Free Practice Questions for Snowflake SnowPro-Core Exam

In Snowflake, a Share is the object that contains all the information required to share a database with other Snowflake accounts. Shares are used to securely share data stored in Snowflake tables and views, enabling data providers to grant data consumers access to their datasets without duplicating data. When a database is shared, it can include one or more schemas, and each schema can contain tables, views, or both.

References:

Snowflake Documentation on Shares: Shares

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:

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GRANT DELETE ON TABLE my_table TO ROLE my_role;

Storing non-native values, such as dates and timestamps, in a VARIANT column in Snowflake can lead to slower query performance and increased storage consumption. VARIANT is a semi-structured data type that allows storing JSON, AVRO, ORC, Parquet, or XML data in a single column. When non-native data types are stored as VARIANT, Snowflake must perform implicit conversion to process these values, which can slow down query execution. Additionally, because the VARIANT data type is designed to accommodate a wide variety of data formats, it often requires more storage space compared to storing data in native, strongly-typed columns that are optimized for specific data types.

The performance impact arises from the need to parse and interpret the semi-structured data on the fly during query execution, as opposed to directly accessing and operating on optimally stored data in its native format. Furthermore, the increased storage consumption is a result of the overhead associated with storing data in a format that is less space-efficient than the native formats optimized for specific types of data.

References:

Snowflake Documentation on Semi-Structured Data: Semi-Structured Data

The settings shown in the image indicate that the data retention time in days is configured at two different levels: the account level and the object level. At the account level, the MIN_DATA_RETENTION_TIME_IN_DAYS is set to 5 days, and at the object level, the DATA_RETENTION_TIME_IN_DAYS is set to 2 days. Since the object level setting has a lower value, it takes precedence over the account level setting for the specific object. Therefore, the data will be retained for 2 days at the object level.References: Snowflake Documentation on Data Retention Policies

Resource monitors in Snowflake are tools used to track and control the consumption of compute resources, ensuring that usage stays within defined limits. These monitors can be created at the account level, allowing administrators to set overall resource consumption limits for the entire Snowflake account. Additionally, resource monitors can be set at the virtual warehouse level, enabling more granular control over the resources consumed by individual warehouses. This dual-level capability allows organizations to manage their Snowflake usage efficiently, preventing unexpected costs and optimizing performance.References: Snowflake Documentation on Resource Monitors

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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.

Snowflake's Continuous Data Protection (CDP) features encompass several mechanisms designed to protect data and ensure its availability and recoverability. Among these features, the one that has associated costs is Fail-safe.

Fail-safe is an additional layer of protection that kicks in after the Time Travel period expires. While Time Travel allows users to access historical data within a defined retention period (which can vary from 1 to 90 days depending on the Snowflake edition), Fail-safe provides a further 7 days (for a total of 7 additional days beyond the Time Travel period) during which Snowflake retains the data. This period is primarily intended for Snowflake's internal operations to recover data in the event of extreme scenarios, such as significant operational failures, and is not directly accessible by customers for data recovery purposes.

The associated costs with Fail-safe arise because Snowflake continues to store the data beyond the customer-specified Time Travel period, thereby incurring additional storage costs. It's important to note that while users do not incur direct costs for enabling Fail-safe (as it is an automatic feature of Snowflake), the extended storage of data during this period contributes to overall storage costs.

References:

Snowflake Documentation on Continuous Data Protection: Continuous Data Protection (CDP)

Snowflake Documentation on Fail-safe: Understanding Fail-safe

The REMOVE command is used in Snowflake to delete files from a stage after they have been successfully ingested into Snowflake tables. This command helps manage storage by allowing users to clean up staged files that are no longer needed, ensuring that the stage does not accumulate unnecessary data over time. Unlike DELETE, DROP, or TRUNCATE commands, which are used for managing data within Snowflake tables or dropping objects, REMOVE specifically targets the management of files in stages.References: Snowflake Documentation on Stages and File Management