it impact performance?

10) How does Snowflake handle the execution of queries that involve data sharing across multiple accounts within the same Snowflake environment?

11) Which of the following best describes the way Snowflake manages query performance for concurrent users?

12) In a multi-region deployment, how can you ensure data consistency and high availability in Snowflake when a region goes offline?

13) Which of the following scenarios would benefit most from using Snowflake’s external tables feature?

14) You are running queries on a large table with high data insertion rates. How can you ensure that query performance remains consistent without manual intervention?

15) How does Snowflake’s security model ensure the privacy of shared data without sacrificing performance?

16) You are designing a Snowflake data warehouse for a retail company. The data will be loaded from multiple sources, including on-premise databases, cloud storage, and IoT devices. Which combination of Snowflake features will allow you to automate and streamline the data ingestion process?

17) You are managing a data pipeline that loads raw transaction data into Snowflake every hour. You need to ensure that downstream reports always reflect the latest data while avoiding performance degradation. What is the best approach?

18) A financial services company needs to run regular, complex analytical queries on historical data spanning several years. The data is frequently updated and includes both structured and semi-structured formats. Which design pattern in Snowflake would ensure optimal query performance?

19) You are tasked with creating a real-time dashboard that shows the latest sales figures. Data from various sources is ingested into Snowflake every minute. What Snowflake features would you use to keep the dashboard updated without overwhelming the system?

20) Your company wants to reduce storage costs while still retaining data for compliance purposes. You need to store large datasets that will be infrequently accessed, but still available for occasional auditing. What is the best strategy in Snowflake?