Pass the Snowflake SnowPro Core Certification COF-C02 Questions and answers with CertsForce

 The COPY INTO SQL command with the option SINGLE=FALSE is used to unload data into multiple files. This option allows the data to be split into multiple files during the unload process. References: SnowPro Core Certification COPY INTO SQLcommand unload multiple files

 Search optimization in Snowflake is designed to improve the performance of queries that are selective and involve point lookup operations using equality and IN predicates. It isparticularly beneficial for queries that access columns with a high number of distinct values1.

References = [COF-C02] SnowPro Core Certification Exam Study Guide, Snowflake Documentation

The Snowflake Cloud Services layer performs a variety of tasks, including the management of metadata and the parsing and optimization of queries. This layer is responsible for coordinating activities across Snowflake, including user session management, security, and query compilation3.

 In Snowflake, both materialized views and transient tables will incur storage charges because they store data. They will also incur compute charges when queries are run against them, as compute resources are used to process the queries. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 Organizing files into logical paths can significantly improve the efficiency of data loading from an external stage. This practice helps in managing and locating files easily, which can be particularly beneficial when dealing with large datasets or complex directory structures1.

 Users are responsible for data storage costs in Snowflake until the data expires from the Fail-safe period. Fail-safe is the final stage in the data lifecycle, following Time Travel, and provides additional protection against accidental data loss. Once data exits the Fail-safe state, users are no longer billed for its storage

Snowflake recommends file sizes between 100-250 MB compressed when loading data to optimize parallel processing. Smaller, compressed files can be loaded in parallel, which maximizes the efficiency of the virtual warehouses and speeds up the data loading process

 In a Snowflake role hierarchy, the top-level role is ACCOUNTADMIN. This role has the highest level of privileges and is capable of performing all administrative functions within the Snowflake account

 When leveraging third-party data from the Snowflake Data Marketplace, the data is live, ready-to-query, and can be personalized. Additionally, the data is available without the need for copying or movingit to an individual Snowflake account, allowing for seamless integration with existing data

The list @my_stage command in Snowflake can be used to verify if data has been uploaded to an external stage named my_stage. This command provides a list of files thatare present in the specified stage2.

 In Snowflake, each size increase in virtual warehouses doubles the number of servers. Therefore, if a sizeSmall virtual warehouse is made up of two servers, a Large warehouse, which is two sizes larger, would be made up of eight servers (2 servers for Small, 4 for Medium, and 8 for Large)2.

Size specifies the amount of compute resources available per cluster in a warehouse. Snowflake supports the following warehouse sizes:

https://docs.snowflake.com/en/user-guide/warehouses-overview.html

 The minimum edition of Snowflake required to use a SCIM security integration is the Enterprise Edition. SCIM integrations are used for automated management of user identities and groups, and this feature is available starting from the Enterprise Edition of Snowflake. References: [COF-C02] SnowPro Core Certification Exam Study Guide

To temporarily bypass an active network policy by configuring the user object propertyMINS_TO_BYPASS_NETWORK_POLICY, the minimum set of privileges required is having the ACCOUNTADMIN role. This role has the necessary privileges to make such changes, including modifying user properties that affect network policies.

Snowflake Documentation: Network Policy Management

Snowflake recommends considering adding a clustering key to a table when its size reaches1 Terabyte (TB)or larger. Clustering keys help optimize the storage and query performance by organizing the data in a table based on the specified columns. This is particularly beneficial for large tables where data retrieval can become inefficient without proper clustering.

Why Clustering Keys Are Important:Clustering keys ensure that data stored in Snowflake is physically ordered in a way that aligns with the most frequent access patterns, thereby reducing the amount of scanned data during queries and improving performance.

Recommendation Basis:The recommendation for tables of size 1 TB or larger is based on the observation that smaller tables generally do not benefit as much from clustering, given Snowflake's architecture. However, as tables grow in size, the benefits of clustering become more pronounced.

Implementing Clustering Keys:

To set a clustering key for a table, you can use theCLUSTER BYclause during table creation or alter an existing table to add it:

CREATETABLEmy_table (... ) CLUSTERBY(column1, column2);

Or for an existing table:

ALTERTABLEmy_table CLUSTERBY(column1, column2);

Comprehensive and Detailed Explanation From Exact Extract:

Why A (Tags) is correct:In Snowflake, object tags are a data governance feature that allow administrators and data stewards to assign metadata key-value pairs to database objects such as tables, views, columns, and other objects. Tags enable the identification, classification, and tracking of objects that contain sensitive or critical information (e.g., PII, PHI, financial data).

According to Snowflake’s governance documentation, “Tags can be used to assign metadata to Snowflake objects for purposes such as data classification, cost tracking, and data protection. You can query tag references to identify all objects that have been assigned a particular tag.”

This means tags directly support identifying sensitive data by labeling and querying metadata rather than by applying security controls themselves.

Why B (External Tokens) is not correct:External tokens refer to data protection mechanisms for securing data at rest or during external function calls. They are not used to assign or query metadata for object identification or classification.

Why C (Row Access Policies) is not correct:Row access policies control access to rows within tables based on conditional logic tied to user attributes. They are used to enforce row-level security, not to identify or tag sensitive data.

Why D (Tag-Based Masking Policies) is not correct:Tag-based masking policies are an extension of the tagging feature, where a masking policy is automatically applied to columns that have a specific tag. While related to data protection, tag-based masking policies rely on tags to function and are not themselves used to identify or assign metadata. Tags must first exist for masking policies to reference them.

References (Snowflake SnowPro Core / Study Guide / Official Documentation):

Snowflake Documentation — Object Tagging: Defines and explains how tags can be assigned to tables, views, and columns to classify and identify sensitive data.

Snowflake Documentation — Governance Features Overview: Emphasizes the use of tags for data discovery and classification within data protection frameworks.

Snowflake Study Guide — Data Protection and Governance: Mentions that tags are used to manage and identify sensitive data via metadata and can be queried using TAG_REFERENCES and TAG_REFERENCES_ALL_COLUMNS views.

When querying VARIANT columns (such as JSON), the values are often returned as strings. To convert them to usable SQL types, users must apply the CAST() function (e.g., CAST(column:value AS NUMBER)).

In Snowflake,cloningis available for certain types of objects, allowing quick duplication without copying data:

Schemas: These can be cloned, enabling users to replicate entire schema structures, including tables and views, for development or testing.

Internal named stages: These stages, used to store data files within Snowflake, can also be cloned, preserving configurations for data loading.

Users and external objects (like external stages or tables) cannot be cloned due to their dependency on external data and configurations outside Snowflake.

Comprehensive and Detailed Explanation From Exact Extract:

Why B (Time Travel will be deactivated for the object) is correct:In Snowflake, the data retention period determines how long historical data (including deleted or modified data) is retained for Time Travel. When this period is set to 0 days, Time Travel is effectively disabled for that object (such as a table, schema, or database).

From the official Snowflake documentation:

“The DATA_RETENTION_TIME_IN_DAYS parameter specifies the number of days for which historical data is retained for Time Travel. A value of 0 disables Time Travel for the object.”

This means:

When DATA_RETENTION_TIME_IN_DAYS = 0, no historical versions of the object’s data are retained.

As a result, Time Travel queries (using AT or BEFORE clauses) and undrop/recovery operations are not available for that object.

Why A (Streams containing the object will become stale) is incorrect:Streams become stale when the data change tracking window exceeds the retention period of the source table’s Time Travel, but only if the retention period expires after some time. Setting retention to 0 disables Time Travel immediately, but doesn’t by itself make existing streams “stale.” Instead, new streams cannot be used effectively because there is no historical data to track.

Why C (Data replication will be disabled) is incorrect:Data replication is independent of the Time Travel retention period. A retention period of 0 does not affect replication; it only impacts the Time Travel feature.

Why D (Data sharing will be disabled) is incorrect:Data sharing is a separate Snowflake feature (using Secure Data Sharing). It is not impacted by Time Travel retention settings. Shared data can still be accessible regardless of the retention period.

References (Snowflake SnowPro Core / Study Guide / Official Documentation):

Snowflake Documentation — Time Travel:

“Setting DATA_RETENTION_TIME_IN_DAYS = 0 disables Time Travel for the object.”

Snowflake Documentation — Object Parameters:Describes DATA_RETENTION_TIME_IN_DAYS behavior at the database, schema, and table levels.

Snowflake Study Guide — Data Protection and Governance:Clarifies that setting retention to zero disables historical data storage and deactivates Time Travel functionality.

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.