Free Snowflake COF-C02 Practice Exam with Questions & Answers | Set: 2

System tags help enable data classification for governance, privacy, and compliance. For example, Snowflake uses system-defined tags like SNOWFLAKE.CORE.DATA_CLASSIFICATION to detect and label sensitive data (e.g., PII, financial info).

In Snowflake, a table's clustering depth indicates the degree of micro-partition overlap based on the clustering keys defined for the table. A clustering depth of 1 implies that the table has no overlapping micro-partitions. This is an optimal scenario, indicating that the table's data is well-clustered according to the specified clustering keys. Well-clustered data can lead to more efficient query performance, as it reduces the amount of data scanned during query execution and improves the effectiveness of data pruning.

TheACCOUNTADMINrole in Snowflake has the highest level of privileges, including the ability to manage accounts, users, roles, and all objects within the account. This role is specifically granted the permission to rename an account and specify whether the original URL can be used to access the renamed account. TheACCOUNTADMINrole encompasses broad administrative capabilities, ensuring that users assigned this role can perform critical account management tasks.

Snowflake recommends not exposing sequence-generated columns in secure views. Secure views are used to protect sensitive data by ensuring that users can only access data for which they have permissions. Exposing sequence-generated columns can potentially reveal information about the underlying data structure or the number of rows, which might be sensitive.

Create Secure Views:Define secure views using theSECUREkeyword to ensure they comply with Snowflake's security policies.

Exclude Sensitive Columns:When creating secure views, exclude columns that might expose sensitive information, such as sequence-generated columns.

CREATE SECURE VIEW secure_view AS

SELECT col1, col2

FROM sensitive_table

WHERE sensitive_column IS NOT NULL;

TheCURRENT_TASK_GRAPHStable function is designed to provide information on Directed Acyclic Graphs (DAGs) that are currently scheduled or executing within Snowflake. This function offers insights into the structure and status of task chains, enabling users to monitor and troubleshoot task executions. DAGs in Snowflake represent sequences of tasks with dependencies, and understanding their current state is crucial for managing complex workflows.

Cross-region replication ensures data availability across multiple cloud regions. In the event of a regional outage, Snowflake accounts with this feature can fail over to a different region.

SnowSQL, Snowflake's command-line client, can be used to diagnose and troubleshoot network connections. SnowSQL provides various commands and options to test connectivity, configure network settings, and troubleshoot issues related to network connections between the client and Snowflake.

Snowflake Documentation: SnowSQL

When creating a Snowpark-optimized virtual warehouse in Snowflake, the minimum size requirement is Small. Snowpark is designed to handle data processing workloads efficiently, and the Small size ensures adequate resources for such tasks.

Virtual Warehouse Sizes:

Snowflake offers different sizes for virtual warehouses, ranging from X-Small to 6X-Large.

Each size corresponds to a specific level of compute resources.

Minimum Size Requirement for Snowpark:

A Small virtual warehouse is the minimum size required to optimize performance and resource allocation for Snowpark workloads.

This ensures that the warehouse has sufficient capacity to handle data processing and transformation tasks efficiently.

Snowflake Documentation: Virtual Warehouse Sizes

Object tagging in Snowflake allows users to assign metadata labels to various database objects to improve data governance and access control. Tags can be used to categorize and manage data based on business needs, helping to enforce governance policies and streamline database administration.

Snowflake Documentation: Object Tagging

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.

Snowflake Documentation: REMOVE Command

TheACCESS_HISTORYview in Snowflake is a powerful tool for enhancing data governance, especially concerning monitoring and auditing data access patterns for both read and write operations. The key ways in whichACCESS_HISTORYenhances overall data governance are:

B. Provides a unified picture of what data was accessed and when it was accessed:This view logs details about query executions, including the objects (tables, views) accessed and the timestamps of these accesses. It's instrumental in auditing and compliance scenarios, where understanding the access patterns to sensitive data is critical.

E. Determines whether a given row in a table can be accessed by the user by filtering the data based on a given policy:While this option is a bit of a misinterpretation of whatACCESS_HISTORYdirectly offers, it indirectly supports data governance by providing the information necessary to analyze access patterns. This analysis can then inform policy decisions, such as implementing Row-Level Security (RLS) to restrict access to specific rows based on user roles or attributes.

ACCESS_HISTORYdoes not automatically apply data masking or tag columns with personal information. However, the insights derived from analyzingACCESS_HISTORYcan be used to identify sensitive data and inform the application of masking policies or other security measures.

Dynamic Data Masking (DDM) can be applied to:

Tables

Views

It cannot be applied to materialized views or external tables as those do not support the runtime evaluation of masking policies.

Access History in Snowflake is primarily utilized for compliance auditing. The Access History feature provides detailed logs that track data access and modifications, includingqueries 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.