Snowflake COF-C02 Practice Test - Questions Answers, Page 60

When deciding to use a secure view, several considerations come into play, especially concerning security and performance:

A . No details of the query execution plan will be available in the query profiler: Secure views are designed to prevent the exposure of the underlying data and the view definition to unauthorized users. Because of this, the detailed execution plans for queries against secure views are not available in the query profiler. This is intended to protect sensitive data from being inferred through the execution plan.

C . Secure views do not take advantage of the same internal optimizations as standard views: Secure views, by their nature, limit some of the optimizations that can be applied compared to standard views. This is because they enforce row-level security and mask data, which can introduce additional processing overhead and limit the optimizer's ability to apply certain efficiencies that are available to standard views.

B . Once created, there is no way to determine if a view is secure or not is incorrect because metadata about whether a view is secure can be retrieved from the INFORMATION_SCHEMA views or by using the SHOW VIEWS command.

D . It is not possible to create secure materialized views is incorrect because the limitation is not on the security of the view but on the fact that Snowflake currently does not support materialized views with the same dynamic data masking and row-level security features as secure views.

E . The view definition of a secure view is still visible to users by way of the information schema is incorrect because secure views specifically hide the view definition from users who do not have the privilege to view it, ensuring that sensitive information in the definition is not exposed.

One key strategy to lower compute resource credit consumption in Snowflake is by automating the suspension and resumption of virtual warehouses. Virtual warehouses consume credits when they are running, and managing their operational times effectively can lead to significant cost savings.

A . Setting up a multi-cluster virtual warehouse increases parallelism and throughput but does not directly lower credit consumption. It is more about performance scaling than cost efficiency.

B . Resizing the virtual warehouse to a larger size increases the compute resources available for processing queries, which increases the credit consumption rate. This option does not help in lowering costs.

C . Automating the virtual warehouse suspension and resumption settings: This is a direct method to manage credit consumption efficiently. By automatically suspending a warehouse when it is not in use and resuming it when needed, you can avoid consuming credits during periods of inactivity. Snowflake allows warehouses to be configured to automatically suspend after a specified period of inactivity and to automatically resume when a query is submitted that requires the warehouse.

D . Increasing the maximum cluster count parameter for a multi-cluster virtual warehouse would potentially increase credit consumption by allowing more clusters to run simultaneously. It is used to scale up resources for performance, not to reduce costs.

Automating the operational times of virtual warehouses ensures that you only consume compute credits when the warehouse is actively being used for queries, thereby optimizing your Snowflake credit usage.

In a scenario where a Snowflake user queries only a small number of rows that require significant processing and the data in the table does not change frequently, the most effective way to optimize performance is by creating a materialized view based on the query. Materialized views store the result of the query and can significantly reduce the computation time for queries that are executed frequently over unchanged data.

Why Materialized Views: Materialized views precompute and store the result of the query. This is especially beneficial for queries that require heavy processing. Since the data does not change frequently, the materialized view will not need to be refreshed often, making it an ideal solution for this use case.

Implementation Steps:

To create a materialized view, use the following SQL command:

CREATE MATERIALIZED VIEW my_materialized_view AS SELECT ... FROM my_table WHERE ...;

When the query is run, Snowflake uses the precomputed results from the materialized view, thus skipping the need for recalculating the data and improving query performance.

To use the overwrite option on insert in Snowflake, the DELETE privilege must be granted to the role. This is because overwriting data during an insert operation implicitly involves deleting the existing data before inserting the new data.

Understanding the Overwrite Option: The overwrite option (INSERT OVERWRITE) allows you to replace existing data in a table with new data. This operation is particularly useful for batch-loading scenarios where the entire dataset needs to be refreshed.

Why DELETE Privilege is Required: Since the overwrite operation involves removing existing rows in the table, the executing role must have the DELETE privilege to carry out both the deletion of old data and the insertion of new data.

Granting DELETE Privilege:

To grant the DELETE privilege to a role, an account administrator can execute the following SQL command:

sqlCopy code

GRANT DELETE ON TABLE my_table TO ROLE my_role;

For minimizing the cost of large tables that are used to store transitory data, which does not need to be protected against failures because it can be reconstructed outside of Snowflake, the best table type to use is Transient. Transient tables in Snowflake are designed for temporary or transitory data storage and offer reduced storage costs compared to permanent tables. However, unlike temporary tables, they persist across sessions until explicitly dropped.

Why Transient Tables: Transient tables provide a cost-effective solution for storing data that is temporary but needs to be available longer than a single session. They have lower data storage costs because Snowflake does not maintain historical data (Time Travel) for as long as it does for permanent tables.

Creating a Transient Table:

To create a transient table, use the TRANSIENT keyword in the CREATE TABLE statement:

CREATE TRANSIENT TABLE my_transient_table (...);

Use Case Considerations: Transient tables are ideal for scenarios where the data is not critical, can be easily recreated, and where cost optimization is a priority. They are suitable for development, testing, or staging environments where data longevity is not a concern.

In Snowflake, the default access level for any securable object (such as a table, view, or schema) is 'No access' until explicit access is granted. This means that when an object is created, only the owner of the object and roles with the necessary privileges can access it. Other users or roles will not have any form of access to the object until it is explicitly granted.

This design adheres to the principle of least privilege, ensuring that access to data is tightly controlled and that users and roles only have the access necessary for their functions. To grant access, the owner of the object or a role with the GRANT option can use the GRANT statement to provide specific privileges to other users or roles.

For example, to grant SELECT access on a table to a specific role, you would use a command similar to:

GRANT SELECT ON TABLE my_table TO ROLE my_role;

In Snowflake, resizing a virtual warehouse that is currently suspended does not immediately provision the new compute resources. Instead, the change in size is recorded, and the additional compute resources are provisioned when the warehouse is resumed. This means that the action of resizing a suspended warehouse does not cause it to resume operation automatically. The warehouse remains suspended until an explicit command to resume it is issued, or until it automatically resumes upon the next query execution that requires it.

This behavior allows for efficient management of compute resources, ensuring that credits are not consumed by a warehouse that is not in use, even if its size is adjusted while it is suspended.

In Snowflake, the use of streams impacts how the data retention period for a table is handled, particularly in scenarios where the stream has not been consumed. The key point to understand is that Snowflake's streams are designed to capture data manipulation language (DML) changes such as INSERTS, UPDATES, and DELETES that occur on a source table. Streams maintain a record of these changes until they are consumed by a DML operation or a COPY command that references the stream.

When a stream is created on a table and remains unconsumed, Snowflake extends the data retention period of the table to ensure that the changes captured by the stream are preserved. This extension is specifically up to the point in time represented by the stream's offset, which effectively ensures that the data necessary for consuming the stream's contents is retained. This mechanism is in place to prevent data loss and ensure the integrity of the stream's data, facilitating accurate and reliable data processing and analysis based on the captured DML changes.

This behavior emphasizes the importance of managing streams and their consumption appropriately to balance between data retention needs and storage costs. It's also crucial to understand how this temporary extension of the data retention period impacts the overall management of data within Snowflake, including aspects related to data lifecycle, storage cost implications, and the planning of data consumption strategies.

References:

Snowflake Documentation on Streams: Using Streams

Snowflake Documentation on Data Retention: Understanding Data Retention

Access History in Snowflake is primarily utilized for compliance auditing. The Access History feature provides detailed logs that track data access and modifications, including queries that read from or write to database objects. This information is crucial for organizations to meet regulatory requirements and to perform audits related to data access and usage.

Role of Access History: Access History logs are designed to help organizations understand who accessed what data and when. This is particularly important for compliance with various regulations that require detailed auditing capabilities.

How Access History Supports Compliance Auditing:

By providing a detailed log of access events, organizations can trace data access patterns, identify unauthorized access, and ensure that data handling complies with relevant data protection laws and regulations.

Access History can be queried to extract specific events, users, time frames, and accessed objects, making it an invaluable tool for compliance officers and auditors.