SnowPro-Core SnowPro Core Certification Exam Question and Answers

 Snowflake supports the storage of semi-structured data using the ARRAY and VARIANT data types. The ARRAY data type can directly contain VARIANT, and thus indirectly contain any other data type, including itself. The VARIANT data type can store a value of any other type, including OBJECT and ARRAY, and is often used to represent semi-structured data formats like JSON, Avro, ORC, Parquet, or XML34.

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

 In Snowflake, a user with the SECURITYADMIN role or higher can activate and enforce a network policy for all users in an account. Additionally, a role that has ownership of the network policy can also activate and enforce it

 Clustering is a technique used in Snowflake to improve the performance of queries. It involves organizing the data in a table into micro-partitions based on the values of one or more columns. This organization allows Snowflake to efficiently prune non-relevant micro-partitions during a query, which reduces the amount of data scanned and improves query performance.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Clustering

In Snowflake, Data Shares can be created and managed in two primary ways:

• Via the Snowflake Web Interface (UI): Users can create and manage shares through the graphical interface provided by Snowflake, which allows for a user-friendly experience.
• Via SQL commands: Snowflake also allows the creation and management of shares using SQL commands. This method is more suited for users who prefer scripting or need to automate the process.

When a Snowflake virtual warehouse is resized, the data cached in the warehouse is not lost. This is because the cache is maintained independently of the warehouse size. Resizing a warehouse, whether scaling up or down, does not affect the cached data, ensuring that query performance is not impacted by such changes.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Virtual Warehouse Performance1

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

Secure sharing in Snowflake allows users to share specific objects with other Snowflake accounts without physically copying the data, thus not consuming additional storage. Tables and Secure User Defined Functions (UDFs) are among the objects that can be shared using this feature. Materialized views, sequences, and procedures are not shareable objects in Snowflake.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Secure Data Sharing1

Assigning the custom role to the SYSADMIN role is considered a best practice because it allows the SYSADMIN role to manage objects created by the custom role. This is important for maintaining proper access control and ensuring that the SYSADMIN can perform necessary administrative tasks on objects created by users with the custom role.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Section 1.3 - SnowPro Core Certification Study Guide1

The correct command to view all roles granted to a specific user in Snowflake is SHOW GRANTS TO USER ;. This command lists all access control privileges that have been explicitly granted to the specified user. References: SHOW GRANTS | Snowflake Documentation

In Snowflake, Streams are the objects that track Data Manipulation Language (DML) changes made to tables, such as inserts, updates, and deletes. Streams record these changes along with metadata about each change, enabling actions to be taken using the changed data. This process is known as change data capture (CDC)2.

 Snowflake Time Travel is a powerful feature that allows users to access historical data within a defined period. It enables two key capabilities:

• B. Restoring data-related objects that have been deleted within the past 90 days: Time Travel can be used to restore tables, schemas, and databases that have been accidentally or intentionally deleted within the Time Travel retention period.
• C. Conducting point-in-time analysis for BI reporting: It allows users to query historical data as it appeared at a specific point in time within the Time Travel retention period, which is crucial for business intelligence and reporting purposes.

While Time Travel does allow querying of past data, it is limited to the retention period set for the Snowflake account, which is typically 1 day for standard accounts and can be extended up to 90 days for enterprise accounts. It does not enable querying or restoring objects created or deleted beyond the retention period, nor does it provide analysis over all periods of time.

References:

• Snowflake Documentation on Time Travel
• 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 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.

In Snowflake, Cloud Services costs are incurred when the Cloud Services usage exceeds 10% of the compute usage (measured in credits). Therefore, scenarios A and E would result in Cloud Services charges because the Cloud Services usage is more than 10% of the compute credits used.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake’s official documentation on billing and usage1

 The COPY INTO command is used in Snowflake to unload data from a table into a file in a stage. This command allows for the export of data from Snowflake tables into flat files, which can then be used for further analysis, processing, or storage in external systems.

References:

• Snowflake Documentation on Unloading Data
• Snowflake SnowPro Core: Copy Into Command to Unload Rows to Files in Named Stage

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

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.

The FLATTEN function can be used with the recursive argument to return a list of distinct key names in all nested elements within an object. This function is particularly useful for working with semi-structured data in Snowflake

UTF-8 is the only supported character set for loading and unloading data from all supported file formats in Snowflake. UTF-8 is a widely used encoding that supports a large range of characters from various languages, making it suitable for internationalization and ensuring data compatibility across different systems and platforms.

References:

• Snowflake Documentation: Data Loading and Unloading

The query acceleration service in Snowflake is designed to benefit queries that involve large scans and selective filters. This service can offload portions of the query processing work to shared compute resources, which can handle these types of workloads more efficiently by performing more work in parallel and reducing the wall-clock time spent in scanning and filtering2. References: [COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake bills virtual warehouse usage in one-minute increments, rounding up to the nearest minute for any partial minute of compute time used. If a virtual warehouse runs for 61 seconds and then, after being shut down, restarts and runs for an additional 30 seconds, the total time billed would be 120 seconds or 2 minutes. The first 61 seconds are rounded up to 2 minutes, and the subsequent 30 seconds are within a new minute, which is also rounded up to the nearest minute.

References:

• Snowflake Documentation: Virtual Warehouses Billing

The STRIP_NULL_VALUE function in Snowflake is used to convert a JSON null value into a SQL NULL value1.

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

The Virtual Private Snowflake (VPS) edition offers the highest level of security for organizations with the strictest security requirements. This edition provides a dedicated and isolated instance of Snowflake, including enhanced security features and compliance certifications to meet the needs of highly regulated industries or any organization requiring the utmost in data protection and privacy.

References:

• Snowflake Documentation: Snowflake Editions

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 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

To temporarily bypass a network policy by configuring the user object property MINS_TO_BYPASS_NETWORK_POLICY, the USERADMIN role should be used. This role has the necessary privileges to modify user properties, including setting a temporary bypass for network policies, which can be crucial for enabling access under specific circumstances without permanently altering the network security configuration.

References:

• Snowflake Documentation: User Management

A cached MFA token is valid for up to four hours. https://docs.snowflake.com/en/user-guide/security-mfa#using-mfa-token-caching-to-minimize-the-number-of-prompts-during-authentication-optional

In the Snowflake Web Interface (UI), the Query History page displays the history of queries executed in Snowflake for up to 14 days. This allows users to review and analyze their query performance, troubleshoot issues, and understand their query patterns over a two-week period. The Query History page is a critical tool for monitoring and optimizing the use of Snowflake.

References:

• Snowflake Documentation: Using the Web Interface

The command to list all the file formats for which a user has access privileges in Snowflake is SHOW FILE FORMATS. This command provides a list of all file formats defined in the user's current session or specified database/schema, along with details such as the name, type, and creation time of each file format. It is a valuable tool for users to understand and manage the file formats available for data loading and unloading operations.

References:

• Snowflake Documentation: SHOW FILE FORMATS

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.

To easily transfer data from a production account to a non-production account in Snowflake within the same cloud provider region, creating a data share is the most efficient approach. Data sharing allows for live, read-only access to selected data objects from the production account to the non-production account without the need to duplicate or move the actual data. This method facilitates seamless access to the data for development, testing, or analytics purposes in the non-production environment.

References:

• Snowflake Documentation: Data Sharing

The TABLE_STORAGE_METRICS metadata table stores the storage utilization information, including for tables that have been dropped but are still incurring storage costs2.

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

The Query Profile in Snowflake provides a graphical representation and statistics for each component of the query's execution plan. This includes details such as the execution time, the number of rows processed, and the amount of data scanned for each operation within the query. The Query Profile is a crucial tool for understanding and optimizing the performance of queries, as it helps identify potential bottlenecks and inefficiencies.

References:

• Snowflake Documentation: Understanding the Query Profile

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

To unload data from a table into one or more files in a named stage, the COPY INTO <location> command should be used. This command exports the result of a query, such as selecting all rows from a table, into files stored in the specified stage. The COPY INTO command is versatile, supporting various file formats and compression options for efficient data unloading.

References:

• Snowflake Documentation: COPY INTO Location

The Fail-safe retention period for transient and temporary tables in Snowflake is 0 days. Fail-safe is a feature designed to protect data against accidental loss or deletion by retaining historical data for a period after its Time Travel retention period expires. However, transient and temporary tables, which are designed for temporary or short-term storage and operations, do not have a Fail-safe period. Once the data is deleted or the table is dropped, it cannot be recovered.

References:

• Snowflake Documentation: Understanding Fail-safe

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

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.

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

 A masking policy in Snowflake consists of a single data type, with one or more conditions, and one or more masking functions. These components define how the data is masked based on the specified conditions3.

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. 

In Snowflake, when setting up a network policy that specifies both allowed and blocked IP address lists, if an IP address appears in both lists, access from that IP address will be denied. The reason is that Snowflake prioritizes security, and the presence of an IP address in the blocked list indicates it should not be allowed regardless of its presence in the allowed list. This ensures that access controls remain stringent and that any potentially unsafe IP addresses are not inadvertently permitted access.

References:

• Snowflake Documentation: Network Policies

When unloading floating-point number columns to CSV or JSON files, Snowflake truncates the values to approximately 15 significant digits with 9 digits following the decimal point, which can be represented as (15,9). This ensures a balance between accuracy and efficiency in representing floating-point numbers in text-based formats, which is essential for data interchange and processing applications that consume these files.

References:

• Snowflake Documentation: Data Unloading Considerations

The Search Optimization Service in Snowflake is designed to improve the performance of specific types of queries, particularly those involving extensive sets of WHERE conditions. By maintaining a search index on tables, this service can accelerate lookup and analytical queries, making it a valuable feature for optimizing query performance and reducing execution times for complex searches.

References:

• Snowflake Documentation: Search Optimization Service

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.

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

Pre-signed URLs in Snowflake allow users to access unstructured data without the need for authentication into Snowflake or passing an authorization token. These URLs are open and can be directly accessed or downloaded by any user or application, making them ideal for business intelligence applications or reporting tools that need to display unstructured file contents

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

Query parsing and compilation in Snowflake occur within the cloud services layer. This layer is responsible for various management tasks, including query compilation and optimization

The Snowflake edition that enables data sharing only through Snowflake Support is the Virtual Private Snowflake (VPS). By default, VPS does not permit data sharing outside of the VPS environment, but it can be enabled through Snowflake Support4.

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’s documentation recommends producing the largest files possible for data loading, as larger files reduce the number of files to process and the overhead associated with handling many small files. This approach can maximize the load performance by leveraging Snowflake’s ability to ingest large files efficiently1. References: [COF-C02] SnowPro Core Certification Exam Study Guide

During a query on a virtual warehouse, the columns in the result set of the query are cached. This allows for faster retrieval of data if the same or a similar query is run again, as the system can retrieve the data from the cache rather than reprocessing the entire query. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 For the ALLOWED VALUES tag property, the maximum number of possible string values for a single tag is 256. This allows for a wide range of values to be assigned to a tag when it is set on an object

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 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

 SnowCD (Snowflake Connectivity Diagnostic Tool) is the best tool provided by Snowflake for troubleshooting network connectivity issues. It helps diagnose and resolve issues related to connecting to Snowflake services

https://docs.snowflake.com/en/user-guide/snowcd.html#:~:text=SnowCD%20(i.e.%20Snowflake%20Connectivity%20Diagnostic,their%20network%20connection%20to%20Snowflake .

The Snowflake Query Profile provides a graphic representation of the main components of the query processing. This visual aid helps users understand the execution details and performance characteristics of their queries4.

The cloud services layer of Snowflake architecture handles various aspects including security functions, authentication of user sessions, and access control, ensuring that only authorized users can access the data and services23.

 To use Partner Connect, the ACCOUNTADMIN role is required. Partner Connect allows account administrators to easily create trial accounts with selected Snowflake business partners and integrate these accounts with Snowflake

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

A materialized view is beneficial when the query consumes many compute resources every time it runs (B), and when the results of the query do not change often and are used frequently (E). This is because materialized views store pre-computed data, which can speed up query performance for workloads that are run frequently or are complex

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

Snowflake retains information in the ACCESS HISTORY view for 365 days. This allows users to query the access history of Snowflake objects within the last year1.

Snowflake supports reader accounts, which are a type of account that allows data providers to share data with consumers who are not Snowflake customers. However, for data sharing to occur, the consumer needs to become a licensed Snowflake customer because data sharing is only supported between Snowflake accounts. References: Introduction to Secure Data Sharing | Snowflake Documentation2.

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. 

Snowflake’s search optimization service supports tables and views that are not protected by row access policies. It is designed to improve the performance of certain types of queries on tables, including selective point lookup queries and queries on fields in VARIANT, OBJECT, and ARRAY (semi-structured) columns1.

The search optimization service and automatic clustering are features that can improve the performance of queries returning a small subset of rows from a large table. The search optimization service is designed for low-latency point lookup queries, while automatic clustering organizes data in micro-partitions based on specific dimensions to reduce the amount of data scanned during queries.

Credit charges for Snowflake virtual warehouses are calculated based on the size of the virtual warehouse and the length of time the warehouse is running. The size determines the compute resources available, and charges are incurred for the time these resources are utilized

The Snowflake object that helps evaluate virtual warehouse performance impacted by query queuing is the Information_schema.warehouse_load_history. This view provides historical data about the load on a warehouse, including the average number of queries that were running or queued within a specific interval, which can be used to assess performance and identify potential issues with query queuing3.

To provide a user with the necessary access to create a materialized view in a schema, the user must be granted a role that has the CREATE MATERIALIZED VIEW privilege on that schema. First, the role is granted to the user, and then the privilege is granted to the role

 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

Snowflake supports storing unstructured data and provides native support for semi-structured file formats such as JSON, Avro, Parquet, ORC, and XML1. GeoJSON, being a type of JSON, and XML are among the formats that can be stored in Snowflake. References: [COF-C02] SnowPro Core Certification Exam Study Guide

To change the existing file format type from CSV to JSON, the recommended way is to use the ALTER FILE FORMAT command with the SET TYPE=JSON clause. This alters the file format specification to use JSON instead of CSV. References: Based on my internal knowledge as of 2021.

Snowflake’s architecture is divided into three layers: database storage, query processing, and cloud services. The metadata, which includes information about the structure of the data, the SQL operations performed, and the service-level policies, is stored in the cloud services layer. This layer acts as the brain of the Snowflake environment, managing metadata, query optimization, and transaction coordination.

Snowflake uses a technique called pruning to limit the number of micro-partitions scanned by each query. Pruning effectively filters out unnecessary micro-partitions based on the query’s filter conditions, which can significantly improve query performance by reducing the amount of data scanned1.

Snowflake supports the VARIANT and ARRAY data types for semi-structured data. VARIANT can store values of any other type, including OBJECT and ARRAY, making it suitable for semi-structured data formats like JSON. ARRAY is used to store an ordered list of elements

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 Snowflake cloud services layer is responsible for tasks such as authentication and access control, ensuring secure access to the platform, and metadata management, which involves organizing and maintaining information about the data stored in Snowflake56.

Best practices for using the ACCOUNTADMIN role include ensuring that all users with this role use Multi-factor Authentication (MFA) for added security. Additionally, it is recommended to assign the ACCOUNTADMIN role to at least two users to avoid delays in case of password recovery issues, but to as few users as possible to maintain strict control over account-level operations4.

The ACCESS_HISTORY view in the SNOWFLAKE.ACCOUNT_USAGE schema contains information about the access history of Snowflake objects, such as tables and views, within the last 365 days1.

 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

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

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 that are present in the specified stage2.

 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

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

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

Snowflake supports the use of external stages with Amazon Web Services (AWS), Microsoft Azure Cloud, and Google Cloud Platform (GCP). These platforms allow users to stage data externally and integrate with Snowflake for data loading operations

A multi-cluster warehouse should be used in auto-scaling mode when there is a need to handle a large number of concurrent queries. Auto-scaling allows Snowflake to automatically add or remove compute clusters to balance the load, ensuring that performance remains consistent during varying levels of demand

The maximum Time Travel retention period for a temporary Snowflake table is 1 day. This is the standard retention period for temporary tables, which allows for accessing historical data within a 24-hour window

The Cloud Services layer in Snowflake is responsible for managing the result cache. When a query is executed, the results are stored in this cache, and subsequent identical queries can leverage these cached results without re-executing the entire query1.

Data masking policies in Snowflake allow for the obfuscation of specific data within a field, enabling some users to see the full data while limiting others. This feature is particularly useful for handling PII, ensuring that sensitive information is only visible to authorized users1.

 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 SQL command unload multiple files

 For a temporary table, the maximum total Continuous Data Protection (CDP) charges incurred are for the duration of the session in which the table was created, which does not exceed 24 hours2.

References = [COF-C02] SnowPro Core Certification Exam Study Guide, Snowflake Documentation2

When a running virtual warehouse in Snowflake is suspended and then restarted, the minimum amount of time it will incur charges for is 60 seconds2.

 The Snowflake Cloud Services layer is a collection of services that coordinate activities across Snowflake, tying together all the different components to process user requests, from login to query dispatch1.

References = [COF-C02] SnowPro Core Certification Exam Study Guide, Snowflake Documentation1

The parse_json() function in Snowflake interprets an input string as a JSON document and produces a VARIANT value containing the JSON document. This function is specifically designed for parsing strings that contain valid JSON information3.

 By default, the Snowflake role required to create a share is ACCOUNTADMIN (D). This role has the necessary privileges to perform administrative tasks, including creating shares for data sharing purposes

Shares in Snowflake are named objects that encapsulate all the information required to share databases, schemas, tables, secure views, and secure UDFs. These objects can be added to a share by granting privileges on them to the share via a database role

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.

 In Snowflake’s architecture, the Cloud Services layer is responsible for generating the query execution plan. This layer handles all the coordination, optimization, and management tasks, including query parsing, optimization, and compilation into an execution plan that can be processed by the Compute layer.

 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

 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.

 The billable compute time includes the time the warehouse is running queries plus the auto-suspend time after the last query if the warehouse is not manually suspended. In this scenario, the warehouse runs for 3 minutes, suspends after 10 minutes of inactivity, resumes for a 10-second query, and then is manually suspended. The total billable time is the sum of the initial 3 minutes, the 10 minutes of auto-suspend time, and the brief period for the 10-second query, which is rounded up to the next full minute due to Snowflake’s billing practices. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The command USE WAREHOUSE <>; is used to set the virtual warehouse for the current session in Snowflake. This command specifies which virtual warehouse to use for executing queries in that session1.

 In the Snowflake Enterprise Edition, the default Time Travel retention is set to 1 day, the maximum Time Travel retention can be set up to 90 days, and the Fail-safe retention time is 7 days3.

 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

Access History in Snowflake is a feature designed to support auditing by tracking access to data within Snowflake, including when a user's query reads column data. It provides detailed information on queries executed, including the user who ran the query, the query text, and the objects (e.g., tables, views) accessed by the query. This feature is instrumental for auditing purposes, helping organizations to monitor and audit data access for security and compliance.

In Snowflake, network policies define the allowed IP address ranges from which users can connect to Snowflake, enhancing security by restricting access based on network location. The creation and management of network policies require sufficient privileges. Specifically, a user with the SECURITYADMIN role or any role with higher privileges, such as ACCOUNTADMIN, can create network policies. Additionally, a custom role can be granted the CREATE NETWORK POLICY privilege, enabling users assigned to that role to also create network policies. This approach allows for flexible and secure management of network access to Snowflake.References: Snowflake Documentation on Network Policies

When a Snowflake user wants to share a newly created object in a database with consumers, the correct action to take is to use the GRANT privilege ... TO SHARE command to grant the necessary privileges for the object to be shared. This approach allows the object owner or a user with the appropriate privileges to share database objects such as tables, secure views, and streams with other Snowflake accounts by granting access to a named share.

The GRANT statement specifies which privileges are granted on the object to the share. The object remains in its original location; sharing does not duplicate or move the object. Instead, it allows the specified share to access the object according to the granted privileges.

For example, to share a table, the command would be:

GRANT SELECT ON TABLE new_table TO SHARE consumer_share;

This command grants the SELECT privilege on a table named new_table to a share named consumer_share, enabling the consumers of the share to query the table.

Automatic sharing, dropping and re-adding the object, or recreating the object with a different name are not required or recommended practices for sharing objects in Snowflake. The use of the GRANT statement to a share is the direct and intended method for this purpose.

The command used to export or unload data from Snowflake to a stage (such as a file in an S3 bucket, Azure Blob Storage, or Google Cloud Storage) is the PUT command. The PUT command is designed to upload data files from a local file system (in the case of SnowSQL or other client) or a virtual warehouse to a specified stage. This functionality is critical for scenarios where data needs to be extracted from Snowflake for use in external systems, backups, or further processing.

The syntax for the PUT command follows the structure: PUT file://<local_file_path> @<stage_name>, where <local_file_path> specifies the path to the file(s) on the local file system that you wish to upload, and <stage_name> specifies the destination stage in Snowflake.

It's important to distinguish that the PUT command is used for exporting data out of Snowflake, whereas the COPY INTO <table> command is used for importing data into Snowflake from a stage. The GET command, on the other hand, is used to download files from a stage to the local file system, essentially the inverse operation of the PUT command.

References:

• Snowflake Documentation on Loading and Unloading Data: [Loading and Unloading Data](https://docs.snowflake.com/en/user-guide/data-load

Snowflake recommends considering adding a clustering key to a table when its size reaches 1 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:

CREATE TABLE my_table (... ) CLUSTER BY (column1, column2);

• uk.co.certification.simulator.questionpool.PList@187201e0

ALTER TABLE my_table CLUSTER BY (column1, column2);

A materialized view in Snowflake gets suspended when structural changes that could impact the view's integrity are made to the base table, such as B. When a column is dropped from the base table. Dropping a column from the base table on which a materialized view is defined can invalidate the view's data, as the view might rely on the column that is being removed. To maintain data consistency and prevent the materialized view from serving stale or incorrect data, Snowflake automatically suspends the materialized view.

Upon suspension, the materialized view does not reflect changes to the base table until it is refreshed or re-created. This ensures that only accurate and current data is presented to users querying the materialized view.

References:

• Snowflake Documentation on Materialized Views: Materialized Views

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, configuring a virtual warehouse to be in a "Maximized" mode implies maximizing the resources allocated to the warehouse for its duration. This is done to ensure that the warehouse has a consistent amount of compute resources available, enhancing performance for workloads that require a high level of parallel processing or for handling high query volumes.

To configure a virtual warehouse in maximized mode, you should set the same value for both MIN_CLUSTER_COUNT and MAX_CLUSTER_COUNT. This configuration ensures that the warehouse operates with a fixed number of clusters, thereby providing a stable and maximized level of compute resources.

Reference to Snowflake documentation on warehouse sizing and scaling:

• Warehouse Sizing and Scaling
• Understanding Warehouses

In Snowflake, a Temporary table is a type of table that is only visible to the user who creates it, can have the same name as permanent tables in the same schema, and is automatically dropped at the end of the session in which it was created. Temporary tables are designed for transient data processing needs, where data is needed for the duration of a specific task or session but not beyond. Since they are automatically cleaned up at the end of the session, they help manage storage usage efficiently and ensure that sensitive data is not inadvertently persisted.

References:

• Snowflake Documentation on Temporary Tables: Temporary Tables

In Snowflake, when dealing with semi-structured data such as a JSON document stored in a VARIANT column, the proper syntax to extract a value is to use the column name followed by the path to the specific element. Since arrays in JSON are zero-indexed, the first element is referenced with [0]. Therefore, to extract the name from the first element of the elements array, the correct syntax is Source_column:elements[0].name.References: Snowflake Documentation on Semi-Structured Data

In the execution time summary of a Query Profile in Snowflake, the categories included provide insights into various aspects of query execution. "Pruning" refers to the process by which Snowflake reduces the amount of data scanned by eliminating partitions of data that are not relevant to the query, thus improving performance. "Initialization" represents the time taken for query planning and setup before actual execution begins. These metrics are crucial for understanding and optimizing query performance.

The correct SQL statement to list the version of the JDBC (Java Database Connectivity) drivers currently being used is to execute SELECT CURRENT_JDBC_VERSION(); from within SnowSQL or any client application that utilizes JDBC for connecting to Snowflake. Snowflake provides specific functions to query the version of the client drivers or connectors being used, such as JDBC, ODBC, and others. The CURRENT_JDBC_VERSION() function is designed specifically to return the version of the JDBC driver in use.

It's important to note that Snowflake supports various types of drivers and connectors for connecting to different client applications, including ODBC, JDBC, Python, and others. Each of these connectors has its own method or function for querying the current version in use. For JDBC, the appropriate function is CURRENT_JDBC_VERSION(), reflecting the specificity required to obtain version information relevant to the JDBC driver specifically.

References:

• Snowflake Documentation on Client Functions: This information can typically be found in the Snowflake documentation under the section that covers SQL functions, particularly those functions that provide information about the client or session.

A pre-signed URL provides a way to access objects stored in a stage without requiring authentication to Snowflake. This URL contains all the necessary information for access control, including an expiration time, and it’s signed by the service's credentials. This mechanism allows users or applications to access specific files stored in a cloud storage location (stage) directly, bypassing the need to authenticate into Snowflake, ensuring secure and temporary access to the data.References: Snowflake Documentation on Stages and Data Loading

In Snowflake, a Share is the object that contains all the information required to share a database with other Snowflake accounts. Shares are used to securely share data stored in Snowflake tables and views, enabling data providers to grant data consumers access to their datasets without duplicating data. When a database is shared, it can include one or more schemas, and each schema can contain tables, views, or both.

References:

• Snowflake Documentation on Shares: Shares

The privilege required on a virtual warehouse to abort any existing executing queries is OPERATE. The OPERATE privilege on a virtual warehouse allows a user to perform operational tasks on the warehouse, including starting, stopping, and restarting the warehouse, as well as aborting running queries. This level of control is essential for managing resource utilization and ensuring that the virtual warehouse operates efficiently.

References:

• Snowflake Documentation on Access Control: Access Control Privileges

Details around the privileges that have been granted for all objects in an account. The ACCESS_HISTORY view in Snowflake provides a comprehensive log of access control changes, including grants and revocations of privileges on all securable objects within the account. This information is crucial for auditing and monitoring the security posture of your Snowflake environment.

Here's how to understand and use the ACCESS_HISTORY view:

• Purpose of ACCESS_HISTORY View: It is designed to track changes in access controls, such as when a user or role is granted or revoked privileges on various Snowflake objects. This includes tables, schemas, databases, and more.
• Querying ACCESS_HISTORY: To access this view, you can use the following SQL query pattern:

SELECT * FROM SNOWFLAKE.ACCOUNT_USAGE.ACCESS_HISTORY WHERE EVENT_TYPE = 'GRANT' OR EVENT_TYPE = 'REVOKE';

• Interpreting the Results: The results from the ACCESS_HISTORY view include the object type, the specific privilege granted or revoked, the grantee (who received or lost the privilege), and the timestamp of the event. This data is invaluable for audits and compliance checks.

In Snowflake, the system-defined, read-only view that displays information on column lineage, which specifies how data flows from source to target in a SQL write operation, is ACCESS_HISTORY. This view is instrumental in auditing and analyzing data access patterns, as it provides detailed insights into how and from where the data is being accessed and manipulated within Snowflake.

Reference to Snowflake documentation on ACCESS_HISTORY:

• Using Access History to Audit Data Access

The settings shown in the image indicate that the data retention time in days is configured at two different levels: the account level and the object level. At the account level, the MIN_DATA_RETENTION_TIME_IN_DAYS is set to 5 days, and at the object level, the DATA_RETENTION_TIME_IN_DAYS is set to 2 days. Since the object level setting has a lower value, it takes precedence over the account level setting for the specific object. Therefore, the data will be retained for 2 days at the object level.References: Snowflake Documentation on Data Retention Policies

The correct function to find the query ID of the second query executed in the current session is SELECT LAST_QUERY_ID(-2). The LAST_QUERY_ID function returns the query ID for the most recent query executed in the session when called with no arguments. When used with an argument, it can retrieve the ID of previous queries within the same session, where -2 would reference the second most recent query executed.

References:

• There's a clarification needed here; Snowflake's documentation indicates LAST_QUERY_ID() function does not accept arguments. It returns the ID of the last query executed in the session. To find the query ID of the second last executed query, users typically need to track query IDs manually or use session history views.

Parquet is a columnar storage file format that is optimized for efficiency in both storage and processing. It supports compression and encoding schemes that significantly reduce the storage space needed and speed up data retrieval operations, making it ideal for handling large volumes of data. Unlike JSON or TSV, which are row-oriented and typically uncompressed, Parquet is designed specifically for use with big data frameworks, offering advantages in terms of performance and cost when storing and querying semi-structured data.References: Apache Parquet Documentation