SnowPro-Core SnowPro Core Certification Exam Questions and Answers

 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

 When an external stage is dropped in Snowflake, the reference to the external storage location is removed, but the actual files within the external storage (like Amazon S3, Google Cloud Storage, or Microsoft Azure) are not deleted. This means that the data remains intact in the external storage location, and only the stage object in Snowflake is removed.

On the other hand, when an internal stage is dropped, any files that were uploaded to the stage are deleted along with the stage itself. These files are not recoverable once the internal stage is dropped, as they are permanently removed from Snowflake’s storage.

References:

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

In Snowflake, a Pipe is the object designed to enable the continuous, near-real-time loading of data from files as soon as they are available in a cloud storage location. Pipes use Snowflake’s COPY command to load data and can be associated with a Stage object to monitor for new files. When new data files appear in the stage, the pipe automatically loads the data into the target table.

References:

• Snowflake Documentation on Pipes
• SnowPro® Core Certification Study Guide

https://docs.snowflake.com/en/user-guide/data-load-snowpipe-intro.html

 The COPY INTO command in Snowflake can be configured to output data into a single file by setting the MAX_FILE_NUMBER option to 1. This option limits the number of files generated by the command, ensuring that only one file is created regardless of the amount of data being exported.

References:

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

 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 .

 To find information about query activity from 90 days ago, a Snowflake user should use the account_usage.query_history_archive view. This view is designed to provide access to historical query data beyond the default 14-day retention period found in the standard query_history view. It allows users to analyze and audit past query activities for up to 365 days after the date of execution, which includes the 90-day period mentioned.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Account Usage Schema1

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

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 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

When a virtual warehouse in Snowflake is resized, specifically when it is increased in size, the additional compute resources become immediately available to all running and queued queries. This means that the performance of these queries can improve due to the increased resources. Conversely, when the size of a warehouse is reduced, the compute resources are not removed until they are no longer being used by any current operations1.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Virtual Warehouses2

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 REST API used with unstructured data in Snowflake is GET /api/files/, which retrieves (downloads) a data file from an internal or external stage4.

 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

The Snowflake Account_Usage.Metering_History view is used to gather the hourly credit usage for an account. This view provides details on the credits consumed by various services within Snowflake for the last 365 days1.

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 secure data sharing feature allows sharing of certain database objects with other Snowflake accounts. Among the options provided, external tables and secure UDFs can be shared

The COPY INTO <table> command in Snowflake supports transformations such as casting using a SELECT statement. This allows for the transformation of data types as the data is being loaded into the table, which can be particularly useful when the data types in the source files do not match the data types in the target table

 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

The Kafka connector stores formatted information in a single column of type VARIANT. This column type is used to store semi-structured data like JSON or Avro, which allows for flexibility in the data structure

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

The minimum privilege required on an external stage for any role to access unstructured data files using a file URL in the GET REST API is READ. This allows the role to retrieve or download data files from the stage.

 Snowflake is designed to optimize query performance by utilizing metadata for certain types of queries. When executing a COUNT query, Snowflake can often fulfill the request by accessing metadata about the table’s row count, rather than scanning the entire table or micro-partitions. This is particularly efficient for large tables like FCT_SALES with a significant number of records. The metadata layer maintains statistics about the table, including the row count, which enables Snowflake to quickly return the result of a COUNT query without the need to perform a full scan.

References:

• Snowflake Documentation on Metadata Management
• SnowPro® Core Certification Study Guide

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 column level security features include Dynamic Data Masking and External Tokenization. Dynamic Data Masking uses masking policies to selectively mask data at query time, while External Tokenization allows for the tokenization of data before loading it into Snowflake and detokenizing it at query runtime5.

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

 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.

When the COPY INTO command is executed in Snowflake, it processes all files present in the specified stage that have not been ingested before or marked as already loaded. Since the user did not remove the original 10 files after the first load, running the same COPY INTO command again will result in all 20 files being processed. This means that the data from the original 10 files will be appended to the table again, along with the data from the new 10 files, potentially leading to duplicate records for the original data set.

References:

• Snowflake Documentation on Data Loading
• SnowPro® Core Certification Study Guide

 Once a database has been created in a consumer account from a share, the shared objects become accessible to users in that account. The shared objects are not transferred or copied; they remain in the provider’s account and are accessible to the consumer account

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

For data loading using Snowpipe, the recommended file size is a compressed file greater than 10 MB and up to 100 MB. This size range is optimal for Snowpipe’s continuous, micro-batch loading process, allowing for efficient and timely data ingestion without overwhelming the system with files that are too large or too small.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Snowpipe1

The Cloud Services layer in Snowflake includes activities such as user authentication and infrastructure management. This layer coordinates activities across Snowflake, including security enforcement, query compilation and optimization, and more

The query that contains a Snowflake hosted file URL in a directory table for a stage named bronzestage is select * from directory(@bronzestage). This query retrieves a list of all files on the stage along with metadata, including the Snowflake file URL for each file3.

 A secure view in Snowflake is exposed only to users with the OWNERSHIP privilege. This privilege ensures that only authorized users who own the view, or roles that include ownership, can access the secure view

 Running EXPLAIN and SHOW commands, as well as running a custom query, consume virtual warehouse credits in the Snowflake environment. These activities require computational resources, and therefore, credits are used to account for the usage of these resources. 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 a Query Profile, the statistical information that indicates a query is too large to fit in memory includes bytes spilled to local cache and bytes spilled to local storage. These metrics suggest that the working data set of the query exceeded the memory available on the warehouse nodes, causing intermediate results to be written to disk

 According to my knowledge, a security administrator can create multiple network policies, but only one network policy can be active for an account or specific user at any given time. This ensures that there is a clear and consistent policy being applied without conflicts. References: Based on my internal knowledge as of 2021.

 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 monitor data storage for individual tables, the commands and objects that can be used are ‘SHOW STORAGE BY TABLE;’ and the Information Schema view ‘TABLE_STORAGE_METRICS’. These tools provide detailed information about the storage utilization for tables. References: Snowflake Documentation

• Managed Access Schemas: These are a special type of schema designed for fine-grained access control in Snowflake.
• Roles with Grant Authority:
• Important Note: The ORGADMIN role focuses on organization-level management, not object access control.

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.

 Snowflake supports semi-structured data types, which include OBJECT and VARIANT. These data types are capable of storing JSON-like data structures, allowing for flexibility in data representation. OBJECT can directly contain VARIANT, and thus indirectly contain any other data type, including itself1.

 When unloading data, Snowflake assigns each file a unique name to ensure there is no overlap or confusion between files. This is part of the bulk unloading process where data is exported from Snowflake tables into flat files3.

 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 USERADMIN role in Snowflake has the ability to create and manage users and roles within the Snowflake environment. This role is specifically dedicated to user and role management and creation

 The kind of Snowflake table that stores file-level metadata for each file in a stage is a directory table. A directory table is an implicit object layered on a stage and stores file-level metadata about the data files in the stage3.

 In Snowflake, the OPERATE privilege is required for a role to suspend or resume a task. This privilege allows the role to perform operational tasks such as starting and stopping tasks, which includes suspending and resuming them6

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.

Snowflake’s Data Exchange feature provides a data hub for secure data collaboration. It allows providers to publish data that can be discovered and accessed by a selected group of invited members, facilitating secure and controlled data sharing within a collaborative environment3. 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

 For optimizing the performance of queries that run on a very large table, it is recommended to choose one high cardinality column as the clustering key. This helps to co-locate similar rows in the same micro-partitions, improving scan efficiency in queries by skipping data that does not match filtering predicates4.

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

The search optimization service in Snowflake is designed to improve the performance of selective point lookup queries on large tables, which is ideal for scenarios where a query returns a small amount of data from a very large base table1. References: [COF-C02] SnowPro Core Certification Exam Study Guide

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

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 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 COPY INTO  command is used to unload data from a Snowflake database table into one or more files in a Snowflake stage1.

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

 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

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

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

 A network policy in Snowflake is used to restrict access to the Snowflake account from unauthorized or unknown sources. It allows administrators to specify allowed IP address ranges, thus preventing access from any IP addresses not listed in the policy1.

To reduce data spilling in Snowflake, using a larger virtual warehouse is effective because it provides more memory and local disk space, which can accommodate larger data operations and minimize the need to spill data to disk or remote storage1. References: [COF-C02] SnowPro Core Certification Exam Study Guide

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.

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

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

 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

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.

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

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 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.

When a tag object is assigned to a table, using the statement CREATE TABLE <table_name> AS SELECT * FROM TABLE_A will automatically assign the TABLE_A tag to the newly created table2.

 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

 In a managed access schema, the schema owner retains the OWNERSHIP privilege of any tables added to the schema. This means that while object owners have certain privileges over the objects they create, only the schema owner can manage privilege grants on these objects1.

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.

Multi-Factor Authentication (MFA) provides increased login security for users connecting to Snowflake. Snowflake’s MFA is powered by Duo Security service, which adds an additional layer of security during the login process.

 Storing JSON data in a table with a VARIANT data type is optimal for querying because it allows Snowflake to leverage its semi-structured data capabilities. This approach enables efficient storage and querying without the need for flattening the data, which can be performance-intensive1.

 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

When using the ALLOW_CLIENT_MFA_CACHING parameter, a cached Multi-Factor Authentication (MFA) token is valid for up to 4 hours. This allows for continuous, secure connectivity without users needing to respond to an MFA prompt at the start of each connection attempt to Snowflake within this timeframe2.

When using the COPY INTO <table> command to load CSV data from a stage into a Snowflake table, one of the crucial guidelines to follow is that the number of columns in each row within the CSV file should be consistent. This ensures data integrity and allows for a smooth data loading process, as Snowflake expects each row in the CSV file to map directly to a row in the target table based on the number of columns. If there's a mismatch in the number of columns between any row in the file and the target table, Snowflake might return an error or produce unexpected results during the load operation.

It's important to note that while the CSV field delimiter can be specified to something other than a comma, ensuring the consistency in the number of columns across all rows is fundamental to successfully loading data.

References:

• Snowflake Documentation on Loading Data: Loading CSV Data

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

The account usage views in Snowflake provide historical usage data about the Snowflake account, and they retain this data for a period of up to 1 year. These views include information about dropped objects, enabling audit and tracking activities. On the other hand, information schema views provide metadata about database objects currently in use, such as tables and views, but do not include dropped objects. The retention of data in information schema views varies, but it is generally shorter than the retention for account usage views, ranging from 7 days to a maximum of 6 months, depending on the specific view.References: Snowflake Documentation on Account Usage and Information Schema

In Snowflake, the use of streams impacts how the data retention period for a table is handled, particularly in scenarios where the stream has not been consumed. The key point to understand is that Snowflake's streams are designed to capture data manipulation language (DML) changes such as INSERTS, UPDATES, and DELETES that occur on a source table. Streams maintain a record of these changes until they are consumed by a DML operation or a COPY command that references the stream.

When a stream is created on a table and remains unconsumed, Snowflake extends the data retention period of the table to ensure that the changes captured by the stream are preserved. This extension is specifically up to the point in time represented by the stream's offset, which effectively ensures that the data necessary for consuming the stream's contents is retained. This mechanism is in place to prevent data loss and ensure the integrity of the stream's data, facilitating accurate and reliable data processing and analysis based on the captured DML changes.

This behavior emphasizes the importance of managing streams and their consumption appropriately to balance between data retention needs and storage costs. It's also crucial to understand how this temporary extension of the data retention period impacts the overall management of data within Snowflake, including aspects related to data lifecycle, storage cost implications, and the planning of data consumption strategies.

References:

• Snowflake Documentation on Streams: Using Streams
• Snowflake Documentation on Data Retention: Understanding Data Retention

Snowflake's Cloud Services layer is responsible for managing various aspects of the platform that are not directly related to computing or storage. Specifically, it handles authorisation, ensuring that users have appropriate access rights to perform actions or access data. Additionally, it takes care of query parsing and optimization, interpreting SQL queries and optimizing their execution plans for better performance. This layer abstracts much of the platform's complexity, allowing users to focus on their data and queries without managing the underlying infrastructure.References: Snowflake Architecture Documentation

One key strategy to lower compute resource credit consumption in Snowflake is by automating the suspension and resumption of virtual warehouses. Virtual warehouses consume credits when they are running, and managing their operational times effectively can lead to significant cost savings.

A. Setting up a multi-cluster virtual warehouse increases parallelism and throughput but does not directly lower credit consumption. It is more about performance scaling than cost efficiency.

B. Resizing the virtual warehouse to a larger size increases the compute resources available for processing queries, which increases the credit consumption rate. This option does not help in lowering costs.

C. Automating the virtual warehouse suspension and resumption settings: This is a direct method to manage credit consumption efficiently. By automatically suspending a warehouse when it is not in use and resuming it when needed, you can avoid consuming credits during periods of inactivity. Snowflake allows warehouses to be configured to automatically suspend after a specified period of inactivity and to automatically resume when a query is submitted that requires the warehouse.

D. Increasing the maximum cluster count parameter for a multi-cluster virtual warehouse would potentially increase credit consumption by allowing more clusters to run simultaneously. It is used to scale up resources for performance, not to reduce costs.

Automating the operational times of virtual warehouses ensures that you only consume compute credits when the warehouse is actively being used for queries, thereby optimizing your Snowflake credit usage.

Access History in Snowflake is primarily utilized for compliance auditing. The Access History feature provides detailed logs that track data access and modifications, including queries that read from or write to database objects. This information is crucial for organizations to meet regulatory requirements and to perform audits related to data access and usage.

• Role of Access History: Access History logs are designed to help organizations understand who accessed what data and when. This is particularly important for compliance with various regulations that require detailed auditing capabilities.
• How Access History Supports Compliance Auditing:

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

• B. Provides a unified picture of what data was accessed and when it was accessed: This view logs details about query executions, including the objects (tables, views) accessed and the timestamps of these accesses. It's instrumental in auditing and compliance scenarios, where understanding the access patterns to sensitive data is critical.
• E. Determines whether a given row in a table can be accessed by the user by filtering the data based on a given policy: While this option is a bit of a misinterpretation of what ACCESS_HISTORY directly offers, it indirectly supports data governance by providing the information necessary to analyze access patterns. This analysis can then inform policy decisions, such as implementing Row-Level Security (RLS) to restrict access to specific rows based on user roles or attributes.

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

References:

• Snowflake Documentation on ACCESS_HISTORY: Access History

The LOGIN_HISTORY view in SNOWFLAKE.ACCOUNT_USAGE shows from which IP address a user connected to Snowflake. This view is particularly useful for auditing and monitoring purposes, as it helps administrators track login attempts, successful logins, and the geographical location of users based on their IP addresses.

Reference to Snowflake documentation on LOGIN_HISTORY:

• Monitoring Login Attempts

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

In Snowflake, the consumption of credits by a virtual warehouse is determined by its size and the duration for which it runs. A size 3X-Large virtual warehouse consumes 128 credits if it runs continuously for 2 hours. This consumption rate is based on the principle that larger warehouses, capable of providing greater computational resources and throughput, consume more credits per hour of operation. The specific rate of consumption is defined by Snowflake’s pricing model and the scale of the virtual warehouse.References: Snowflake Pricing Documentation

To see the definition of a secure view in Snowflake, the minimum privilege required is OWNERSHIP of the view. Ownership grants the ability to view the definition as well as to modify or drop the view. Secure views are designed to protect sensitive data, and thus the definition of these views is restricted to users with sufficient privileges to ensure data security.

References:

• Snowflake Documentation: Secure Views

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

In Snowflake, a user within a reader account primarily has read-only access to the shared data. Reader accounts are created to enable third parties or separate business units to access and query data shared with them without allowing them to modify the underlying data. This means a user with a reader account can perform operations like querying shared data to analyze or report on it but cannot load new data, update existing data, or create new shares. This setup is crucial for maintaining data governance and security while enabling data sharing and collaboration.References: Snowflake Documentation on Reader Accounts

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 supports a variety of subquery types, including both correlated and uncorrelated subqueries. The correct answers are B and E, which highlight Snowflake's flexibility in handling subqueries within SQL queries.

• Uncorrelated Scalar Subqueries: These are subqueries that can execute independently of the outer query. They return a single value and can be used anywhere a value expression is allowed, offering great flexibility in SQL queries.
• EXISTS, ANY/ALL, and IN Subqueries: These subqueries are used in WHERE clauses to filter the results of the main query based on the presence or absence of matching rows in a subquery. Snowflake supports both correlated and uncorrelated versions of these subqueries, providing powerful tools for complex data analysis scenarios.
• Examples and Usage:

SELECT * FROM employees WHERE salary > (SELECT AVG(salary) FROM employees);

• uk.co.certification.simulator.questionpool.PList@18720860

SELECT * FROM orders o WHERE EXISTS (SELECT 1 FROM customer c WHERE c.id = o.customer_id AND c.region = 'North America');

Resource monitors in Snowflake are tools used to track and control the consumption of compute resources, ensuring that usage stays within defined limits. These monitors can be created at the account level, allowing administrators to set overall resource consumption limits for the entire Snowflake account. Additionally, resource monitors can be set at the virtual warehouse level, enabling more granular control over the resources consumed by individual warehouses. This dual-level capability allows organizations to manage their Snowflake usage efficiently, preventing unexpected costs and optimizing performance.References: Snowflake Documentation on Resource Monitors

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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

The primary factor that determines the cost of using a virtual warehouse in Snowflake is D. The length of time the compute resources in each cluster run. Snowflake's pricing model for compute usage is based on the concept of Snowflake credits, which are consumed based on the time virtual warehouses are running and the size of the warehouses. The more compute resources are utilized and the longer they run, the more credits are consumed, which directly impacts the cost.

References:

• Snowflake Documentation on Virtual Warehouses: Understanding and Managing Warehouse Credit Usage

Snowflake’s Time Travel feature allows users to access historical data within a specific period. This feature supports the restoration of various objects, including databases and schemas, to their previous states. Time Travel can be used for recovering dropped objects, undoing accidental changes, or analyzing data changes over time. However, it does not support user or role objects like Users and Roles, or compute resources like Virtual Warehouses.References: Snowflake Documentation on Time Travel

In the Snowflake Data Exchange, the ability to get or request data from listings is generally controlled by specific roles and privileges:

• A. Users with ACCOUNTADMIN role: This role typically has the highest level of access within a Snowflake account, including the ability to manage and access all features and functions. Users with this role can access data listings within the Data Exchange.
• D. Users with import share privilege: This specific privilege is necessary for users who need to import shared data from the Data Exchange. This privilege allows them to request and access data listings explicitly shared with them.