In the scenario described, where a third data domain needs access to two existing data products in a Snowflake account structured according to Data Mesh principles, the best approach is to utilize Snowflake's Data Exchange functionality. Option D is correct as it facilitates the sharing and governance of data across different domains efficiently and securely. Data Exchange allows domains to publish and subscribe to live data products, enabling real-time data collaboration and access management in a governed manner. This approach is in line with Data Mesh principles, which advocate for decentralized data ownership and architecture, enhancing agility and scalability across the organization.
Reference:
Snowflake Documentation on Data Exchange
Articles on Data Mesh Principles in Data Management

* The correct answer is A because it improves the performance of queries by reducing the amount of data scanned and processed. By adding a create_date field with a timestamp data type, Snowflake can automatically cluster the table based on this field and prune the micro-partitions that do not match the filter condition. This avoids the need to parse the JSON data and access the variant field for every record.
* Option B is incorrect because it does not improve the performance of queries. By adding a create_date field with a varchar data type, Snowflake cannot automatically cluster the table based on this field and
* prune the micro-partitions that do not match the filter condition. This still requires parsing the JSON data and accessing the variant field for every record.
* Option C is incorrect because it does not address the root cause of the performance issue. By validating the size of the warehouse being used, Snowflake can adjust the compute resources to match the data volume and parallelize the query execution. However, this does not reduce the amount of data scanned and processed, which is the main bottleneck for queries on JSON data.
* Option D is incorrect because it adds unnecessary complexity and overhead to the data loading and querying process. By incorporating the use of multiple tables partitioned by date ranges, Snowflake can reduce the amount of data scanned and processed for queries that specify a date range. However, this requires creating and maintaining multiple tables, loading data into the appropriate table based on the date, and joining the tables for queries that span multiple date ranges. References:
* Snowflake Documentation: Loading Data Using Snowpipe: This document explains how to use Snowpipe to continuously load data from external sources into Snowflake tables. It also describes the syntax and usage of the COPY INTO command, which supports various options and parameters to control the loading behavior, such as ON_ERROR, PURGE, and SKIP_FILE.
* Snowflake Documentation: Date and Time Data Types and Functions: This document explains the different data types and functions for working with date and time values in Snowflake. It also describes how to set and change the session timezone and the system timezone.
* Snowflake Documentation: Querying Metadata: This document explains how to query the metadata of the objects and operations in Snowflake using various functions, views, and tables. It also describes how to access the copy history information using the COPY_HISTORY function or the COPY_HISTORY view.
* Snowflake Documentation: Loading JSON Data: This document explains how to load JSON data into Snowflake tables using various methods, such as the COPY INTO command, the INSERT command, or the PUT command. It also describes how to access and query JSON data using the dot notation, the FLATTEN function, or the LATERAL join.
* Snowflake Documentation: Optimizing Storage for Performance: This document explains how to optimize the storage of data in Snowflake tables to improve the performance of queries. It also describes the concepts and benefits of automatic clustering, search optimization service, and materialized views.

This design meets all the requirements for the data pipeline. Snowpipe is a feature that enables continuous data loading into Snowflake from object storage using event notifications. It is efficient, scalable, and serverless, meaning it does not require any infrastructure or maintenance from the user. Streams and tasks are features that enable automated data pipelines within Snowflake, using change data capture and scheduled execution. They are also efficient, scalable, and serverless, and they simplify the data transformation process.
External functions are functions that can invoke external services or APIs from within Snowflake. They can be used to integrate with Amazon Comprehend and perform sentiment analysis on the data. The results can be written back to a Snowflake table using standard SQL commands. Snowflake Marketplace is a platform that allows data providers to share data with data consumers across different accounts, regions, and cloud platforms. It is a secure and easy way to make data publicly available to other companies.
Snowpipe Overview | Snowflake Documentation
Introduction to Data Pipelines | Snowflake Documentation
External Functions Overview | Snowflake Documentation
Snowflake Data Marketplace Overview | Snowflake Documentation

A multiple-account design strategy is a way of organizing Snowflake accounts into logical groups based on different criteria, such as cloud provider, region, environment, or business unit. A multiple-account design strategy can help achieve various goals, such as cost optimization, performance isolation, security compliance, and data sharing1. In this question, the scenarios that would be most cost-effective with a multiple-account design strategy are:
The company wants to clone a production database that resides on AWS to a development database that resides on Azure. This scenario would benefit from a multiple-account design strategy because it would allow the company to leverage the cross-cloud replication feature of Snowflake, which enables replicating databases across different cloud platforms and regions. This feature can help reduce the data transfer costs and latency, as well as provide high availability and disaster recovery2.
The company security policy mandates the use of different Active Directory instances for the development, test, and production environments. This scenario would benefit from a multiple-account design strategy because it would allow the company to use different federated authentication methods for each environment, and integrate them with different Active Directory instances. This can help improve the security and governance of the access to the Snowflake accounts, as well as simplify the user management and provisioning3.
The other scenarios would not be most cost-effective with a multiple-account design strategy, because:
The company needs to share data between two databases, where one must support Payment Card Industry Data Security Standard (PCI DSS) compliance but the other one does not. This scenario can be handled within a single Snowflake account, by using secure views and secure UDFs to mask or filter the sensitive data, and applying the appropriate roles and privileges to the users who access the data. This can help achieve the PCI DSS compliance without incurring the additional costs of managing multiple accounts4.
The company needs to support different role-based access control features for the development, test, and production environments. This scenario can also be handled within a single Snowflake account, by using the native role-based access control (RBAC) features of Snowflake, such as roles, grants, and privileges, to define different access levels and permissions for each environment. This can help ensure the security and integrity of the data and the objects, as well as the separation of duties and responsibilities among the users.
The company must use a specific network policy for certain users to allow and block given IP addresses. This scenario can also be handled within a single Snowflake account, by using the network policy feature of Snowflake, which enables creating and applying network policies to restrict the IP addresses that can access the Snowflake account. This can help prevent unauthorized access and protect the data from malicious attacks.
Reference:
Designing Your Snowflake Topology
Cross-Cloud Replication
Configuring Federated Authentication and SSO
Using Secure Views and Secure UDFs to Comply with PCI DSS
[Understanding Access Control in Snowflake]
[Network Policies]