These three statements are true according to the Snowflake documentation and the web search results. A row access policy is a feature that allows filtering rows based on user-defined conditions. A row access policy can be applied to an external table, which is a table that reads data from external files in a stage. However, there are some limitations and considerations for using row access policies with external tables.

• An external table can be created with a row access policy by using the WITH ROW ACCESS POLICY clause in the CREATE EXTERNAL TABLE statement. The policy can be applied to theVALUE column, which is the column that contains the raw data from the external files in a VARIANT data type1.
• A row access policy can also be applied to the VALUE column of an existing external table by using the ALTER TABLE statement with the SET ROW ACCESS POLICY clause2.
• A row access policy cannot be directly added to a virtual column of an external table. A virtual column is a column that is derived from the VALUE column using an expression. To apply a row access policy to a virtual column, the policy must be applied to the VALUE column and the expression must be repeated in the policy definition3.
• External tables are not supported as mapping tables in a row access policy. A mapping table is a table that is used to determine the access rights of users or roles based on some criteria. Snowflake does not support using an external table as a mapping table because it may cause performance issues or errors4.
• While cloning a database, Snowflake clones the row access policy, but not the external table. Therefore, the policy in the cloned database refers to a table that is not present in the cloned database. To avoid this issue, the external table must be manually cloned or recreated in the cloned database4.
• A row access policy can be applied to a view created on top of an external table. The policy can be applied to the view itself or to the underlying external table. However, if the policy is applied to the view, the view must be a secure view, which is a view that hides the underlying data and the view definition from unauthorized users5.

References:

• CREATE EXTERNAL TABLE | Snowflake Documentation
• ALTER EXTERNAL TABLE | Snowflake Documentation
• Understanding Row Access Policies | Snowflake Documentation
• Snowflake Data Governance: Row Access Policy Overview
• Secure Views | Snowflake Documentation

Comprehensive and Detailed Explanation: According to the SnowPro Advanced: Architect documents and learning resources, some of the characteristics of result set caches are:

• Snowflake persists the data results for 24 hours. This means that the result set cache holds the results of every query executed in the past 24 hours, and can be reused if the same query is submitted again and the underlying data has not changed1.
• Each time persisted results for a query are used, a 24-hour retention period is reset. This means that the result set cache extends the lifetime of the results every time they are reused, up to a maximum of 31 days from the date and time that the query was first executed1.
• The retention period can be reset for a maximum of 31 days. This means that the result set cache will purge the results after 31 days, regardless of whether they are reused or not. After 31 days, the next time the query is submitted, a new result is generated and persisted1.

The other options are incorrect because they are not characteristics of result set caches. Option A is incorrect because Time Travel queries cannot be executed against the result set cache. Time Travel queries use the AS OF clause to access historical data that is stored in the storage layer, not the result set cache2. Option D is incorrect because the data stored in the result set cache does not contribute to storage costs. The result set cache is maintained by the service layer, and does not incur any additional charges1. Option F is incorrect because the result set cache is shared between warehouses. The result set cache is available across virtual warehouses, so query results returned to one user are available to any other user on the system who executes the same query, provided the underlying data has not changed1. References: Using Persisted Query Results | Snowflake Documentation, Time Travel | Snowflake Documentation

 Snowflake Connector for Kafka and Snowpipe are two ingestion methods that can be used to load near real-time data by using the messaging services provided by a cloud provider. Snowflake Connector for Kafka enables you to stream structured and semi-structured data from Apache Kafka topics into Snowflake tables. Snowpipe enables you to load data from files that are continuously added to a cloud storage location, such as Amazon S3 or Azure Blob Storage. Both methods leverage Snowflake’s micro-partitioning and columnar storage to optimize data ingestion and query performance. Snowflake streams and Spark are not ingestion methods, but rather components of the Snowflake architecture. Snowflake streams provide change data capture (CDC) functionality by tracking data changes in a table. Spark is a distributed computing framework that can be used to process large-scale data and write it to Snowflake using the Snowflake Spark Connector. References:

• Snowflake Connector for Kafka
• Snowpipe
• Snowflake Streams
• Snowflake Spark Connector

• The correct answer is B and E because they use the correct syntax and values for the identifier function and the session variables.
• The identifier function allows you to use a variable or expression as an identifier (such as a table name or column name) in a SQL statement. It takes a single argument and returns it as an identifier. For example, identifier($tbl_ref) returns EMP_TBL as an identifier.
• The session variables are set using the SET command and can be referenced using the $ sign. For example, $var1 returns Name1 as a value.
• Option A is incorrect because it uses Stbl_ref and Scol_ref, which are not valid session variables or identifiers. They should be $tbl_ref and $col_ref instead.
• Option C is incorrect because it uses identifier, which is not a valid syntax for the identifier function. It should be identifier($tbl_ref) instead.
• Option D is incorrect because it uses Cvarl, var2, and var3, which are not valid session variables or values. They should be $var1, $var2, and $var3 instead. References:
• Snowflake Documentation: Identifier Function
• Snowflake Documentation: Session Variables
• Snowflake Learning: SnowPro Advanced: Architect Exam Study Guide

The requirement is for the data to be accessible as quickly as possible after it arrives in the external stage with minimal coding effort.

Option A:Snowpipe with auto-ingest is a service that continuously loads data as it arrives in the stage. With auto-ingest, Snowpipe automatically detects new files as they arrive in a cloud stage and loads the data into the specified Snowflake table with minimal delay and no intervention required. This is an ideal low-maintenance solution for the given scenario where files are arriving at a very high frequency.

Option E:Using a combination of a task and a stream allows for real-time change data capture in Snowflake. A stream records changes (inserts, updates, and deletes) made to a table, and a task can be scheduled to trigger on a very short interval, ensuring that changes are processed into the dashboard tables as they occur.

https://docs.snowflake.com/en/user-guide/data-sharing-intro

According to the SnowPro Advanced: Architect documents and learning resources, the organization-related tasks that can be performed by the ORGADMIN role are:

• Creating an account in the organization. A user with the ORGADMIN role can use the CREATE ACCOUNT command to create a new account that belongs to the same organization as the current account1.
• Viewing a list of organization accounts. A user with the ORGADMIN role can use the SHOW ORGANIZATION ACCOUNTS command to view the names and properties of all accounts in the organization2. Alternatively, the user can use the Admin » Accounts page in the web interface to view the organization name and account names3.
• Enabling the replication of a database. A user with the ORGADMIN role can use the SYSTEM$GLOBAL_ACCOUNT_SET_PARAMETER function to enable database replication for an account in the organization. This allows the user to replicate databases across accounts in different regions and cloud platforms for data availability and durability4.

The other options are incorrect because they are not organization-related tasks that can be performed by the ORGADMIN role. Option A is incorrect because changing the name of the organization is not a task that can be performed by the ORGADMIN role. To change the name of an organization, the user must contact Snowflake Support3. Option D is incorrect because changing the name of an account is not a task that can be performed by the ORGADMIN role. To change the name of an account, the user must contact Snowflake Support5. Option E is incorrect because deleting an account is not a task that can be performed by the ORGADMIN role. To delete an account, the user must contact Snowflake Support. References: CREATE ACCOUNT | Snowflake Documentation, SHOW ORGANIZATION ACCOUNTS | Snowflake Documentation, Getting Started with Organizations | Snowflake Documentation, SYSTEM$GLOBAL_ACCOUNT_SET_PARAMETER | Snowflake Documentation, ALTER ACCOUNT | Snowflake Documentation, [DROP ACCOUNT | Snowflake Documentation]

Dynamic Data Masking is a feature that allows masking sensitive data in query results based on the role of the user who executes the query. A masking policy is a user-defined function that specifies the masking logic and can be applied to one or more columns in one or more tables. A masking policy that is currently set on a table can be dropped using the ALTER TABLE command. A single masking policy can be applied to columns in different tables using the ALTER TABLE command with the SET MASKING POLICY clause. The other options are either incorrect or not supported by Snowflake. A masking policy cannot be applied to the value column of an external table, as external tables do not support column-level security. The role that creates the masking policy will not always see unmasked data in query results, as the masking policy can be applied to the owner role as well. A masking policy cannot be applied to a column with the GEOGRAPHY data type, as Snowflake only supports masking policies for scalar data types. References: Snowflake Documentation: Dynamic Data Masking, Snowflake Documentation: ALTER TABLE

An external table schema defines the columns and data types of the data stored in an external stage. All external tables include the following columns by default:

• VALUE: A VARIANT type column that represents a single row in the external file.
• METADATA$FILENAME: A pseudocolumn that identifies the name of each staged data file included in the external table, including its path in the stage.
• METADATA$FILE_ROW_NUMBER: A pseudocolumn that shows the row number for each record in a staged data file.

You can also create additional virtual columns as expressions using the VALUE column and/or the pseudocolumns. However, the following columns are not valid for external tables and cannot be included in the schema:

• METADATASROW_ID: This column is only available for internal tables and shows the unique identifier for each row in the table.
• METADATASISUPDATE: This column is only available for internal tables and shows whether the row was inserted or updated by a merge operation.
• METADATASEXTERNAL TABLE PARTITION: This column is not a valid column name and does not exist in Snowflake.

References: Introduction to External Tables, CREATE EXTERNAL TABLE

 These two actions are possible with an inbound share, according to the Snowflake documentation and the web search results. An inbound share is a share that is created by another Snowflake account (the provider) and imported into your account (the consumer). An inbound share allows you to access the data shared by the provider, but not to modify or delete it. However, you can perform some actions with the inbound share, such as:

• Clone a table from a share. You can create a copy of a table from an inbound share using the CREATE TABLE … CLONE statement. The clone will contain the same data and metadata as the original table, but it will be independent of the share. You can modify or delete the clone as you wish, but it will not reflect any changes made to the original table by the provider1.
• Create additional views inside the shared database. You can create views on the tables or views from an inbound share using the CREATE VIEW statement. The views will be stored in the shared database, but they will be owned by your account. You can query the views as you would query any other view in your account, but you cannot modify or delete the underlying objects from the share2.

The other actions listed are not possible with an inbound share, because they would require modifying the share or the shared objects, which are read-only for the consumer. You cannot grant modify permissions on the share, create a table from the shared database, or create a table stream on the shared table34.

References:

• Cloning Objects from a Share | Snowflake Documentation
• Creating Views on Shared Data | Snowflake Documentation
• Importing Data from a Share | Snowflake Documentation
• Streams on Shared Tables | Snowflake Documentation

 A star schema model is a type of dimensional data model that consists of a single fact table and multiple dimension tables. A 3NF model is a type of relational data model that follows the third normal form, which eliminates data redundancy and ensures referential integrity. A Snowflake Architect might use a star schema model rather than a 3NF model when designing a data architecture to run in Snowflake for the following reasons:

• A star schema model is more suitable for analytical queries that require aggregating and slicing data across different dimensions, such as those performed by a BI tool. A 3NF model is more suitable for transactional queries that require inserting, updating, and deleting individual records.
• A star schema model is simpler and faster to query than a 3NF model, as it involves fewer joins and less complex SQL statements. A 3NF model is more complex and slower to query, as it involves more joins and more complex SQL statements.
• A star schema model can provide a simple flattened single view of the data to a particular group of end users, such as business analysts or data scientists, who need to explore and visualize the data. A 3NF model can provide a more detailed and normalized view of the data to a different group of end users, such as application developers or data engineers, who need to maintain and update the data.

The other options are not valid reasons for choosing a star schema model over a 3NF model in Snowflake:

• Snowflake can handle the joins implied in a 3NF data model, as it supports ANSI SQL and has a powerful query engine that can optimize and execute complex queries efficiently.
• The Architect can use both star schema and 3NF models to remove data duplication from the data stored in Snowflake, as both models can enforce data integrity and avoid data anomalies. However, the trade-off is that a star schema model may have more data redundancy than a 3NF model, as it denormalizes the data for faster query performance, while a 3NF model may have less data redundancy than a star schema model, as it normalizes the data for easier data maintenance.
• The Architect can use both star schema and 3NF models to design a landing zone to receive raw data into Snowflake, as both models can accommodate different types of data sources and formats. However, the choice of the model may depend on the purpose and scope of the landing zone, such as whether it is a temporary or permanent storage, whether it is a staging area or a data lake, and whether it is a single source or a multi-source integration.

References:

• Snowflake Architect Training
• Data Modeling: Understanding the Star and Snowflake Schemas
• Data Vault vs Star Schema vs Third Normal Form: Which Data Model to Use?
• Star Schema vs Snowflake Schema: 5 Key Differences
• Dimensional Data Modeling - Snowflake schema
• Star schema vs Snowflake Schema

The Snowflake system functions used to view and monitor the clustering metadata for a table are:

• SYSTEM$CLUSTERING_INFORMATION
• SYSTEM$CLUSTERING_DEPTH

Comprehensive But Short Explanation:

• TheSYSTEM$CLUSTERING_INFORMATIONfunction in Snowflake returns a variety of clustering information for a specified table. This information includes the average clustering depth, total number of micro-partitions, total constant partition count, average overlaps, average depth, and a partition depth histogram. This function allows you to specify either one or multiple columns for which the clustering information is returned, and it returns this data in JSON format.
• TheSYSTEM$CLUSTERING_DEPTHfunction computes the average depth of a table based on specified columns or the clustering key defined for the table. A lower average depth indicates that the table is better clustered with respect to the specified columns. This function also allows specifying columns to calculate the depth, and the values need to be enclosed in single quotes.

References:

• SYSTEM$CLUSTERING_INFORMATION: Snowflake Documentation
• SYSTEM$CLUSTERING_DEPTH: Snowflake Documentation

• Warehouse credits are used to pay for the processing time used by each virtual warehouse in Snowflake. A virtual warehouse is a cluster of compute resources that enables executing queries, loading data, and performing other DML operations. Warehouse credits are charged based on the number of virtual warehouses you use, how long they run, and their size1.
• Among the commands listed in the question, the following ones will use warehouse credits:
• The command that will not use warehouse credits is:

References: : Understanding Compute Cost : MAX Function : COUNT Function : GROUP BY Clause : SHOW TABLES

To provide near real-time sales results to category managers, the Architect can use the following steps:

• Create an external stage that references the cloud storage location where the POS sends the sales transactions files. The external stage should use the file format and encryption settings that match the source files2
• Create a Snowpipe that loads the files from the external stage into a target table in Snowflake. The Snowpipe should be configured with AUTO_INGEST = true, which means that itwill automatically detect and ingest new files as they arrive in the external stage. The Snowpipe should also use a copy option to purge the files from the external stage after loading, to avoid duplicate ingestion3
• Create a stream on the target table that captures the INSERTS made by the Snowpipe. The stream should include the metadata columns that provide information about the file name, path, size, and last modified time. The stream should also have a retention period that matches the real-time analytics needs4
• Create a task that runs a query on the stream to process the near real-time data. The query should use the stream metadata to extract the store number and timestamps from the file name and path, and perform the calculations for exceptions, aggregations, and scoring using external functions. The query should also output the results to another table or view that can be accessed by the category managers. The task should be scheduled to run at a frequency that matches the real-time analytics needs, such as every minute or every 5 minutes.

The other options are not optimal or feasible for providing near real-time results:

• All files should be concatenated before ingestion into Snowflake to avoid micro-ingestion. This option is not recommended because it would introduce additional latency and complexity in the data pipeline. Concatenating files would require an external process or service that monitors the cloud storage location and performs the file merging operation. This would delay the ingestion of new files into Snowflake and increase the risk of data loss or corruption. Moreover, concatenating files would not avoid micro-ingestion, as Snowpipe would still ingest each concatenated file as a separate load.
• An external scheduler should examine the contents of the cloud storage location and issue SnowSQL commands to process the data at a frequency that matches the real-time analytics needs. This option is not necessary because Snowpipe can automatically ingest new files from the external stage without requiring an external trigger or scheduler. Using an external scheduler would add more overhead and dependency to the data pipeline, and it would not guarantee near real-time ingestion, as it would depend on the polling interval and the availability of the external scheduler.
• The copy into command with a task scheduled to run every second should be used to achieve the near-real time requirement. This option is not feasible because tasks cannot be scheduled to run every second in Snowflake. The minimum interval for tasks is one minute, and even that is not guaranteed, as tasks are subject to scheduling delays and concurrency limits. Moreover, using the copy into command with a task would not leverage the benefits of Snowpipe, such as automatic file detection, load balancing, and micro-partition optimization. References:
• 1: SnowPro Advanced: Architect | Study Guide
• 2: Snowflake Documentation | Creating Stages
• 3: Snowflake Documentation | Loading Data Using Snowpipe
• 4: Snowflake Documentation | Using Streams and Tasks for ELT
• : Snowflake Documentation | Creating Tasks
• : Snowflake Documentation | Best Practices for Loading Data
• : Snowflake Documentation | Using the Snowpipe REST API
• : Snowflake Documentation | Scheduling Tasks
• : SnowPro Advanced: Architect | Study Guide
• : Creating Stages
• : Loading Data Using Snowpipe
• : Using Streams and Tasks for ELT
• : [Creating Tasks]
• : [Best Practices for Loading Data]
• : [Using the Snowpipe REST API]
• : [Scheduling Tasks]

• A healthcare company that handles PHI data must ensure compliance with relevant privacy standards, such as HIPAA, HITRUST, and GDPR. Snowflake provides several features and best practices to help customers meet their data protection and compliance requirements1.
• One best practice recommendation is to use, at minimum, the Business Critical edition of Snowflake. This edition provides the highest level of data protection and security, including end-to-end encryption with customer-managed keys, enhanced object-level security, and HIPAA and HITRUST compliance2. Therefore, option A is correct.
• Another best practice recommendation is to create Dynamic Data Masking policies and apply them to columns that contain PHI. Dynamic Data Masking is a feature that allows masking or redacting sensitive data based on the current user’s role. This way, only authorized users can view the unmasked data, while others will see masked values, such as NULL, asterisks, or random characters3. Therefore, option B is correct.
• A third best practice recommendation is to use the External Tokenization feature to obfuscate sensitive data. External Tokenization is a feature that allows replacing sensitive data with tokens that are generated and stored by an external service, such as Protegrity. This way, the original data is never stored or processed by Snowflake, and only authorized users can access the tokenized data through the external service4. Therefore, option D is correct.
• Option C is incorrect, because the Internal Tokenization feature is not available in Snowflake. Snowflake does not provide any native tokenization functionality, but only supports integration with external tokenization services4.
• Option E is incorrect, because rewriting SQL queries to eliminate projections of PHI data based on current_role() is not a best practice. This approach is error-prone, inefficient, and hard to maintain. A better alternative is to use Dynamic Data Masking policies, which can automatically mask data based on the user’s role without modifying the queries3.
• Option F is incorrect, because avoiding sharing data with partner organizations is not a best practice. Snowflake enables secure and governed data sharing with internal and external consumers, such as business units, customers, or partners. Data sharing does not involve copying or moving data, but only granting access privileges to the shared objects. Data sharing can also leverage Dynamic Data Masking and External Tokenization features to protect sensitive data5.

References: : Snowflake’s Security & Compliance Reports : Snowflake Editions : Dynamic Data Masking : External Tokenization : Secure Data Sharing