COF-C02 Exam Dumps - SnowPro Core Certification Exam

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.

While Snowflake primarily deals with structured and semi-structured data, it also has the capability to handle unstructured data. Unstructured data can be stored in Snowflake using variants of SQL data types in tables, which can be permanent tables. These permanent tables, while traditionally used for structured or semi-structured data (like JSON, Avro, or Parquet), can also accommodate unstructured data in the form of binary formats or strings, offering flexibility in data storage and analysis. However, the management and querying of unstructured data in Snowflake may require additional considerations compared to structured data.References: Snowflake Documentation on Data Types

When querying the maximum value of a date field, Snowflake retrieves metadata stored in its Database Storage layer.

Snowflake stores data in columnar format with metadata (e.g., min/max values per column per micro-partition), enabling efficient queries like MAX() to use metadata without scanning all rows.

Why Other Options Are Incorrect:

B. Query Processing: Processes query execution but does not store metadata.

C. Cloud Services: Manages metadata and services but does not execute the query directly.

D. Compute: Executes queries but accesses data from Database Storage.

References:

Snowflake Architecture

The property mins_to_bypass_network_policy is set at the account level in Snowflake. This setting allows administrators to specify a time frame during which users can bypass network policies that have been set on their account. It is particularly useful in scenarios where temporary access needs to be granted from IPs not covered by the existing network policies. By adjusting this property at the account level, Snowflake administrators can manage and enforce network access controls efficiently across the entire account.

References:

Snowflake Documentation on Network Policies: Network Policies

When creating a virtual warehouse in Snowflake, users have the option to set several properties to manage its behavior and resource usage. Two of these optional properties are Auto-suspend and Resource monitor.

Auto-suspend: This property defines the period of inactivity after which the warehouse will automatically suspend. This helps in managing costs by stopping the warehouse when it is not in use.

CREATE WAREHOUSE my_warehouse

WITH WAREHOUSE_SIZE = 'XSMALL'

AUTO_SUSPEND = 300; -- Auto-suspend after 5 minutes of inactivity

Resource monitor: Users can assign a resource monitor to a warehouse to control and limit the amount of credit usage. Resource monitors help in setting quotas and alerts for warehouse usage.

CREATE WAREHOUSE my_warehouse

WITH WAREHOUSE_SIZE = 'XSMALL'

RESOURCE_MONITOR = 'my_resource_monitor';

References:

Snowflake Documentation: Creating Warehouses

Snowflake Documentation: Resource Monitors

The SHOW TABLES command in Snowsight provides metrics for storage usage, including:

Time Travel bytes (A): Amount of storage used for Time Travel data.

Fail-safe bytes (C): Amount of storage used for Fail-safe data.

References:

Snowflake Documentation: SHOW TABLES

Snowflake Table Storage Metrics

Snowflake supports semi-structured data types that can store complex data structures within a single column. The VARIANT data type can hold structured and semi-structured data formats such as JSON, Avro, ORC, Parquet, and XML. The ARRAY data type is also used for semi-structured data and can store an ordered list of elements. These data types enable users to work with semi-structured data directly in Snowflake without needing to flatten the data into a relational schema first.References: Snowflake Documentation on Semi-Structured Data Types

The ACCESS_HISTORY view in Snowflake provides information about:

Who accessed the data.

When the data was accessed.

What columns were accessed.

This view is part of Snowflake's Account Usage Schema and is designed for detailed auditing of data access.

Why Other Options Are Incorrect:

A. QUERY_HISTORY: Tracks query execution details but does not provide column-level access information.

C. EVENT_USAGE_HISTORY: Tracks usage of Snowflake events but not data access.

D. OBJECT_DEPENDENCIES: Tracks object dependencies, unrelated to data access details.

References:

ACCESS_HISTORY View Documentation