Snowflake ARA-C01 Practice Test - Questions Answers, Page 10

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.

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]

A masking policy is a feature of Snowflake that allows masking sensitive data in query results based on the role of the user and the condition of the data. A masking policy can be applied to a column in a table or a view, and it can use another column in the same table or view as a conditional column.A conditional column is a column that determines whether the masking policy is applied or not based on its value1.

In this case, the requirement can be met by using a masking policy on the username column with event_timestamp as a conditional column. The masking policy can use a function that masks the username if the event_timestamp is older than six months based on the current date, and returns the original username otherwise.The masking policy can be applied to the user_events table, and it will also be applied when creating views using the table or cloning the table2.

The other options are not correct because:

A) Using a masking policy on the username column using an entitlement table with valid dates would require creating another table that stores the valid dates for each username, and joining it with the user_events table in the masking policy function. This would add complexity and overhead to the masking policy, and it would not use the event_timestamp column as the condition for masking.

B) Using a row level policy on the user_events table using an entitlement table with valid dates would require creating another table that stores the valid dates for each username, and joining it with the user_events table in the row access policy function. This would filter out the rows that have event_timestamp older than six months based on the valid dates, instead of masking the username column. This would not meet the requirement of masking the username, and it would also reduce the visibility of the event data.

D) Using a secure view on the user_events table using a case statement on the username column would require creating a view that uses a case expression to mask the username column based on the event_timestamp column. This would meet the requirement of masking the username, but it would not be applied when cloning the table. A secure view is a view that prevents the underlying data from being exposed by queries on the view.However, a secure view does not prevent the underlying data from being exposed by cloning the table3.

1:Masking Policies | Snowflake Documentation

2: Using Conditional Columns in Masking Policies | Snowflake Documentation

3: Secure Views | Snowflake Documentation

The Snowflake system functions used to view and monitor the clustering metadata for a table are:

SYSTEM$CLUSTERING_INFORMATION

SYSTEM$CLUSTERING_DEPTH

Comprehensive But Short Explanation:

The SYSTEM$CLUSTERING_INFORMATION function in Snowflake returns a variety of clustering information for a specified table. This information includes the average clustering depth, total number of micro-partitions, total constant partition count, average overlaps, average depth, and a partition depth histogram. This function allows you to specify either one or multiple columns for which the clustering information is returned, and it returns this data in JSON format.

The SYSTEM$CLUSTERING_DEPTH function computes the average depth of a table based on specified columns or the clustering key defined for the table. A lower average depth indicates that the table is better clustered with respect to the specified columns. This function also allows specifying columns to calculate the depth, and the values need to be enclosed in single quotes.

SYSTEM$CLUSTERING_INFORMATION: Snowflake Documentation

SYSTEM$CLUSTERING_DEPTH: Snowflake Documentation

Event notifications in Snowpipe are messages sent by cloud storage providers to notify Snowflake of new or modified files in a stage. Snowpipe uses these notifications to trigger data loading from the stage to the target table. When a pipe is paused, event messages received for the pipe enter a limited retention period, which varies depending on the cloud storage provider. If the pipe is not resumed within the retention period, the event messages will be discarded and the data will not be loaded automatically. To load the data, the pipe must be resumed and the COPY command must be executed manually. This is a characteristic of event notifications in Snowpipe that distinguishes them from other options.Reference:Snowflake Documentation: Using Snowpipe,Snowflake Documentation: Pausing and Resuming a Pipe

The best design to store and analyze large amounts of structured and semi-structured data for different business units and departments is to use a centralized Snowflake database for core business data, and use separate databases for departmental or project-specific data. This design allows for scalability, security, and cost efficiency by leveraging Snowflake's features such as:

Database cloning:Cloning a database creates a zero-copy clone that shares the same data files as the original database, but can be modified independently. This reduces storage costs and enables fast and consistent data replication for different purposes.

Database sharing:Sharing a database allows granting secure and governed access to a subset of data in a database to other Snowflake accounts or consumers. This enables data collaboration and monetization across different business units or external partners.

Warehouse scaling:Scaling a warehouse allows adjusting the size and concurrency of a warehouse to match the performance and cost requirements of different workloads. This enables optimal resource utilization and flexibility for different data analysis needs.Reference:Snowflake Documentation: Database Cloning,Snowflake Documentation: Database Sharing, [Snowflake Documentation: Warehouse Scaling]