ARA-C01 Practice Exam Questions with Answers SnowPro Advanced: Architect Certification Exam Certification

Enabling the search optimization service on the table can improve the performance of queries that have selective filtering criteria, which seems to be the case here. This service optimizes the execution of queries by creating a persistent data structure called a search access path, which allows some micro-partitions to be skipped during the scanning process. This can significantly speed up query performance without increasing compute costs1.

References

•Snowflake Documentation on Search Optimization Service1.

 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

 Option C is the correct answer because schema-on-read is a technique that allows Snowflake to ingest and consume semi-structured data without requiring a predefined schema. Snowflake supports various semi-structured data formats such as JSON, Avro, ORC, Parquet, and XML, and provides native data types (ARRAY, OBJECT, and VARIANT) for storing them. Snowflake also provides native support for querying semi-structured data using SQL and dot notation. Schema-on-read enables Snowflake to query semi-structured data at the same speed as performing relational queries while preserving the flexibility of schema-on-read. Snowflake’s near-instant elasticity rightsizes compute resources, and consumption-based pricing ensures you only pay for what you use.

Option A is incorrect because IDEF1X is a data modeling technique that defines the structure and constraints of relational data using diagrams and notations. IDEF1X is not suitable for ingesting and consuming semi-structured data, which does not have a fixed schema or structure.

Option B is incorrect because schema-on-write is a technique that requires defining a schema before loading and processing data. Schema-on-write is not efficient for ingesting and consuming semi-structured data, which may have varying or complex structures that are difficult to fit into a predefined schema. Schema-on-write also introduces additional overhead and complexity for data transformation and validation.

Option D is incorrect because information schema is a set of metadata views that provide information about the objects and privileges in a Snowflake database. Information schema is not a technique for ingesting and consuming semi-structured data, but rather a way of accessing metadata about the data.

References:

• Semi-structured Data
• Snowflake for Data Lake

The Snowflake SQL REST API is a REST API that you can use to access and update data in a Snowflake database. You can use this API to execute standard queries and most DDL and DML statements. This API can be used to develop custom applications and integrations that can read and write data to Snowflake without installing any additional software on the application server. Option A is not correct because SnowSQL is a command-line client that requires installation and configuration on the application server. Option B is not correct because the Snowpipe REST API is used to load data from cloud storage into Snowflake tables, not to read or write data to Snowflake. Option D is not correct because the Snowflake ODBC driver is a software component that enables applications to connect to Snowflake using the ODBC protocol, which also requires installation and configuration on the application server. References: The answer can be verified from Snowflake’s official documentation on the Snowflake SQL REST API available on their website. Here are some relevant links:

• Snowflake SQL REST API | Snowflake Documentation
• Introduction to the SQL API | Snowflake Documentation
• Submitting a Request to Execute SQL Statements | Snowflake Documentation

This is the correct answer because it allows the ORDER_ADMIN role to perform the data cleanup without needing the DELETE privilege on the ORDERS table. A stored procedure is a feature that allows scheduling and executing SQL statements or stored procedures in Snowflake. A stored procedure can run with either the caller’s rights or the owner’s rights. A caller’s rights stored procedure runs with the privileges of the role that called the stored procedure, while an owner’s rights stored procedure runs with the privileges of the role that created the stored procedure. By creating a stored procedure that runs with owner’s rights, the ORDER_MANAGER role can delegate the specific task of deleting old data to the ORDER_ADMIN role, without granting the ORDER_ADMIN role more general privileges on the ORDERS table. The stored procedure must include the appropriate business logic to delete only the records older than 5 years, and the ORDER_MANAGER role must grant the USAGE privilege on the stored procedure to the ORDER_ADMIN role. The ORDER_ADMIN role can then execute the stored procedure to perform the data cleanup12.

References:

• Snowflake Documentation: Stored Procedures
• Snowflake Documentation: Understanding Caller’s Rights and Owner’s Rights Stored Procedures

Tri-Secret Secure is a feature that allows customers to use their own key, called the customer-managed key (CMK), in addition to the Snowflake-managed key, to create a composite master key that encrypts the data in Snowflake. The composite master key is also known as the account master key (AMK), as it is unique for each account and encrypts the table master keys (TMKs) that encrypt the file keys that encrypt the data files. The customer-managed key is used at the account level, not at the root level, the table level, or the micro-partition level. The root level is protected by a hardware security module (HSM), the table level is protected by the TMKs, and the micro-partition level is protected by the file keys12. References:

• Understanding Encryption Key Management in Snowflake
• Tri-Secret Secure FAQ for Snowflake on AWS

In Snowflake, data sharing capabilities allow a Business Critical edition account to share data with an Enterprise edition consumer account. The ability to share data is contingent upon the role permissions within the provider account. If a user has the necessary role authority (like ACCOUNTADMIN or a role with similar privileges to create or manage shares), they can add an Enterprise edition account as a consumer. This feature enables flexibility in data sharing across different Snowflake account editions, facilitating broader data collaboration and accessibility.References: Snowflake's data sharing documentation and the specifics of edition-based capabilities discussed in SnowPro Advanced: Architect certification materials.

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]

 This is the correct answer because it allows the company to ingest data from different regions using a storage integration for the external stage. A storage integration is a feature that enables secure and easy access to files in external cloud storage from Snowflake. A storage integration can be used to create an external stage, which is a named location that references the files in the external storage. An external stage can be used to load data into Snowflake tables using the COPY INTO command, or to unload data from Snowflake tables using the COPY INTO LOCATION command. A storage integration can support multiple regions and cloud platforms, as long as the external storage service is compatible with Snowflake12.

References:

• Snowflake Documentation: Storage Integrations
• Snowflake Documentation: External Stages

In Snowflake, the change tracking metadata for a table is utilized by the MERGE command and the STREAM object. The MERGE command uses change tracking to determine how to apply updates and inserts efficiently based on differences between source and target tables. STREAM objects, on the other hand, specifically capture and store change data, enabling incremental processing based on changes made to a table since the last stream offset was committed.References: Snowflake Documentation on MERGE and STREAM Objects.

The most cost-effective way to increase the availability and performance of the reports during peak usage times, while keeping costs under control, is to use a multi-cluster warehouse in auto-scale mode. Option D suggests using a multi-cluster warehouse with 1 size Medium cluster and allowing it to auto-scale between 1 and 4 clusters based on demand. This setup ensures that additional computing resources are available when needed (e.g., during Monday morning peaks) and are scaled down to minimize costs when the demand decreases. This approach optimizes resource utilization and cost by adjusting the compute capacity dynamically, rather than maintaining a larger fixed size or multiple clusters continuously.References: Snowflake's official documentation on managing warehouses and using auto-scaling features.

A clustering key is a subset of columns or expressions that are used to co-locate the data in the same micro-partitions, which are the units of storage in Snowflake. Clustering can improve the performance of queries that filter on the clustering key columns, as it reduces the amount of data that needs to be scanned. The best choice for a clustering key depends on the query patterns and the data distribution in the table. In this case, the columns DeviceId, IOT_timestamp, and CustomerId are frequently used in the filter predicate for the select statement, which means they are good candidates for the clustering key. The columns City and DeviceManufacturer are often retrieved, but not filtered on, so they are not as important for the clustering key. The column UniqueId is used for counting, but it is not a good choice for the clustering key, as it is likely to have a high cardinality and a uniform distribution, which means it will not help to co-locate the data. Therefore, the best option is to use DeviceId and CustomerId as the clustering key, as they can help to prune the micro-partitions and speed up the queries. References: Clustering Keys & Clustered Tables, Micro-partitions & Data Clustering, A Complete Guide to Snowflake Clustering

• The masking policy defined in the image indicates that if a user has the PI_ANALYTICS role, they will be able to see the last 4 characters of the CREDITCARDNO column data in clear text. Otherwise, they will see ‘MASKED’. Since Snowflake system roles have not been granted any additional roles, they won’t have the PI_ANALYTICS role and therefore cannot view the last 4 characters of credit card numbers.
• To apply a masking policy on a column in Snowflake, you need to use the ALTER TABLE … ALTER COLUMN command or the ALTER VIEW command and specify the policy name. For example, to apply the creditcardno_mask policy on the CREDITCARDNO column of the CREDITCARDINFO table, you can use the following command:

ALTER TABLE CREDITCARDINFO ALTER COLUMN CREDITCARDNO SET MASKING POLICY creditcardno_mask;

• For more information on how to create and use masking policies in Snowflake, you can refer to the following resources:

CREATE MASKING POLICY: This document explains the syntax and usage of the CREATE MASKING POLICY command, which allows you to create a new masking policy or replace an existing one.

Using Dynamic Data Masking: This guide provides instructions on how to configure and use dynamic data masking in Snowflake, which is a feature that allows you to mask sensitive data based on the execution context of the user.

ALTER MASKING POLICY: This document explains the syntax and usage of the ALTER MASKING POLICY command, which allows you to modify the properties of an existing masking policy.

References: 1: https://docs.snowflake.com/en/sql-reference/sql/create-masking-policy  2: https://docs.snowflake.com/en/user-guide/security-column-ddm-use  3: https://docs.snowflake.com/en/sql-reference/sql/alter-masking-policy

 When a database is cloned, all of its objects, including tasks, are also cloned. However, cloned tasks are suspended by default and must be manually resumed by using the ALTER TASK command. This is to prevent the cloned tasks from running unexpectedly or interfering with the original tasks. Therefore, the reason why the tasks stop running after the cloning is because they are suspended by default (Option C). Options A, B, and D are not correct because tasks can be cloned, the objects that the tasks reference are also cloned and do not need to be fully qualified, and the Architect does not need to alter the tasks on the cloned database, only resume them. References: The answer can be verified from Snowflake’s official documentation on cloning and tasks available on their website. Here are some relevant links:

• Cloning Objects | Snowflake Documentation
• Tasks | Snowflake Documentation
• ALTER TASK | Snowflake Documentation

 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

 According to the Snowflake documentation, the best practices for choosing clustering keys are:

• Choose columns that are frequently used in join predicates. This can improve the join performance by reducing the number of micro-partitions that need to be scanned and joined.
• Choose columns that are most actively used in selective filters. This can improve the scan efficiency by skipping micro-partitions that do not match the filter predicates.
• Avoid using low cardinality columns, such as gender or country, as clustering keys. This can result in poor clustering and high maintenance costs.
• Avoid using TIMESTAMP columns with nanoseconds, as they tend to have very high cardinality and low correlation with other columns. This can also result in poor clustering and high maintenance costs.
• Avoid using columns with duplicate values or NULLs, as they can cause skew in the clustering and reduce the benefits of pruning.
• Cluster on multiple columns if the queries use multiple filters or join predicates. This can increase the chances of pruning more micro-partitions and improve the compression ratio.
• Clustering is not always useful, especially for small or medium-sized tables, or tables that are not frequently queried or updated. Clustering can incur additional costs for initially clustering the data and maintaining the clustering over time.

References:

• Clustering Keys & Clustered Tables | Snowflake Documentation
• [Considerations for Choosing Clustering for a Table | Snowflake Documentation]

According to the Snowflake documentation, tasks are objects that enable scheduling and execution of SQL statements or JavaScript user-defined functions (UDFs) in Snowflake. Tasks can be used to automate data loading, transformation, and maintenance operations. Snowflake scripting is a feature that allows writing procedural logic using SQL statements and JavaScript UDFs. Snowflake scripting can be used to create complex workflows and orchestrate tasks. Therefore, the best option to automate the daily import of two files from an external stage into Snowflake, join and aggregate the data, and produce a final result set is to create a task using Snowflake scripting that will import the files using the COPY INTO command, and then call a UDF to perform the join and aggregation logic. The UDF can return a table or a variant value as the final result set. References:

• Tasks
• Snowflake Scripting
• User-Defined Functions

In Snowflake, a storage integration is used to define and configure external cloud storage that Snowflake will interact with. This includes specifying security policies for access control. One of the main features of storage integrations is the ability to set restrictions on where data may be exported. This is done by binding the storage integration to specific cloud storage locations, thereby ensuring that Snowflake can only access those locations. It helps to maintain control over the data and complies with data governance and security policies by preventing unauthorized data exports to unspecified locations.

The SYSTEM$GET_PRIVATELINK function is used to retrieve the list of Snowflake service endpoints that need to be accessible when configuring private connectivity (such as AWS PrivateLink or Azure Private Link) for a Snowflake account. The function returns information necessary for setting up the networking infrastructure that allows secure and private access to Snowflake without using the public internet. SnowCD can then be used to verify connectivity to these endpoints.

Data Vault 2.0 on Snowflake supports the HASH_DIFF concept for change data capture, which is a method to detect changes in the data by comparing the hash values of the records. Additionally, Snowflake’s multi-table insert feature allows for the loading of multiple PIT tables in parallel from a single join query, which can significantly streamline the data loading process and improve performance1.

References =

•Snowflake’s documentation on multi-table inserts1

•Blog post on optimizing Data Vault architecture on Snowflake2

The most cost-effective and administratively efficient solution is to use a combination of native and external tables. Native tables for reporting data ensure performance and governance, while external tables allow for flexibility with frequently changing experimental data. Creating roles with specific grants to datasets aligns with the principle of least privilege, centralizing access control and simplifying user management12.

References

•Snowflake Documentation on Optimizing Cost1.

•Snowflake Documentation on Controlling Cost2.

To ensure that an analyst_user can only access Snowflake from specific IP addresses, the following steps are required:

• Option B: This alters the network policy directly linked to analyst_user. Setting a network policy on the user level is effective and ensures that the specified network restrictions apply directly and exclusively to this user.
• Option D: Before a network policy can be set or altered, the appropriate role with permission to manage network policies must be used. SECURITYADMIN is typically the role that has privileges to create and manage network policies in Snowflake. Creating a network policy that specifies allowed IP addresses ensures that only requests coming from those IPs can access Snowflake under this policy. After creation, this policy can be linked to specific users or roles as needed.

Options A and E mention altering roles or using the wrong role (USERADMIN typically does not manage network security settings), and option C incorrectly attempts to set a network policy directly as an IP address, which is not syntactically or functionally valid.References: Snowflake's security management documentation covering network policies and role-based access controls.

 Partitioning an external table is a technique that improves the performance of queries executed against the table by reducing the amount of data scanned. Partitioning an external table involves creating one or more partition columns that define how the table is logically divided into subsets of data based on the values in those columns. The partition columns can be derived from the file metadata (such as file name, path, size, or modification time) or from the file content (such as a column value or a JSON attribute). Partitioning an external table allows the query optimizer to prune the files that do not match the query predicates, thus avoiding unnecessary data scanning and processing2

The other options are not effective steps for improving the performance of queries executed against an external table:

• Shorten the names of the source files. This option does not have any impact on the query performance, as the file names are not used for query processing. The file names are only used for creating the external table and displaying the query results3
• Convert the source files’ character encoding to UTF-8. This option does not affect the query performance, as Snowflake supports various character encodings for external table files, such as UTF-8, UTF-16, UTF-32, ISO-8859-1, and Windows-1252. Snowflake automatically detects the character encoding of the files and converts them to UTF-8 internally for query processing4
• Use an internal stage instead of an external stage to store the source files. This option is not applicable, as external tables can only reference files stored in external stages, such as Amazon S3, Google Cloud Storage, or Azure Blob Storage. Internal stages are used for loading data into internal tables, not external tables5 References:
• 1: SnowPro Advanced: Architect | Study Guide
• 2: Snowflake Documentation | Partitioning External Tables
• 3: Snowflake Documentation | Creating External Tables
• 4: Snowflake Documentation | Supported File Formats and Compression for Staged Data Files
• 5: Snowflake Documentation | Overview of Stages
• : SnowPro Advanced: Architect | Study Guide
• : Partitioning External Tables
• : Creating External Tables
• : Supported File Formats and Compression for Staged Data Files
• : Overview of Stages

In a scenario where strong legal isolation is required alongside the need for multi-tenancy, the most effective approach is to create separate accounts for each tenant within the Snowflake organization. This approach ensures complete isolation of data, resources, and management, adhering to strict legal and compliance requirements. Role-Based Access Control (RBAC) further enhances security by allowing granular control over who can access what resources within each account. This solution leverages Snowflake’s capabilities for managing multiple accounts under a single organization umbrella, ensuring that each tenant's data and operations are isolated from others.References: Snowflake documentation on multi-tenancy and account management, part of the SnowPro Advanced: Architect learning path.

According to the SnowPro Advanced: Architect documents and learning resources, the best way to enable optimal clustering to enhance performance for different access paths on a given table is to create multiple materialized views with different cluster keys. A materialized view is a pre-computed result set that is derived from a query on one or more base tables. A materialized view can be clustered by specifying a clustering key, which is a subset of columns or expressions that determines how the data in the materialized view is co-located in micro-partitions. By creating multiple materialized views with different cluster keys, an Architect can optimize the performance of queries that use different access paths on the same base table. For example, if a base table has columns A, B, C, and D, and there are queries that filter on A and B, or on C and D, or on A and C, the Architect can create three materialized views, each with a different cluster key: (A, B), (C, D), and (A, C). This way, each query can leverage the optimal clustering of the corresponding materialized view and achieve faster scan efficiency and better compression.

References:

• Snowflake Documentation: Materialized Views
• Snowflake Learning: Materialized Views

https://www.snowflake.com/blog/using-materialized-views-to-solve-multi-clustering-performance-problems/

 A masking policy is a feature of Snowflake that allows masking sensitive data in query results based on the role of the user and the condition of the data. A masking policy can be applied to a column in a table or a view, and it can use another column in the same table or view as a conditional column. A conditional column is a column that determines whether the masking policy is applied or not based on its value1.

In this case, the requirement can be met by using a masking policy on the username column with event_timestamp as a conditional column. The masking policy can use a function that masks the username if the event_timestamp is older than six months based on the current date, and returns the original username otherwise. The masking policy can be applied to the user_events table, and it will also be applied when creating views using the table or cloning the table2.

The other options are not correct because:

• A. Using a masking policy on the username column using an entitlement table with valid dates would require creating another table that stores the valid dates for each username, and joining it with the user_events table in the masking policy function. This would add complexity and overhead to the masking policy, and it would not use the event_timestamp column as the condition for masking.
• B. Using a row level policy on the user_events table using an entitlement table with valid dates would require creating another table that stores the valid dates for each username, and joining it with the user_events table in the row access policy function. This would filter out the rows that have event_timestamp older than six months based on the valid dates, instead of masking the username column. This would not meet the requirement of masking the username, and it would also reduce the visibility of the event data.
• D. Using a secure view on the user_events table using a case statement on the username column would require creating a view that uses a case expression to mask the username column based on the event_timestamp column. This would meet the requirement of masking the username, but it would not be applied when cloning the table. A secure view is a view that prevents the underlying data from being exposed by queries on the view. However, a secure view does not prevent the underlying data from being exposed by cloning the table3.

References:

• 1: Masking Policies | Snowflake Documentation
• 2: Using Conditional Columns in Masking Policies | Snowflake Documentation
• 3: Secure Views | Snowflake Documentation

According to the SnowPro Advanced: Architect documents and learning resources, column masking policies are applied at query time based on the privileges of the user who runs the query. Therefore, if a user has the privilege to see unmasked data in a column, they will see the original data when they query that column. If they load this column data into another column that does not have a masking policy, the unmasked data will be loaded in the new column, and any user who can query the new column will see the unmasked data as well. The masking policy does not affect the underlying data in the column, only the query results.

References:

• Snowflake Documentation: Column Masking
• Snowflake Learning: Column Masking

• Option A (RETURN_FAILED_ONLY) will only load files that previously failed to load. Since file5.csv already exists in the stage with the same name, it will not be considered a new file and will not be loaded.
• Option D (FORCE) will overwrite any existing data in the table. This is not desired as we only want to load the data from file5.csv.
• Option E (NEW_FILES_ONLY) will only load files that have been added to the stage since the last COPY command. This will not work because file5.csv was already in the stage before it was fixed.
• Option F (MERGE) is used to merge data from a stage into an existing table, creating new rows for any data not already present. This is not needed in this case as we simply want to load the data from file5.csv.

Therefore, the architect can use either COPY INTO tablea FROM @%tablea or COPY INTO tablea FROM @%tablea FILES = ('file5.csv') to load only file5.csv from the stage. Both options will load the data from the specified file without overwriting any existing data or requiring additional configuration

• Snowpipe is a service that automates and optimizes the loading of data from external stages into Snowflake tables. Snowpipe uses a queue to ingest files as they become available in the stage. Snowpipe also provides REST endpoints to load data and retrieve load history reports1.
• The loadHistoryScan endpoint returns the history of files that have been ingested by Snowpipe within a specified time range. The endpoint accepts the following parameters2:
• The loadHistoryScan endpoint can be used to keep a log of data load history by calling it periodically with a suitable time range. The best option among the choices is D, which is to call loadHistoryScan every 10 minutes for a 15-minute time range. This option ensures that the endpoint is called frequently enough to capture the latest files that have been ingested, and that the time range is wide enough to avoid missing any files that may have been delayed or retried by Snowpipe. The other options are either too infrequent, too narrow, or use the wrong endpoint3.

References:

• 1: Introduction to Snowpipe | Snowflake Documentation
• 2: loadHistoryScan | Snowflake Documentation
• 3: Monitoring Snowpipe Load History | Snowflake Documentation

According to the Snowflake documentation1 and the web search results2, these two statements are true about how the change of local time due to daylight savings time is handled in Snowflake tasks. A task is a feature that allows scheduling and executing SQL statements or stored procedures in Snowflake. A task can be scheduled using a cron expression that specifies the frequency and time zone of the task execution.

• A task scheduled in a UTC-based schedule will have no issues with the time changes. UTC is a universal time standard that does not observe daylight savings time. Therefore, a task that uses UTC as the time zone will run at the same time throughout the year, regardless of the local time changes1.
• Task schedules can be designed to follow specified or local time zones to accommodate the time changes. Snowflake supports using any valid IANA time zone identifier in the cron expression for a task. This allows the task to run according to the local time of the specified time zone, which may include daylight savings time adjustments. For example, a task that uses Europe/London as the time zone will run one hour earlier or later when the local time switches between GMT and BST12.

References:

• Snowflake Documentation: Scheduling Tasks
• Snowflake Community: Do the timezones used in scheduling tasks in Snowflake adhere to daylight savings?

The Time Travel retention period for T1 will be 30 days, which is the retention period set at the table level. The Time Travel retention period determines how long the historical data is preserved and accessible for an object after it is modified or dropped. The Time Travel retention period can be set at the account level, the database level, the schema level, or the table level. The retention period set at the lowest level of the hierarchy takes precedence over the higher levels. Therefore, the retention period set at the table level overrides the retention periods set at the schema level, the database level, or the account level. When the user drops the database DB1, the table T1 is also dropped, but the historical data is still preserved for 30 days, which is the retention period set at the table level. The user can use the UNDROP command to restore the table T1 within the 30-day period. The other options are incorrect because:

• 10 days is the retention period set at the database level, which is overridden by the table level.
• 20 days is the retention period set at the schema level, which is also overridden by the table level.
• 37 days is not a valid option, as it is not the retention period set at any level.

References:

• Understanding & Using Time Travel
• AT | BEFORE
• Snowflake Time Travel & Fail-safe

 According to the SnowPro Advanced: Architect documents and learning resources, the requirement to allow data sharing between two companies that are not on the same cloud platform is to set up data replication to the region and cloud platform where the consumer resides. Data replication is a feature of Snowflake that enables copying databases across accounts in different regions and cloud platforms. Data replication allows data providers to securely share data with data consumers across different regions and cloud platforms by creating a replica database in the consumer’s account. The replica database is read-only and automatically synchronized with the primary database in the provider’s account. Data replication is useful for scenarios where data sharing is not possible or desirable due to latency, compliance, or security reasons1. The other options are incorrect because they are not required or feasible to allow data sharing between two companies that are not on the same cloud platform. Option A is incorrect because creating a pipeline to write shared data to a cloud storage location in the target cloud provider is not a secure or efficient way of sharing data. It would require additional steps to load the data from the cloud storage to the consumer’s account, and it would not leverage the benefits of Snowflake’s data sharing features. Option B is incorrect because ensuring that all views are persisted is not relevant for data sharing across cloud platforms. Views can be shared across cloud platforms as long as they reference objects in the same database. Persisting views is an option to improve the performance of querying views, but it is not required for data sharing2. Option D is incorrect because Company A and Company B do not need to agree to use a single cloud platform. Data sharing is possible across different cloud platforms using data replication or other methods, such as listings or auto-fulfillment3. References: Replicating Databases Across Multiple Accounts | Snowflake Documentation, Persisting Views | Snowflake Documentation, Sharing Data Across Regions and Cloud Platforms | Snowflake Documentation

 These two features are relevant for modeling using Data Vault on Snowflake. Data Vault is a data modeling approach that organizes data into hubs, links, and satellites. Data Vault is designed to enable high scalability, flexibility, and performance for data integration and analytics. Snowflake is a cloud data platform that supports various data modeling techniques, including Data Vault. Snowflake provides some features that can enhance the Data Vault modeling, such as:

• Snowflake’s support of multi-table inserts into the data model’s Data Vault tables. Multi-table inserts (MTI) are a feature that allows inserting data from a single query into multiple tables in a single DML statement. MTI can improve the performance and efficiency of loading data into Data Vault tables, especially for real-time or near-real-time data integration. MTI can also reduce the complexity and maintenance of the loading code, as well as the data duplication and latency12.
• Scaling up the virtual warehouses will support parallel processing of new source loads. Virtual warehouses are a feature that allows provisioning compute resources on demand for data processing. Virtual warehouses can be scaled up or down by changing the size of the warehouse, which determines the number of servers in the warehouse. Scaling up the virtual warehouses can improve the performance and concurrency of processing new source loads into Data Vault tables, especially for large or complex data sets. Scaling up the virtual warehouses can also leverage the parallelism and distribution of Snowflake’s architecture, which can optimize the data loading and querying34.

References:

• Snowflake Documentation: Multi-table Inserts
• Snowflake Blog: Tips for Optimizing the Data Vault Architecture on Snowflake
• Snowflake Documentation: Virtual Warehouses
• Snowflake Blog: Building a Real-Time Data Vault in Snowflake

 According to the Snowflake documentation, materialized views have some limitations on the query specification that defines them. One of these limitations is that they cannot include nested subqueries, such as subqueries in the FROM clause or scalar subqueries in the SELECT list. Another limitation is that they cannot include ORDER BY clauses, context functions (such as CURRENT_TIME()), or outer joins. However, materialized views can support MIN and MAX aggregates, as well as other aggregate functions, such as SUM, COUNT, and AVG.

References:

• Limitations on Creating Materialized Views | Snowflake Documentation
• Working with Materialized Views | Snowflake Documentation

The best design to store and analyze large amounts of structured and semi-structured data for different business units and departments is to use a centralized Snowflake database for core business data, and use separate databases for departmental or project-specific data. This design allows for scalability, security, and cost efficiency by leveraging Snowflake’s features such as:

• Database cloning: Cloning a database creates a zero-copy clone that shares the same data files as the original database, but can be modified independently. This reduces storage costs and enables fast and consistent data replication for different purposes.
• Database sharing: Sharing a database allows granting secure and governed access to a subset of data in a database to other Snowflake accounts or consumers. This enables data collaboration and monetization across different business units or external partners.
• Warehouse scaling: Scaling a warehouse allows adjusting the size and concurrency of a warehouse to match the performance and cost requirements of different workloads. This enables optimal resource utilization and flexibility for different data analysis needs. References: Snowflake Documentation: Database Cloning, Snowflake Documentation: Database Sharing, [Snowflake Documentation: Warehouse Scaling]

 According to Snowflake recommended best practice, the requirements of the large manufacturing company should be met by deploying a Private Data Exchange in combination with data shares for the European accounts. A Private Data Exchange is a feature of the Snowflake Data Cloud platform that enables secure and governed sharing of data between organizations. It allows Snowflake customers to create their own data hub and invite other parts of their organization or external partners to access and contribute data sets. A Private Data Exchange provides centralized management, granular access control, and data usage metrics for the data shared in the exchange1. A data share is a secure and direct way of sharing data between Snowflake accounts without having to copy or move the data. A data share allows the data provider to grant privileges on selected objects in their account to one or more data consumers in other accounts2. By using a Private Data Exchange in combination with data shares, the company can achieve the following benefits:

• The business divisions can decide what data to share and publish it to the Private Data Exchange, where it can be discovered and accessed by other members of the exchange. This reduces the effort and complexity of managing multiple data sharing relationships and configurations.
• The company can leverage the existing Snowflake accounts in the same cloud deployments to create the Private Data Exchange and invite the members to join. This minimizes the migration and setup costs and leverages the existing Snowflake features and security.
• The company can use data shares to share data with the European accounts that are in different regions or cloud platforms. This allows the company to comply with the regional and regulatory requirements for data sovereignty and privacy, while still enabling data collaboration across the organization.
• The company can use the Snowflake Data Cloud platform to perform data analysis and transformation on the shared data, as well as integrate with other data sources and applications. This enables the company to optimize its supply chain and increase its purchasing leverage with multiple vendors.

This command will fail because while the user has USAGE permission on db2 and schema2 through Role B, and can create a view in schema2, they do not have SELECT permission on db1.schemal.table1 with Role B. Since Role A, which has SELECT permission on db1.schemal.table1, is not the currently active role when the view v2 is being created in db2.schema2, the user does not have the necessary permissions to read from db1.schemal.table1 to create the view. Snowflake’s security model requires that the active role have all necessary permissions to execute the command.

A. Snowflake's transactions can indeed include DDL (Data Definition Language), DML (Data Manipulation Language), and query statements. When executed within a transaction block, they all contribute to the atomicity of the transaction—either all of them commit together or none at all.C. Snowflake supports explicit transaction control through the use of the BEGIN TRANSACTION (or simply BEGIN) and COMMIT statements. Alternatively, the BEGIN WORK and COMMIT WORK syntax is also supported, which is a standard SQL syntax for initiating and ending transactions, respectively.Note: The END TRANSACTION statement is not used in Snowflake to end a transaction; the correct statement is COMMIT or COMMIT WORK.

• The architecture in the image shows a Snowflake data platform with two databases, DB1 and DB2, and two schemas, SH1 and SH2. DB1 contains a table TBL1 and a stage STAGE1. DB2 contains a table TBL2. The image also shows a snippet of code written in SQL language that copies data from STAGE1 to TBL2 using a file format FF PIPE 1.
• To copy data from DB1 to TBL2, there are two possible options among the choices given:

SQLAI-generated code. Review and use carefully. More info on FAQ.

use database DB2;

use schema SH2;

create stage EXT_STAGE1

url = @DB1.SH1.STAGE1;

• uk.co.certification.simulator.questionpool.PList@6bee35f0

SQLAI-generated code. Review and use carefully. More info on FAQ.

copy into TBL2

from @EXT_STAGE1

file format = (format name = DB1.SH1.FF PIPE 1);

• uk.co.certification.simulator.questionpool.PList@6bee3630

SQLAI-generated code. Review and use carefully. More info on FAQ.

use database DB2;

use schema SH2;

insert into TBL2

select * from DB1.SH1.TBL1;

• The other options are not valid because:

References:

• 1: CREATE STAGE | Snowflake Documentation
• 2: COPY INTO table | Snowflake Documentation
• 3: Cross-database Queries | Snowflake Documentation

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 the VALUE 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

 The SIMULATED_DATA_SHARING_CONSUMER session parameter allows a data provider to simulate the data access of a consumer account without creating a reader account or logging in with the consumer credentials. This parameter can be used to validate the data accessibility by the consumers in a data share, especially when using secure views or secure UDFs that filter data based on the current account or role. By setting this parameter to the name of a consumer account, the data provider can see the same data as the consumer would see when querying the shared database. This is a convenient and efficient way to test the data sharing functionality and ensure that only the intended data is visible to the consumers.

References:

• Using the SIMULATED_DATA_SHARING_CONSUMER Session Parameter
• SnowPro Advanced: Architect Exam Study Guide

• 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

Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported. This could be caused by the following factors:

• The order of the keys in the JSON was changed. Snowflake stores semi-structured data internally in a column-like structure for the most common elements, and the remainder in a leftovers-like column. The order of the keys in the JSON affects how Snowflake determines the common elements and how it optimizes the query performance. If the order of the keys in the JSON was changed, Snowflake might have to re-parse the data and re-organize the internal storage, which could result in slower query performance.
• There were variations in string lengths for the JSON values in the recent data imports. Non-native values, such as dates and timestamps, are stored as strings when loaded into a VARIANT column. Operations on these values could be slower and also consume more space than when stored in a relational column with the corresponding data type. If there were variations in string lengths for the JSON values in the recent data imports, Snowflake might have to allocate more space and perform more conversions, which could also result in slower query performance.

The other options are not valid causes for poor query performance:

• There were JSON nulls in the recent data imports. Snowflake supports two types of null values in semi-structured data: SQL NULL and JSON null. SQL NULL means the value is missing or unknown, while JSON null means the value is explicitly set to null. Snowflake can distinguish between these two types of null values and handle them accordingly. Having JSON nulls in the recent data imports should not affect the query performance significantly.
• The recent data imports contained fewer fields than usual. Snowflake can handle semi-structured data with varying schemas and fields. Having fewer fields than usual in the recent data imports should not affect the query performance significantly, as Snowflake can still optimize the data ingestion and query execution based on the existing fields.

References:

• Considerations for Semi-structured Data Stored in VARIANT
• Snowflake Architect Training
• Snowflake query performance on unique element in variant column
• Snowflake variant performance