COF-C02 SnowPro Core Certification Exam Question and Answers

The most efficient way to get files into Snowflake, especially when new files are being written to a cloud storage location at frequent intervals, is to use Snowpipe with auto-ingest. Snowpipe is Snowflake’s continuous data ingestion service that loads data as soon as it becomes available in a cloud storage location. By setting up cloud provider notifications, Snowpipe can be triggered automatically whenever new files are written to the storage location, ensuring that the data is loaded into Snowflake with minimal latency and without the need for manual intervention or scheduling frequent tasks.

References:

Snowflake Documentation on Snowpipe

SnowPro® Core Certification Study Guide

 To evaluate the details of dynamic data masking, the POLICY_REFERENCES and ACCESS_HISTORY views in the account_usage schema are used. The POLICY_REFERENCES view provides information about the objects to which a masking policy is applied, and the ACCESS_HISTORY view contains details about access to the masked data, which can be used to audit and verify the application of dynamic data masking policies.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Dynamic Data Masking1

Semi-structured

JSON, Parquet

Snowflake supports unloading data in several semi-structured file formats, including Parquet and JSON. These formats allow for efficient storage and querying of semi-structured data, which can be loaded directly into Snowflake tables without requiring a predefined schema12.

https://docs.snowflake.com/en/user-guide/data-unload-prepare.html#:~:text=Supported%20File%20Formats,-The%20following%20file &text=Delimited%20(CSV%2C%20TSV%2C%20etc.)

 The size of the virtual warehouse for a task can be configured to handle concurrency automatically using a Multi-cluster warehouse (MCW). This is because tasks are designed to run their body on a schedule, and MCW allows for scaling compute resources to match the task’s execution needs without manual intervention. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 Snowflake manages various compute resources and features, including Snowpipe and the ability to scale up a warehouse. Snowpipe is Snowflake’s continuous data ingestion service that allows users to load data as soon as it becomes available. Scaling up a warehouse refers to increasing the compute resources allocated to a virtual warehouse to handle larger workloads or improve performance.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Snowpipe and Virtual Warehouses1

Snowflake credits are the unit of measure for the compute resources used in Snowflake. The number of credits consumed depends on the size of the virtual warehouse and the time it is running. Larger warehouses consume more credits per hour than smaller ones, and credits are billed for the time the warehouse is active, regardless of the actual usage within that time.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

Provisioning time can vary based on the size of the warehouse. A 4X-Large Warehouse typically has more resources and may take longer to provision compared to a X-Small Warehouse, which has fewer resources and can generally be provisioned more quickly.References: Understanding and viewing Fail-safe | Snowflake Documentation

 In a CREATE PIPE ... AS COPY ... FROM (....) statement, the supported transformations include filtering data using an optional WHERE clause and omitting columns. The WHERE clause allows for the specification of conditions to filter the data that is being loaded, ensuring only relevant data is inserted into the table. Omitting columns enables the exclusion of certain columns from the data load, which can be useful when the incoming data contains more columns than are needed for the target table.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Simple Transformations During a Load1

https://docs.snowflake.com/en/user-guide/admin-security-privatelink.html

 One of the key features of Snowflake’s architecture is its unique approach to eliminating resource contention through the use of virtual warehouses. This is achieved by separating storage and compute resources, allowing multiple virtual warehouses to operate independently on the same data without affecting each other. This means that different workloads, such as loading data, running queries, or performing complex analytics, can be processed simultaneously without any performance degradation due to resource contention.

References:

Snowflake Documentation on Virtual Warehouses

SnowPro® Core Certification Study Guide

 Virtual Warehouses in Snowflake can indeed be resized while they are suspended. Resizing a warehouse involves changing the number of compute resources (servers) allocated to it, which can be done to adjust performance and cost. When a warehouse is suspended, it is not currently running any queries, but its definition and metadata remain intact, allowing for modifications like resizing.

The list command in Snowflake is used to validate and display the list of files in a specified stage. When a user has unloaded data to a stage, running the list @file__stage command will show all the files that have been uploaded to that stage, allowing the user to verify the data that was unloaded.

References:

Snowflake Documentation on Stages

SnowPro® Core Certification Study Guide

 Materialized Views in Snowflake are designed to store the result of a query and can be refreshed to maintain up-to-date data. However, they have certain limitations, one of which is that they cannot be defined using a JOIN clause. This means that a Materialized View can only be created based on a single source table and cannot combine data from multiple tables using JOIN operations.

References:

Snowflake Documentation on Materialized Views

SnowPro® Core Certification Study Guide

 The correct statement to accommodate the request for a marketing co-worker to change the size of their medium virtual warehouse called mktg__WH is to grant the MODIFY privilege on the warehouse to the ROLE MARKETING. This privilege allows the role to change the warehouse size among other properties.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Access Control Privileges1

 Snowflake applies egress charges in the case of database replication when data is transferred out of a Snowflake region to another region or cloud provider. This is because the data transfer incurs costs associated with moving data across different networks. Egress charges are not applied for data sharing within the same region, query result retrieval, or loading data into Snowflake, as these actions do not involve data transfer across regions.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Data Replication and Egress Charges1

 In all Snowflake editions, two key capabilities are universally available:

B. Automatic encryption of all data: Snowflake automatically encrypts all data stored in its platform, ensuring security and compliance with various regulations. This encryption is transparent to users and does not require any configuration or management.

D. Object-level access control: Snowflake provides granular access control mechanisms that allow administrators to define permissions at the object level, including databases, schemas, tables, and views. This ensures that only authorized users can access specific data objects.

These features are part of Snowflake’s commitment to security and governance, and they are included in every edition of the Snowflake Data Cloud.

References:

Snowflake Documentation on Security Features

SnowPro® Core Certification Exam Study Guide

Column-level security is a feature that allows fine-grained control over access to specific columns within a table. This is particularly useful for managing sensitive data and ensuring that only authorized users can view or manipulate certain pieces of information. According to my last update, this feature was available in the Enterprise Edition or higher editions of Snowflake.

References: Based on my internal data as of 2021, column-level security is an advanced feature typically reserved for higher-tiered editions like the Enterprise Edition in data warehousing solutions such as Snowflake.

https://docs.sn owflake.com/en/user-guide/intro-editions.html

 Materialized views in Snowflake are a feature that allows for the pre-computation and storage of query results for faster query performance. This feature is available starting from the Enterprise Edition of Snowflake. It is not available in the Standard Edition, and while it is also available in higher editions like Business Critical and Virtual Private Snowflake, the Enterprise Edition is the minimum requirement.

References:

Snowflake Documentation on CREATE MATERIALIZED VIEW1.

Snowflake Documentation on Working with Materialized Views

https://docs.snowflake.com /en/sql-reference/sql/create-materialized-view.html#:~:text=Materialized%20views%20require%20Enterprise%20Edition,upgrading%2C%20please%20contact%20Snowflake%20Support.

 External functions in Snowflake are a special type of User-Defined Function (UDF) that call code executed outside of Snowflake, typically through a remote service. Unlike traditional UDFs, external functions do not contain SQL code within Snowflake; instead, they interact with external services to process data2.

https://docs.snowflake.com/en/sql-reference/external-functions.html#:~:text=External %20functions%20are%20user%2Ddefined,code%20running%20outside%20of%20Snowflake.

 Even if a developer is granted ownership of a table with a masking policy, they will not be able to modify the table to read the masked data if their role does not have the necessary permissions. Ownership of a table does not automatically confer the ability to alter masking policies, which are designed to protect sensitive data. Masking policies are applied at the schema level and require specific privileges to modify12.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Masking Policies

The COPY command in Snowflake allows for the reordering of columns as they are loaded into a table, and it also permits the omission of columns from the source file during the load process. This provides flexibility in handling the schema of the data being ingested. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The auto-suspend and auto-resume settings in Snowflake apply to the entire virtual warehouse. These settings allow the warehouse to automatically suspend when it’s not in use, helping to save on compute costs. When queries or tasks are submitted to the warehouse, it can automatically resume operation. This functionality is designed to optimize resource usage and cost-efficiency.

References:

SnowPro Core Certification Exam Study Guide (as of 2021)

Snowflake documentation on virtual warehouses and their settings (as of 2021)

 Snowflake supports several methods for authenticating users, including federated authentication, key-pair authentication, and OAuth. Federated authentication allows users to authenticate using their organization’s identity provider. Key-pair authentication uses a public-private key pair for secure login, and OAuth is an open standard for access delegation commonly used for token-based authentication. References: Authentication policies | Snowflake Documentation, Authenticating to the server | Snowflake Documentation, External API authentication and secrets | Snowflake Documentation.

A VARIANT column in Snowflake can store semi-structured data types. This includes:

B. OBJECT: An object is a collection of key-value pairs in JSON, and a VARIANT column can store this type of data structure.

D. ARRAY: An array is an ordered list of zero or more values, which can be of any variant-supported data type, including objects or other arrays.

The VARIANT data type is specifically designed to handle semi-structured data like JSON, Avro, ORC, Parquet, or XML, allowing for the storage of nested and complex data structures.

References:

Snowflake Documentation on Semi-Structured Data Types

SnowPro® Core Certification Study Guide

Snowpipe data load history is retained for 64 days. This retention period allows users to review and audit the data load operations performed by Snowpipe over a significant period of time, which can be crucial for troubleshooting and ensuring data integrity.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Snowpipe1

Transient tables in Snowflake have a default Time Travel retention period of 1 day, which allows users to access historical data within the last 24 hours. However, transient tables do not have a Fail-safe period. Fail-safe is an additional layer of data protection that retains data beyond the Time Travel period for recovery purposes in case of extreme data loss. Since transient tables are designed for temporary or intermediate workloads with no requirement for long-term durability, they do not include a Fail-safe period by default1.

References:

Snowflake Documentation on Storage Costs for Time Travel and Fail-safe

Resource monitors in Snowflake can perform actions such as suspending a virtual warehouse when its credit usage reaches a defined limit and triggering a notification to account administrators when credit usage reaches a specified threshold. These actions help manage and control resource usage and costs within Snowflake.

References:

Snowflake Documentation: Resource Monitors

In Snowflake, shares are used to share data between Snowflake accounts. When creating a share, it is possible to grant access to the share to roles within the Snowflake account that is creating the share. The type of role that can be granted to a share is a Custom role. Custom roles are user-defined roles that account administrators can create to manage access control in a more granular way. Unlike predefined roles such as ACCOUNTADMIN or SYSADMIN, custom roles can be tailored with specific privileges to meet the security and access requirements of different groups within an organization.

Granting a custom role access to a share enables users associated with that role to access the shared data if the share is received by another Snowflake account. It is important to carefully manage the privileges granted to custom roles to ensure that data sharing aligns with organizational policies and data governance standards.

References:

Snowflake Documentation on Shares: Shares

Snowflake Documentation on Roles: Access Control

The PARTITION BY <expression> parameter option in the COPY INTO <location> command is used to split the output into multiple files based on the distinct values of the specified expression. This feature is particularly useful for organizing large datasets into smaller, more manageable files and can help with optimizing downstream processing or consumption of the data. For example, if you are unloading a large dataset of transactions and use PARTITION BY DATE(transactions.transaction_date), Snowflake generates a separate output file for each unique transaction date, facilitating easier data management and access.

This approach to data unloading can significantly improve efficiency when dealing with large volumes of data by enabling parallel processing and simplifying data retrieval based on specific criteria or dimensions.

References:

Snowflake Documentation on Unloading Data: COPY INTO

Snowflake's Data Exchange allows data providers to publish data that is accessible only to users they explicitly invite. This mechanism ensures that only approved users or Snowflake accounts have access, adding a layer of security and control. Invited users can query the shared data directly within their Snowflake account without requiring data copies, reducing redundancy and enhancing data accessibility within controlled boundaries. This selective sharing feature is part of Snowflake’s secure data-sharing capabilities.

Snowflake Streams are used to track and record Data Manipulation Language (DML) changes made to a table. Streams capture changes such as inserts, updates, and deletes, which can then be processed by other Snowflake objects or external applications.

Creating a Stream:

CREATE OR REPLACE STREAM my_stream ON TABLE my_table;

Using Streams: Streams provide a way to process changes incrementally, making it easier to build efficient data pipelines.

Consuming Stream Data: The captured changes can be consumed using SQL queries or Snowflake tasks.

References:

Snowflake Documentation: Using Streams

Snowflake Documentation: Change Data Capture (CDC) with Streams

When using the Snowflake Kafka connector, the table schema includes two important variant columns:

RECORD_TIMESTAMP: This column stores the timestamp from the Kafka record, enabling time-based analysis of incoming data.

RECORDKEY: This captures the unique key of each Kafka message, useful for uniquely identifying records or managing deduplication.

These columns ensure that each message’s metadata and key information are preserved, facilitating data analysis and real-time processing tasks in Snowflake.

Based on Snowflake recommendations, when creating a hierarchy of custom roles, the top-most custom role should ideally be granted to the ACCOUNTADMIN role. This recommendation stems from the best practices for implementing a least privilege access control model, ensuring that only the necessary permissions are granted at each level of the role hierarchy. The ACCOUNTADMIN role has the highest level of privileges in Snowflake, including the ability to manage all aspects of the Snowflake account. By assigning the top-most custom role to ACCOUNTADMIN, you ensure that the administration of role hierarchies and the assignment of roles remain under the control of users with the highest level of oversight and responsibility within the Snowflake environment.

References:

Snowflake Documentation on Access Control: Managing Access Control

In Snowflake, cloning is 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.

The ACCOUNTADMIN role 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. The ACCOUNTADMIN role encompasses broad administrative capabilities, ensuring that users assigned this role can perform critical account management tasks.References: Snowflake Documentation on Roles and Permissions

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

The Remote Disk I/O statistic in the Query Profile reflects time spent waiting on remote disk access, which can occur when data needs to be retrieved from external storage (remote). This metric is crucial for identifying bottlenecks related to I/O delays, often suggesting a need for performance optimization in data retrieval paths.

The other options relate to caching and network activity, but Remote Disk I/O specifically measures the wait time for data access from remote storage locations.

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

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

The Query Profile in Snowflake provides detailed insights into the execution of a query. It helps in troubleshooting performance issues by showing the steps of the query execution and the resources consumed. One of the key aspects it reveals is whether the virtual warehouse memory was sufficient for the query.

Access Query Profile: Navigate to the Query History page and select the query you want to analyze.

Examine Query Execution Steps: The Query Profile displays the different stages of the query execution, including the time taken and resources used at each step.

Identify Memory Issues: Look for indicators of memory issues, such as spilling to disk or memory errors, which suggest that the virtual warehouse memory might be too small.

References:

Snowflake Documentation: Query Profile

Snowflake Documentation: Optimizing Queries

Snowflake recommends removing data files from the staging area after they have been loaded into the target table. This practice ensures that the data is only loaded once and prevents accidental reloading of the same data. By removing the files, you eliminate the risk of duplicate data loads.

Stage the Data: Upload the data files to a Snowflake stage (internal or external).

Load the Data: Use the COPY INTO command to load the data from the stage into the Snowflake table.

Remove the Data Files: After successfully loading the data, remove the data files from the stage using the REMOVE command.

References:

Snowflake Documentation: Loading Data into Snowflake

Snowflake Documentation: Staging Data Files

Snowflake Documentation: COPY INTO Command

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

Snowflake's governance features vary across different editions, but the OBJECT_DEPENDENCIES view is supported by all Snowflake editions. This feature is part of Snowflake's Information Schema and is designed to help users understand the dependencies between various objects in their Snowflake environment.

The OBJECT_DEPENDENCIES view provides a way to query and analyze the relationships and dependencies among different database objects, such as tables, views, and stored procedures. This is crucial for governance, as it allows administrators and data engineers to assess the impact of changes, understand object relationships, and ensure proper management of data assets.

Object tags, masking policies, and row access policies are more advanced features that offer fine-grained data governance capabilities such as tagging objects for classification, dynamically masking sensitive data based on user roles, and controlling row-level access to data. These features may have varying levels of support across different Snowflake editions, with some features being exclusive to higher-tier editions.

In Snowflake, the DOUBLE data type is used for floating-point numbers and can represent a wide range of values without losing precision. This data type is ideal for storing numerical values that require decimal precision.

Define the Column: When creating a table, specify the column with the DOUBLE data type to store floating-point numbers.

CREATE TABLE example_table (

id INTEGER,

value DOUBLE

);

Insert Data: Insert floating-point numbers into the DOUBLE column.

INSERT INTO example_table (id, value) VALUES (1, 123.456);

References:

Snowflake Documentation: Data Types

Snowflake Documentation: Numeric Data Types

In Snowsight, Snowflake's web interface, user preferences can be customized to enhance the user experience. Among these preferences, users can set a default database and default schema. These settings streamline the user experience by automatically selecting the specified database and schema when the user initiates a new session or query, reducing the need to manually specify these parameters for each operation. This feature is particularly useful for users who frequently work within a specific database or schema context.References: Snowflake Documentation on Snowsight User Preferences

In Snowflake, to change the columns referenced in a view, the view must be recreated with the required changes. The ALTER VIEW command does not allow changing the definition of a view; it can only be used to rename a view, convert it to or from a secure view, or add, overwrite, or remove a comment for a view. Therefore, the correct approach is to drop the existing view and create a new one with the desired column references. 

 In Snowflake, schema objects include Pipes and File formats. Pipes are used for continuous data loading, and File formats specify the format of data files used in loading and unloading operations within Snowflake

 Snowflake recommends using the COPY command for bulk loading data batches from files already available in cloud storage. This command allows for efficient and large-scale data loading operations from files staged in cloud storage into Snowflake tables3.

 Multi-Factor Authentication (MFA) token caching is typically supported for clients that maintain a persistent connection or session with Snowflake, such as the ODBC driver and Python connector, to reduce the need for repeated MFA challenges. References: Based on general security practices in cloud services as of 2021.

The Cloud Services layer in Snowflake is responsible for various services, including security (like authentication and authorization) and metadata management (like query parsing and optimization). References: Based on general cloud architecture knowledge as of 2021.

 Stored procedures in Snowflake can perform a variety of database operations, including DDL and DML, whereas UDFs are designed to return values and cannot execute database operations1.

The bulk data load history available after a COPY statement is stored in the metadata of the pipe and is retained for 64 days1.

When unloading data to an external stage, the maximum file size supported is 5 GB. This limit ensures efficient data transfer and management within Snowflake’s architecture

 Snowflake supports several semi-structured data formats for loading data. The supported formats include JSON, Avro, and Parquet12. These formats allow for efficient storage and querying of data that does not conform to a traditional relational database schema.

User-Defined Function (UDF) handlers must be written inline for Javascript and SQL. These languages allow the UDF logic to be included directly within the SQL statement that creates the UDF2.

The details section of a Query Profile in Snowflake provides users with various statistics and information about the execution of a query. One of the key pieces of information that can be determined from this section is the total duration of the query, which helps in understanding the performance and identifying potential bottlenecks. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 Scaling up the virtual warehouse in Snowflake involves increasing the compute resources available for a single warehouse, which can improve the performance of large and complex queries by providing more CPU and memory resources. References: Based on general cloud data warehousing knowledge as of 2021.

To ensure a data consumer has access to the required objects, a data provider can enable the data sharing feature and validate that the consumer can access the views or tables shared with them. References: Based on general data sharing practices in cloud services as of 2021.

 Snowflake’s hierarchical key model includes several levels of keys, where Account master keys and File keys are part of this hierarchy. Account master keys are used to encrypt all the data within an account, while File keys are used to encrypt individual files within the database2.

 Snowflake automatically rotates the Account and Table master keys when they are more than 30 days old. Active keys are retired, and new keys are created, ensuring robust security through frequent key changes1

The privileges granted to Snowflake's system-defined roles cannot be revoked. System-defined roles, such as SYSADMIN, ACCOUNTADMIN, SECURITYADMIN, and others, come with a set of predefined privileges that are essential for the roles to function correctly within the Snowflake environment. These privileges are intrinsic to the roles and ensure that users assigned these roles can perform the necessary tasks and operations relevant to their responsibilities.

The design of Snowflake's role-based access control (RBAC) model ensures that system-defined roles have a specific set of non-revocable privileges to maintain the security, integrity, and operational efficiency of the Snowflake environment. This approach prevents accidental or intentional modification of privileges that could disrupt the essential functions or compromise the security of the Snowflake account.

References:

Snowflake Documentation on Access Control: Understanding Role-Based Access Control (RBAC)

In Snowflake, overloading refers to the creation of multiple User-Defined Functions (UDFs) with the same name but differing in the number or types of their arguments. This feature allows for more flexible function usage, as Snowflake can differentiate between functions based on the context of their invocation, such as the types or the number of arguments passed. Overloading helps to create more adaptable and readable code, as the same function name can be used for similar operations on different types of data.

References:

Snowflake Documentation: User-Defined Functions

The SnowSQL file that can store connection information is named ‘config’. It is used to store user credentials and connection details for easy access to Snowflake instances. References: Based on general database knowledge as of 2021.

The OPERATE privilege is required for a role to resume a suspended warehouse if auto-resume is not enabled. This privilege allows the role to start, stop, suspend, or resume a virtual warehouse3.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

 The OBJECT_CONSTRUCT function is used in Snowflake to create a JSON object from relational data, which is useful when unloading semi-structured data. The SINGLE = TRUE parameter is used when unloading data to ensure that the data is exported as a single file, which can be up to 5 GB in size. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 Snowpark is designed to bring the data programmability to Snowflake, enabling developers to write code in familiar languages like Scala, Java, and Python. It allows for the execution of these codes directly within Snowflake’s virtual warehouses, eliminating the need for a separate cluster. Additionally, Snowpark’s compatibility with Spark allows users to leverage their existing Spark code with minimal changes1.

The SQL command GET_DDL can be used to retrieve the CREATE definition of a masking policy in Snowflake. This command generates the DDL statement required to recreate the masking policy

The WAIT_FOR_COMPLETION = TRUE parameter in an ALTER WAREHOUSE command ensures that the command does not return until the warehouse has completed resizing. This means that the command will wait until all the necessary compute resources have been provisioned and the warehouse size has been changed. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The UNDROP command in Snowflake can be used to directly restore Views and Tables. These objects, when dropped, are moved to a ‘Recycle Bin’ where they can be restored within a time limit before they are permanently deleted. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 In the Snowflake Data Marketplace, the types of data listings available include ‘Vendor’, which refers to the providers of data, and ‘Personalized’, which indicates customized data offerings tailored to specific consumer needs45.

 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

Network policies in Snowflake can be set at the user level and at the account level2.

 The GET command is used to download files from a Snowflake stage to a local folder on a client’s machine2.

The Enterprise edition of Snowflake allows for a dedicated metadata store, providing additional features designed for large-scale enterprises

The default file size when unloading data from Snowflake using the COPY command is not explicitly stated in the provided resources. However, Snowflake documentation suggests that the file size can be specified using the MAX_FILE_SIZE option in the COPY INTO  command2.

To manage compliance with Personal Identifiable Information (PII) requirements, the Virtual Private Snowflake and Business Critical Editions of Snowflake should be used. These editions provide advanced security features necessary for handling sensitive data

Snowflake’s data caching features include the ability to clear the least-recently used data when the data cache is full to make room for new data. Additionally, the RESULT_SCAN table function can access and filter the contents of the query result cache, allowing users to retrieve and work with the results of previous queries. The other statements are incorrect: the data cache is not saved on the remote storage layer when a virtual warehouse is suspended, users can access queries from the query result cache that were run by other users, and there is no setting called USE_METADATA_CACHE in Snowflake. References: Caching in the Snowflake Cloud Data Platform, Optimizing the warehouse cache

The graphical representation of warehouse utilization indicates periods of significant queuing, suggesting that the current single cluster cannot efficiently handle all incoming queries. Configuring the warehouse to a multi-cluster warehouse will distribute the load among multiple clusters, reducing queuing times and improving overall performance1.

References = Snowflake Documentation on Multi-cluster Warehouses1

 The supported file formats for unloading data from Snowflake include JSON, Parquet, and CSV. These formats are commonly used for their flexibility and compatibility with various data processing tools

A well-clustered table in Snowflake means that the data is organized in such a way that related data points are stored close to each other within the micro-partitions. This optimizes query performance by reducing the amount of scanned data. The diagram indicated by option C shows a well-clustered table, as it likely represents a more evenly distributed range of values across the micro-partitions1.

References = Snowflake Micro-partitions & Table Clustering

 Snowflake uses a Role-Based Access Control (RBAC) model to manage access to data and resources. Additionally, Snowflake ensures that all data is encrypted, both at rest and in transit, to provide a high level of security for data stored within the platform. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The LATERAL FLATTEN command in Snowflake produces a result set that includes several columns, among which PATH and INDEX are includedPATH indicates the path to the element within a data structure that needs to be flattened, and INDEX represents the index of the element if it is an array2.

The Snowflake Search Optimization Service is designed to support improved performance for selective point lookup queries. These are queries that retrieve specific records from a database, often based on a unique identifier or a small set of criteria3.

The Snowflake Enterprise edition includes database replication and failover for business continuity and disaster recovery, as well as extended time travel capabilities for longer data retention periods1.

The error “Max LOB size (16777216) exceeded” indicates that the size of the parsed column exceeds the maximum size allowed for a single column value in Snowflake, which is 16 MB. To resolve this issue, the file should be split into multiple smaller files that are within the recommended size range of 100 MB to 250 MB. This will ensure that each JSON document within the files is smaller than the maximum LOB size allowed. Compressing the file, using a larger-sized warehouse, or setting STRIP_OUTER_ARRAY=TRUE will not resolve the issue of the column size exceeding the maximum allowed. References: COPY INTO Error during Structured Data Load: “Max LOB size (16777216) exceeded…”

The OWNERSHIP privilege is unique in that it must be granted by one role to another and cannot be revoked. This ensures that the transfer of ownership is deliberate and permanent, reflecting the importance of ownership in managing access and permissions.

The STRIP NULL_VALUES file format option, when set to TRUE, removes object or array elements that contain null values during the loading process of semi-structured data files into Snowflake. This ensures that the data loaded into Snowflake tables does not contain these null elements, which can be useful when the “null” values in files indicate missing values and have no other special meaning2.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

 Tags in Snowflake allow users to track sensitive data for compliance, discovery, protection, and resource usage. They enable the categorization and tracking of data, supporting compliance with privacy regulations678. References: [COF-C02] SnowPro Core Certification Exam Study Guide

To determine which user has accessed a database object containing sensitive information, a Snowflake administrator can query the ACCESS_HISTORY view in the ACCOUNT_USAGE schema, which provides information about access to database objects3.

The SECURITYADMIN role in Snowflake can manage any object grant globally, including modifying and revoking grants. This role has the necessary privileges to oversee and control access to all securable objects within the Snowflake environment4.

 Virtual warehouses in Snowflake can be started and stopped at any time, providing flexibility in managing compute resources. They can also be resized at any time, even while running, to accommodate varying workloads910. References: [COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake external tables are read-only, which means data manipulation language (DML) operations like INSERT, RENAME, or ALTER cannot be performed on them. However, external tables can be used for query and join operations3.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake’s Time Travel feature is used for the maintenance of historical data, allowing users to access and restore data that has been changed or deleted within a defined period4.

 In a regular schema within Snowflake, the object owner has the privilege to grant object privileges. The object owner is typically the role that created the object or to whom the ownership of the object has been transferred78.

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

Micro-partitioning is a Snowflake function that is maintained separately from the data and supports features such as Time Travel, Secure Data Sharing, and pruning. It allows Snowflake to efficiently manage and query large datasets by organizing them into micro-partitions1.

The ACCESS_HISTORY view in Snowflake is utilized to support compliance auditing. It provides detailed information on data access within Snowflake, including reads and writes by user queries. This view is essential for regulatory compliance auditing as it offers insights into the usage of tables and columns, and maintains a direct link between the user, the query, and the accessed data1.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

 When the DROP MANAGED ACCOUNT command is executed in Snowflake, it removes the managed account, including all objects created within the account, and access to the account is immediately restricted2.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

 An account administrator in the Data Exchange can perform tasks such as adding and removing members and approving or denying listing approval requests. These tasks are part of managing the Data Exchange and ensuring that only authorized listings and members are part of it12.

The parameter DATA_RETENTION_TIME_IN_DAYS can be set at the account level to define the minimum number of days Snowflake retains historical data for Time Travel1.

 The Access History feature in Snowflake allows administrators to track data access patterns and identify unused data. This information can be used to make decisions about archiving or deleting data to optimize storage and reduce costs. 

The purpose of a Query Profile is to provide a detailed analysis of a particular query’s execution plan, including the mechanics, behavior, and performance. It helps in identifying potential performance bottlenecks and areas for optimization

When federated authentication is activated in Snowflake, users authenticate via an external identity provider (IdP) rather than using Snowflake-managed credentials. Therefore, a user with a password defined by Snowflake will be unable to enter a password and must use their IdP credentials to log in.

QUSTION NO: 579

What value provides information about disk usage for operations where intermediate results do not fit in memory in a Query Profile?

A. IO

B. Network

C. Pruning

D. Spilling

Answer: D

In Snowflake, when a query execution requires more memory than what is available, Snowflake handles these situations by spilling the intermediate results to disk. This process is known as "spilling." The Query Profile in Snowflake includes a metric that helps users identify when and how much data spilling occurs during the execution of a query. This information is crucial for optimizing queries as excessive spilling can significantly slow down query performance. The value that provides this information about disk usage due to intermediate results not fitting in memory is appropriately labeled as "Spilling" in the Query Profile.

References:

Snowflake Documentation on Query Profile and Performance: This section explains the various components of the query profile, including the spilling metric, which indicates disk usage for operations where intermediate results exceed available memory.

 A directory table in Snowflake is an object layered on a stage that is used to store file-level metadata. It is not a separate database object but is conceptually similar to an external table because it stores metadata about the data files in the stage5.

The GOVERNANCE_VIEWER role in the ACCOUNT_USAGE schema provides visibility into policy-related information within Snowflake. This role is specifically designed to access views that display object metadata and usage metrics related to governance12.

The Snowflake command to unload the result of a query to a single file is COPY INTO with SINGLE = TRUE followed by a GET command to download the file. This command unloads the query result into a single file in the specified internal stage

A Stream object in Snowflake is used for change data capture (CDC), which records data manipulation language (DML) changes made to tables, including inserts, updates, and deletes3.

In Snowflake, to extract a specific value from a VARIANT column, you use the column name followed by a colon and then the key. The keys are case-sensitive. Therefore, to extract the employee’s name from the “Data” column, the correct syntax is data:employee.name.

Snowflake’s architecture allows for the independent scaling of compute resources, meaning you can increase or decrease the computational power as needed without affecting storage. This separation also means that storage can grow independently of compute usage, allowing for more flexible and cost-effective data management. 

The Query Profile statistics that indicate intermediate results do not fit in memory are the bytes spilled to local storage and bytes spilled to remote storage2.

The RETURN_ERRORS value in the VALIDATION_MODE option of the COPY command instructs Snowflake to validate the data files and return errors encountered across all specified files, including those that were partially loaded during an earlier load2. References: [COF-C02] SnowPro Core Certification Exam Study Guide

To start configuring Snowflake for Single Sign-On (SSO), the CREATE SECURITY INTEGRATION command is used. This command sets up a security integration object in Snowflake, which is necessary for enabling SSO with external identity providers using SAML 2.01.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

The MIN_DATA_RETENTION_TIME_IN_DAYS parameter is set at the account level. This parameter determines the minimum number of days Snowflake retains historical data for Time Travel operations

The Notify & Suspend action for a resource monitor in Snowflake sends a notification to all account administrators who have notifications enabled and suspends all assigned warehouses. However, the suspension only occurs after all currently running statements in the warehouses have been completed1. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 In Snowflake, if a temporary table and a permanent table have the same name within the same schema, the temporary table takes precedence over the permanent table within the session where the temporary table was created4.

SnowCD is a connectivity diagnostic tool that helps users troubleshoot network connections to Snowflake. It performs a series of checks to evaluate the network connection and provides suggestions for resolving any issues4.

The Snowflake data types that can be used to build nested hierarchical data are OBJECT and VARIANT. These data types support the storage and querying of semi-structured data, allowing for the creation of complex, nested data structures

The minimum Snowflake Edition that supports secure storage of Protected Health Information (PHI) data is the Business Critical Edition. This edition offers enhanced security features necessary for compliance with regulations such as HIPAA and HITRUST CSF4.

In owner’s rights stored procedures, certain commands are restricted to maintain security and integrity. The SHOW and DESCRIBE commands are limited because they can reveal metadata and structure information that may not be intended for all roles. 

 To validate data unloaded using the COPY INTO command, a Snowflake user can use the VALIDATION_MODE parameter within the SQL statement to test the files for errors without loading them3.

 Protegrity is listed as a data tokenization integration partner for Snowflake. This partnership allows Snowflake users to utilize Protegrity’s tokenization solutions within the Snowflake environment3.

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

 To ensure that additional multi-clusters are resumed with no delay, a virtual warehouse should be configured to a size larger than generally required. This configuration allows for immediate availability of additional resources when needed, without waiting for new clusters to start up

Serverless features in Snowflake are billed based on the time they are used, measured in minutes. The cost is calculated by multiplying the duration of the job by an automatic sizing determined by Snowflake, with a minimum billing increment of one minute. This means that even if a serverless feature is used for less than a minute, it will still be billed for the full minute.

 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 moving it to an individual Snowflake account, allowing for seamless integration with existing data

When publishing a Snowflake Data Marketplace listing into a remote region, it’s important to note that while the listing is replicated into all selected regions automatically, the data itself is not. Therefore, the data must be replicated separately. Additionally, the user must have the ORGADMIN role in at least one account to manage the replication of accounts1.

 When a pipe is recreated using the CREATE OR REPLACE PIPE command, the load history of the pipe is reset. This means that Snowpipe will consider all files in the stage as new and will attempt to load them, even if they were loaded previously by the old pipe2.

 Snowpipe retains load history for 14 days. This allows users to view and audit the data that has been loaded into Snowflake using Snowpipe within this time frame3.

 The COPY command is used in Snowflake to load data from files located in an external stage into a table. This command allows for efficient and parallelized data loading from various file formats1.

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

The minimum Snowflake edition required to use Dynamic Data Masking is the Enterprise edition. This feature is not available in the Standard edition2.

 Row level security in Snowflake is available starting with the Enterprise edition. This feature allows for the creation of row access policies that can control access to data at the row level within tables and views

The Search Optimization Service significantly improves the performance of selective point lookup queries on tables by creating and maintaining a persistent data structure called a search access path, which allows some micro-partitions to be skipped when scanning the table

 Transient tables in Snowflake have a minimum Fail-safe retention time period of 0 days. This means that once the Time Travel retention period ends, there is no additional Fail-safe period for transient tables

An explain plan is beneficial because it allows for the evaluation of how a query will be processed without the need to actually execute the query. This can help in understanding the query’s performance implications and potential bottlenecks without consuming resources that would be used if the query were run

 Scaling out in Snowflake involves adding clusters of the same size to a virtual warehouse, which allows for handling more concurrent queries without affecting the performance of individual queries. Scaling up refers to resizing a virtual warehouse to increase its compute resources, enabling it to handle more complex workloads and larger queries more efficiently.

 The credit consumption for maintaining a materialized view is impacted by how often the base table changes © and whether the materialized view has a cluster key defined (D). Changes to the base table can trigger a refresh of the materialized view, consuming credits. Additionally, having a cluster key defined can optimize the performance and credit usage during the materialized view’s maintenance. References: SnowPro Core Certification materialized view credit consumption

 In Snowflake’s data sharing model, any full Snowflake account can both provide and consume shared data. Additionally, the data consumer pays only for the compute resources used to query the shared data. No actual data is copied or transferred between accounts, and shared data does not take up any storage in a consumer account, so the consumer does not pay for data storage1.

References = Introduction to Secure Data Sharing | Snowflake Documentation

When cloning a database containing stored procedures and regular views with fully qualified table references, the cloned views and stored procedures will reference the cloned tables in the cloned database (A). This ensures that the cloned database is a self-contained copy of the original, with all references pointing to objects within the same cloned database. References: SnowPro Core Certification cloning database stored procedures views

The feature in Snowflake that allows a user to substitute a randomly generated identifier for sensitive data before loading it into Snowflake is known as External Tokenization. This process helps to secure sensitive data by ensuring that it is not exposed in its original form, thus preventing unauthorized access3.

 In an auto-scaling multi-cluster virtual warehouse with the SCALING_POLICY set to ECONOMY, another cluster is started when the system has enough load for 2 minutes (A). This policy is designed to optimize the balance between performance and cost, starting additional clusters only when the sustained load justifies it2.

When data is loaded into Snowflake, it undergoes a reorganization process where the data is stored in a columnar format and compressed. The columnar storage format enables efficient querying and data retrieval, as it allows for reading only the necessary columns for a query, thereby reducing IO operations. Additionally, Snowflake uses advanced compression techniques to minimize storage costs and improve performance. This combination of columnar storage and compression is key to Snowflake's data warehousing capabilities.

References:

Snowflake Documentation: Data Storage and Organization

The SQL command used to delete files from a Snowflake internal stage is REMOVE. This command can be used to remove files from either an internal or external stage within Snowflake

By default, the ACCOUNTADMIN role in Snowflake has the ability to create a share from a shared database. This role has the highest level of access within a Snowflake account, including the management of all aspects of the account, such as users, roles, warehouses, and databases, as well as the creation and management of shares for secure data sharing with other Snowflake accounts.

References:

Snowflake Documentation: Roles

Characteristics of Snowsight worksheets in Snowflake include:

A. Worksheets can be grouped under folders, and a folder of folders: This organizational feature allows users to efficiently manage and categorize their worksheets within Snowsight, Snowflake's web-based UI, enhancing the user experience by keeping related worksheets together.

E. Users can import worksheets and share them with other users: Snowsight supports the sharing of worksheets among users, fostering collaboration by allowing users to share queries, analyses, and findings. This feature is crucial for collaborative data exploration and analysis workflows.

References:

Snowflake Documentation: Snowsight (UI for Snowflake)

When unloading data, the Parquet file format is known for its efficiency in preserving the data values for floating-point number columns. Parquet is a columnar storage file format that offers high compression ratios and efficient data encoding schemes. It is especially effective for floating-point data, as it maintains high precision and supports efficient querying and analysis.

References:

Snowflake Documentation: Using the Parquet File Format for Unloading Data

A clustering key can be defined when a table is created by appending a CLUSTER Where each clustering key consists of one or more table columns/expressions, which can be of any data type, except GEOGRAPHY, VARIANT, OBJECT, or ARRAY https://docs.snowflake.com/en/user-guide/tables-clustering-keys

To verify the privileges that have been granted to a specific role in Snowflake, the correct SQL command is SHOW GRANTS TO ROLE <Role Name>. This command lists all the privileges granted to the specified role, including access to schemas, tables, and other database objects. This is a useful command for administrators and users with sufficient privileges to audit and manage role permissions within the Snowflake environment.

References:

Snowflake Documentation: SHOW GRANTS

The layer of Snowflake's architecture associated with virtual warehouses is the Query Processing layer. Virtual warehouses in Snowflake are dedicated compute clusters that execute SQL queries against the stored data. This layer is responsible for the entire query execution process, including parsing, optimization, and the actual computation. It operates independently of the storage layer, enabling Snowflake to scale compute and storage resources separately for efficiency and cost-effectiveness.

References:

Snowflake Documentation: Snowflake Architecture

When querying semi-structured data in Snowflake, the behavior towards case sensitivity is distinct between column names and the names of elements within the semi-structured data. Column names follow the general SQL norm of being case-insensitive, meaning you can reference them in any case without affecting the query. However, element names within JSON, XML, or other semi-structured data are case-sensitive. This distinction is crucial for accurate data retrieval and manipulation in Snowflake, especially when working with JSON objects where the case of keys can significantly alter the outcome of queries.

References:

Snowflake Documentation: Querying Semi-structured Data

When initiating a session in Snowflake, the system determines the current role based on the user's connection details and role assignments. If a user specifies a role during the connection, and that role is already granted to them, Snowflake sets it as the current role for the session. Alternatively, if no role is specified during the connection, but the user has a default role assigned, Snowflake will use this default role as the current session role. These mechanisms ensure that users operate within their permissions, enhancing security and governance within Snowflake environments.

References:

Snowflake Documentation: Understanding Roles

Characteristics of reader accounts in Snowflake include:

A. Reader account users cannot add new data to the account: Reader accounts are intended for data consumption only. Users of these accounts can query and analyze the data shared with them but cannot upload or add new data to the account.

E. Reader accounts enable data consumers to access and query data shared by the provider: One of the primary purposes of reader accounts is to allow data consumers to access and perform queries on the data shared by another Snowflake account, facilitating secure and controlled data sharing.

References:

Snowflake Documentation: Reader Accounts

The OBJECT_CONSTRUCT function in Snowflake is used to convert rows in a relational table into a single VARIANT column that represents each row as a JSON object. This function dynamically creates a JSON object from a list of key-value pairs, where each key is a column name and each value is the corresponding column value for a row. This is particularly useful for aggregating and transforming structured data into semi-structured JSON format for further processing or analysis.

References:

Snowflake Documentation: Semi-structured Data Functions

When creating a CSV file format in Snowflake and the requirement is to wrap columns by single quotes or double quotes, the FIELD_OPTIONALLY_ENCLOSED_BY parameter should be used in the file format specification. This parameter allows you to define a character (either a single quote or a double quote) that can optionally enclose each field in the CSV file, providing flexibility in handling fields that contain special characters or delimiters as part of their data.

References:

Snowflake Documentation: CSV File Format

Object Name Resolution: When unqualified object names (e.g., table name without schema) are used in a UDF, Snowflake follows a specific hierarchy to resolve them. Here's the order:

Current Schema: Snowflake first checks if an object with the given name exists in the schema currently in use for the session.

PUBLIC Schema: If the object isn't found in the current schema, Snowflake looks in the PUBLIC schema of the current database.

Note: The SEARCH_PATH parameter influences object resolution for queries, not within UDFs.

References:

Snowflake Documentation (Object Naming Resolution): https://docs.snowflake.com/en/sql-reference/name-resolution.html

To insert JSON formatted string data into a VARIANT field in Snowflake, the correct function to use is PARSE_JSON. The PARSE_JSON function is specifically designed to interpret a JSON formatted string and convert it into a VARIANT type, which is Snowflake's flexible format for handling semi-structured data like JSON, XML, and Avro. This function is essential for loading and querying JSON data within Snowflake, allowing users to store and manage JSON data efficiently while preserving its structure for querying purposes. This function's usage and capabilities are detailed in the Snowflake documentation, providing users with guidance on how to handle semi-structured data effectively within their Snowflake environments.

References:

Snowflake Documentation: PARSE_JSON

 Snowflake recommends creating separate warehouses for each workload to resolve performance issues caused by shared virtual warehouses. This ensures that the resources are not being overutilized by one user’s activities, thereby affecting the performance of another user’s activities4.

The search optimization service in Snowflake supports use cases involving conjunctions (AND) of multiple equality predicates. This service enhances the performance of queries that include multiple equality conditions by utilizing search indexes to quickly filter data without scanning entire tables or partitions. It's particularly beneficial for improving the response times of complex queries that rely on specific data matching across multiple conditions.

References:

Snowflake Documentation: Search Optimization Service

 Snowflake allows data providers to share data with consumers across different cloud platforms and regions through database replication. The data provider must replicate the database to a secondary account in the target region or cloud platform within the same organization, and then create a share to the data consumer’s account. This process ensures that the data is available in the consumer’s region and on their cloud platform, facilitating seamless data sharing. References: Sharing data securely across regions and cloud platforms | Snowflake Documentation

For continuous data loads using Snowpipe, the recommended compressed file size range is between 100-250 MB. This size range is suggested to optimize the number of parallel operations for a load and to avoid size limitations, ensuring efficient and cost-effective data loading

 Network policies in Snowflake can be applied to users and accounts. These policies control inbound access to the Snowflake service and internal stages, allowing or denying access based on the originating network identifiers12.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

 The stream type that can be used for tracking the records in external tables is ‘External’. This type of stream is specifically designed to track changes in external tables

In the Admin area of Snowsight, users can analyze query performance, manage Snowflake warehouses, set up and view details about resource monitors, manage users and roles, and administer Snowflake accounts in their organization2.

 Snowflake’s billing for virtual warehouses is per-second, with a minimum of 60 seconds for each time the warehouse is started or resumed. Therefore, if a warehouse runs for 61 seconds, it is billed for 61 seconds. If it is then shut down and restarted, running for an additional 30 seconds, it is billed for another 60 seconds (the minimum charge for a restart), totaling 121 seconds2

 Snowflake supports three types of internal stages: Named, User, and Table stages. These stages are used for staging data files to be loaded into Snowflake tables. Schema, Stream, and Database stages are not supported as internal stages in Snowflake. References: Snowflake Documentation1.

To find out which numeric values are present in both tables, the INTERSECT set operator should be used. This operator returns rows from one query’s result set which also appear in another query’s result set, effectively finding the common elements between the two tables45.

 An outer join, specifically a left outer join, will list all rows from the left table and match them with rows from the right table. If there is no match, the result will still include the row from the left table, with NULLs for columns from the right table. References: Based on general SQL knowledge as of 2021.

 In Snowflake, a namespace is comprised of a database and a schema. The combination of a database and schema uniquely identifies database objects within an account

In Snowflake, a tabular User-Defined Function (UDF) is defined with a return clause that includes the keyword “TABLE.” This indicates that the UDF will return a set of rows, which can be used in the FROM clause of a query. References: Based on my internal knowledge as of 2021.

 The command used to unload files from an internal or external stage to a local file system in Snowflake is the GET command. This command allows users to download data files that have been staged, making them available on the local file system for further use23.

 Snowsight automatically detects if a target account is in a different region and enables cross-cloud auto-fulfillment when using a paid listing on the Snowflake Marketplace. This feature allows Snowflake to manage the replication of data products to consumer regions as needed, without manual intervention1.

To access a JSON object in Snowflake, dot notation is used where the path to the object is specified after the column name containing the JSON data. Both lowercase and uppercase can be used for attribute names, so both “name” and “Name” are valid. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The Snowflake Cloud Services layer coordinates activities within the Snowflake account. It is responsible for tasks such as authentication, infrastructure management, metadata management, query parsing and optimization, and access control. References: Based on general cloud database architecture knowledge.

Snowflake implements Discretionary Access Control (DAC) by using a role-based access control model. In this model, access privileges are not directly assigned to individual objects or users but are encapsulated within roles. These roles are then assigned to users, effectively granting them the access privileges contained within the role. This approach allows for granular control over database access, making it easier to manage permissions in a scalable and flexible manner.References: Snowflake Documentation on Access Control