Snowflake COF-C02 Practice Test - Questions Answers, Page 65

The REMOVE command in Snowflake is used to delete files from either external cloud storage locations or internal stages. This command helps manage staged files by removing them when they are no longer needed, which is useful for maintaining organization and managing storage costs.

References:

Snowflake Documentation: REMOVE Command

The search optimization service in Snowflake uses persistent data structures known as search access paths to improve the performance of point lookups. These structures enable efficient retrieval of data by reducing the amount of data scanned during queries.

Search Access Paths:

Search access paths are special indexing structures maintained by the search optimization service.

They store metadata about the distribution of data within tables, enabling faster lookups for specific values.

Point Lookups:

Point lookups involve searching for a specific value within a column.

By leveraging search access paths, Snowflake can quickly locate the exact micro-partition containing the value, minimizing the amount of data scanned.

Performance Improvement:

The use of search access paths significantly reduces query execution time for point lookups.

This is especially beneficial for large tables where scanning all micro-partitions would be computationally expensive.

References:

Snowflake Documentation: Search Optimization Service

Snowflake Documentation: Understanding Search Access Paths

In Snowflake, a tag can be associated with only one masking policy for each data type. This means that for a given data type, you can define a single masking policy to be applied when a tag is used. Tags and masking policies are part of Snowflake's data classification and governance features, allowing for data masking based on the context defined by the tags.

References:

Snowflake Documentation: Tag-Based Masking Policies

To unload data from a relational table to JSON format, you can use the TO_CHAR function. This function converts a number to a character string, which can then be serialized into JSON format. While there isn't a direct function specifically named for unloading to JSON, converting the necessary fields to a string representation is a common step in preparing data for JSON serialization.

References:

Snowflake Documentation: TO_CHAR Function

To ensure that the output will be a single file when unloading data into a stage, you should use the COPY option SINGLE=TRUE. This option specifies that the result of the COPY INTO command should be written to a single file, rather than multiple files.

References:

Snowflake Documentation: COPY INTO <location>

The search optimization service in Snowflake significantly improves the performance of range searches and equality searches. Range searches involve looking for values within a specific range (e.g., BETWEEN, <, >). Equality searches involve looking for values that match a specific value (e.g., =).

References:

Snowflake Documentation: Search Optimization Service

The Merge operator is considered a DML (Data Manipulation Language) operator in Snowflake. DML operators are used to modify data in tables, and the Merge operator specifically allows for conditional updates, inserts, or deletes in a target table based on the results of a join with a source table.

References:

Snowflake Documentation: Query Profile Overview

Snowflake Documentation: MERGE Statement

In Snowflake, reader accounts are special types of accounts that allow data sharing with external consumers without them having their own Snowflake account. The creation and management of reader accounts can be performed by users with the ACCOUNTADMIN role or the ORGADMIN role. The ACCOUNTADMIN role has comprehensive administrative privileges within a Snowflake account, including managing other accounts and roles. The ORGADMIN role, which is higher in hierarchy, oversees multiple accounts within an organization and can manage reader accounts across those accounts.

References:

Snowflake Documentation: Creating and Managing Reader Accounts

Masking policies in Snowflake are created and applied at the column level. These policies are used to obfuscate sensitive data by masking the values in a specific column. Masking policies can enforce data privacy and protection measures by dynamically masking the data based on the role of the user querying the data.

References:

Snowflake Documentation: Dynamic Data Masking