Snowflake SnowPro Core Practice Test - Questions Answers, Page 5

TheResult cacheis used in Snowflake to cache the data output from SQL queries. This feature is designed to improve performance by storing the results of queries for a period of time. When the same or similar query is executed again, Snowflake can retrieve the result from this cache instead of re-computing the result, which saves time and computational resources.

Snowflake Documentation on Query Results Cache

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One of the key features of Snowflake's architecture is its unique approach to eliminating resource contention through the use of virtual warehouses. This is achieved by separating storage and compute resources, allowing multiple virtual warehouses to operate independently on the same data without affecting each other. This means that different workloads, such as loading data, running queries, or performing complex analytics, can be processed simultaneously without any performance degradation due to resource contention.

Snowflake Documentation on Virtual Warehouses

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Materialized Views in Snowflake are designed to store the result of a query and can be refreshed to maintain up-to-date data. However, they have certain limitations, one of which is that they cannot be defined using a JOIN clause. This means that a Materialized View can only be created based on a single source table and cannot combine data from multiple tables using JOIN operations.

Snowflake Documentation on Materialized Views

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Snowflake Time Travel is a powerful feature that allows users to access historical data within a defined period. It enables two key capabilities:

B) Restoring data-related objects that have been deleted within the past 90 days: Time Travel can be used to restore tables, schemas, and databases that have been accidentally or intentionally deleted within the Time Travel retention period.

C) Conducting point-in-time analysis for BI reporting: It allows users to query historical data as it appeared at a specific point in time within the Time Travel retention period, which is crucial for business intelligence and reporting purposes.

While Time Travel does allow querying of past data, it is limited to the retention period set for the Snowflake account, which is typically 1 day for standard accounts and can be extended up to 90 days for enterprise accounts. It does not enable querying or restoring objects created or deleted beyond the retention period, nor does it provide analysis over all periods of time.

Snowflake Documentation on Time Travel

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Snowflake credits are the unit of measure for the compute resources used in Snowflake. The number of credits consumed depends on the size of the virtual warehouse and the time it is running. Larger warehouses consume more credits per hour than smaller ones, and credits are billed for the time the warehouse is active, regardless of the actual usage within that time.

Snowflake's architecture is designed to handle high concurrency workloads through several features, two of which are particularly effective:

B) The use of warehouse auto scaling: This feature allows Snowflake to automatically adjust the compute resources allocated to a virtual warehouse in response to the workload. If there is an increase in concurrent queries, Snowflake can scale up the resources to maintain performance.

D) Use of multi-clustered warehouses: Multi-clustered warehouses enable Snowflake to run multiple clusters of compute resources simultaneously. This allows for the distribution of queries across clusters, thereby reducing the load on any single cluster and improving the system's ability to handle a high number of concurrent queries.

These features ensure that Snowflake can manage varying levels of demand without manual intervention, providing a seamless experience even during peak usage.

Snowflake Documentation on Virtual Warehouses

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To address a high volume of queries queuing in a warehouse, Snowflake recommends two best practices:

A) Use multi-clustered warehousing to scale out warehouse capacity: This approach allows for the distribution of queries across multiple clusters within a warehouse, effectively managing the load and reducing the queue volume.

B) Scale up the warehouse size to allow Queries to execute faster: Increasing the size of the warehouse provides more compute resources, which can reduce the time it takes for queries to execute and thus decrease the number of queries waiting in the queue.

These strategies help to optimize the performance of the warehouse by ensuring that resources are scaled appropriately to meet demand.

Snowflake Documentation on Multi-Cluster Warehousing

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Secure data sharing in Snowflake allows users to share various objects between Snowflake accounts without physically copying the data, thus not consuming additional storage. Among the options provided, external tables can be shared through secure data sharing. External tables are used to query data directly from files in a stage without loading the data into Snowflake tables, making them suitable for sharing across different Snowflake accounts.

Snowflake Documentation on Secure Data Sharing

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In Snowflake, a reader account is a type of account that is intended for consuming shared data rather than performing any data management or DDL operations. TheCREATE SHAREcommand is used to share data from your account with another account, which is not a capability provided to reader accounts. Reader accounts are typically restricted from creating shares, as their primary purpose is to read shared data rather than to share it themselves.

Snowflake Documentation on Reader Accounts

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