Snowflake ARA-C01 SnowPro Advanced: Architect Certification Exam Exam Practice Test

The requirements state that the data analysts need to be able to create and modify database objects and load data, but should not be able to manage access for users outside of their role.

Option C: By making each schema within the database a managed access schema and having them owned by SYSADMIN, the ability to grant privileges on the schema's objects is strictly controlled. Managed access schemas limit the granting of privileges to the role specified as the owner of the schema, in this case, SYSADMIN. The ANALYST_ROLE can be granted the privileges necessary to create and modify objects within these schemas, satisfying the requirement for the analysts to perform their tasks without being able to extend access beyond their role.

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.

 A reader account is a type of Snowflake account that allows external users to access data shared by a provider account without being a Snowflake customer. A reader account can be created and managed by the provider account, and can use the Snowflake web interface or JDBC/ODBC drivers to query the shared data. A reader account is billed to the provider account based on the credits consumed by the queries1. A secure view is a type of view that applies row-level security filters to the underlying tables, and masks the data that is not accessible to the user. A secure view can be shared with a reader account to provide granular and governed access to the data2. In this scenario, creating a reader account for the partner and sharing the data sets as secure views would be the most cost-effective and secure design, while using the required Snowflake features, because:

• It would avoid the data transfer and storage costs of using an S3 bucket as a destination, and the potential security risks of exposing the data to unauthorized access or modification.
• It would avoid the complexity and overhead of publishing the data sets on the Snowflake Marketplace, and the potential loss of control over the data ownership and pricing.
• It would avoid the need to create a database user for the partner and grant them access to the required data sets, which would require the partner to have a Snowflake account and consume the provider’s resources.

References:

• Reader Accounts
• Secure Views

• A stage is a named location in Snowflake that can store files for data loading and unloading. A stage can be internal or external, depending on where the files are stored.
• The query in the question uses the LIST function to list the files in a stage named FILEROWS. The function returns a table with various columns, including FILE_ROW_NUMBER, which is the line number of the file in the stage.
• Therefore, the query can be interpreted as listing the files in a stage named FILEROWS and showing the line number of each file in the stage.

References:

• : Stages
• : LIST Function

When sharing unstructured data within Snowflake, using a scoped URL is recommended. Scoped URLs provide temporary access to staged files without granting privileges to the stage itself, enhancing security. The URL expires when the persisted query result period ends, which is currently set to 24 hours. This approach is suitable for sharing unstructured data over secure views within Snowflake’s data sharing framework.

References: The answer is based on Snowflake’s official documentation regarding the sharing of unstructured data and the use of scoped URLs1.

The best way to meet the requirement of consolidating company B’s sales data into company A’s Snowflake account is to use cross-region data replication within Snowflake. This feature allows data providers to securely share data with data consumers across different regions and cloud platforms. By replicating the sales data from company B’s account in Azure West Europe region to company A’s account in AWS us-east-1 region, the data will be synchronized and available for consumption. To enable data replication, the accounts must be linked and replication must be enabled by a user with the ORGADMIN role. Then, a replication group must be created and the sales database must be added to the group. Finally, a direct share must be configured from company B’s account to company A’s account to grant access to the replicated data. This option is more efficient and secure than exporting and importing data using CSV files or migrating the entire Snowflake deployment to another region or cloud platform. It also does not require building a custom data pipeline using external tools.

References:

• Sharing data securely across regions and cloud platforms
• Introduction to replication and failover
• Replication considerations
• Replicating account objects

For ingesting a large volume of CSV data into Snowflake using Snowpipe, especially for a substantial amount like 10 TB, the on error = SKIP_FILE option in the COPY INTO command can be highly effective. This approach allows Snowpipe to skip over files that cause errors during the ingestion process, thereby not halting or significantly slowing down the overall data load. It helps in maintaining performance and cost-effectiveness by avoiding the reprocessing of problematic files and continuing with the ingestion of other data.

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

 The most efficient solution is to ask the partner to create a share and add the company’s account (Option A). This way, the company can access the live data from the partner without any data movement or manual intervention. Snowflake’s secure data sharing feature allows data providers to share selected objects in a database with other Snowflake accounts. The shared data is read-only and does not incur any storage or compute costs for the data consumers. The data consumers can query the shared data directly or create local copies of the shared objects in their own databases. Option B is not efficient because it involves using the data lake export feature, which is intended for exporting data from Snowflake to an external data lake, not for importing data from another Snowflake account. The data lake export feature also requires the data provider to create an external stage on cloud storage and use the COPY INTO command to export the data into parquet files. The data consumer then needs to create an external table or a file format to load the data from the cloud storage into Snowflake. This process can be complex and costly, especially if the data changes frequently. Option C is not efficient because it does not solve the problem of manual data ingestion and adaptation. Keeping the current structure of daily JSON extracts on an FTP server and requesting the partner to stop changing files, instead only appending new files, does not improve the efficiency or reliability of the data ingestion process. The company still needs to upload the data to Snowflake manually and deal with any schema changes or data quality issues. Option D is not efficient because it requires the partner to set up a Snowflake reader account and use that account to get the data for ingestion. A reader account is a special type of account that can only consume data from the provider account that created it. It is intended for data consumers who are not Snowflake customers and do not have a licensing agreement with Snowflake. A reader account is not suitable for data ingestion from another Snowflake account, as it does not allow uploading, modifying, or unloading data. The company would need to use external tools or interfaces to access the data from the reader account and load it into their own account, which can be slow and expensive. References: The answer can be verified from Snowflake’s official documentation on secure data sharing, data lake export, and reader accounts available on their website. Here are some relevant links:

• Introduction to Secure Data Sharing | Snowflake Documentation
• Data Lake Export Public Preview Is Now Available on Snowflake | Snowflake Blog
• Managing Reader Accounts | Snowflake Documentation

 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

• A transient table is a type of table in Snowflake that does not have a Fail-safe period and can have a Time Travel retention period of either 0 or 1 day. Transient tables are suitable for temporary or intermediate data that can be easily reproduced or replicated1.
• A temporary table is a type of table in Snowflake that is automatically dropped when the session ends or the current user logs out. Temporary tables do not incur any storage costs, but they are not visible to other users or sessions2.
• A permanent table is a type of table in Snowflake that has a Fail-safe period and a Time Travel retention period of up to 90 days. Permanent tables are suitable for persistent and durable data that needs to be protected from accidental or malicious deletion3.
• In this case, the use case requires loading some data that can be visualized through Tableau. The data is updated every day and the old data is no longer required. Therefore, the best type of table to use in this case to optimize cost is a transient table, because it does not incur any Fail-safe costs and it can have a short Time Travel retention period of 0 or 1 day. This way, the data can be loaded and queried by Tableau, and then deleted or overwritten without incurring any unnecessary storage costs.

References: : Transient Tables : Temporary Tables : Understanding & Using Time Travel

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

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.

Effective pruning in Snowflake relies on the organization of data within micro-partitions. By using an ORDER BY clause with clustering keys when loading data into the reporting tables, Snowflake can better organize the data within micro-partitions. This organization allows Snowflake to skip over irrelevant micro-partitions during a query, thus improving query performance and reducing the amount of data scanned12.

References =

•Snowflake Documentation on micro-partitions and data clustering2

•Community article on recognizing unsatisfactory pruning and improving it1

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.

Option B is the best design to meet the requirements because it uses Snowpipe to ingest the data continuously and efficiently as new records arrive in the object storage, leveraging event notifications. Snowpipe is a service that automates the loading of data from external sources into Snowflake tables1. It also uses streams and tasks to orchestrate transformations on the ingested data. Streams are objects that store the change history of a table, and tasks are objects that execute SQL statements on a schedule or when triggered by another task2. Option B also uses an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. An external function is a user-defined function that calls an external API, such as Amazon Comprehend, to perform computations that are not natively supported by Snowflake3. Finally, option B uses the Snowflake Marketplace to make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions. The Snowflake Marketplace is a platform that enables data providers to list and share their data sets with data consumers, regardless of the cloud platform or region they use4.

Option A is not the best design because it uses copy into to ingest the data, which is not as efficient and continuous as Snowpipe. Copy into is a SQL command that loads data from files into a table in a single transaction. It also exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.

Option C is not the best design because it uses Amazon EMR and PySpark to ingest and transform the data, which also increases the operational complexity and maintenance of the infrastructure. Amazon EMR is a cloud service that provides a managed Hadoop framework to process and analyze large-scale data sets. PySpark is a Python API for Spark, a distributed computing framework that can run on Hadoop. Option C also develops a python program to do model inference by leveraging the Amazon Comprehend text analysis API, which increases the development effort.

Option D is not the best design because it is identical to option A, except for the ingestion method. It still exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.

References: 1: Snowpipe Overview 2: Using Streams and Tasks to Automate Data Pipelines 3: External Functions Overview 4: Snowflake Data Marketplace Overview : [Loading Data Using COPY INTO] : [What is Amazon EMR?] : [PySpark Overview]

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 it will 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 pipe is a Snowflake object that continuously loads data from files in a stage (internal or external) into a table. A pipe can be configured to use auto-ingest, which means that Snowflake automatically detects new or modified files in the stage and loads them into the table without any manual intervention1.
• A pipe is the most cost-effective way to bring large numbers of small JSON files into a Snowflake table, because it minimizes the number of COPY commands executed and the number of micro-partitions created. A pipe can use file aggregation, which means that it can combine multiple small files into a single larger file before loading them into the table. This reduces the load time and the storage cost of the data2.
• An external table is a Snowflake object that references data files stored in an external location, such as Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage. An external table does not store the data in Snowflake, but only provides a view of the data for querying. An external table is not a cost-effective way to bring data into a Snowflake table, because it does not support file aggregation, and it requires additional network bandwidth and compute resources to query the external data3.
• A stream is a Snowflake object that records the history of changes (inserts, updates, and deletes) made to a table. A stream can be used to consume the changes from a table and apply them to another table or a task. A stream is not a way to bring data into a Snowflake table, but a way to process the data after it is loaded into a table4.
• A copy command is a Snowflake command that loads data from files in a stage into a table. A copy command can be executed manually or scheduled using a task. A copy command is not a cost-effective way to bring large numbers of small JSON files into a Snowflake table, because it does not support file aggregation, and it may create many micro-partitions that increase the storage cost of the data5.

References: : Pipes : Loading Data Using Snowpipe : External Tables : Streams : COPY INTO

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 most efficient way to load data from the vendor into Snowflake is to create an external stage on Google Cloud Storage and use the external table to load the data into Snowflake (Option B). This way, you can avoid copying or moving the data across different cloud platforms, which can incur additional costs and latency. You can also leverage the external table feature to query the data directly from Google Cloud Storage without loading it into Snowflake tables, which can save storage space and improve performance. Option A is not efficient because it requires the vendor to create a Snowflake account and a data share, which can be complicated and costly. Option C is not efficient because it involves copying the data from Google Cloud Storage to Azure Blob storage using external tools, which can be slow and expensive. Option D is not efficient because it requires creating a Snowflake account in the Google Cloud Platform (GCP), ingesting data into this account, and using data replication to move the data from GCP to Azure, which can be complex and time-consuming. References: The answer can be verified from Snowflake’s official documentation on external stages and external tables available on their website. Here are some relevant links:

• Using External Stages | Snowflake Documentation
• Using External Tables | Snowflake Documentation
• Loading Data from a Stage | 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

 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.

In Snowflake, a stream records data manipulation language (DML) changes to its base table since the stream was created or last consumed. STREAM_1 will show all changes including the TRUNCATE operation, while STREAM_2, being APPEND_ONLY, will not show deletions like TRUNCATE. Therefore, STREAM_1 will count the three inserts, the TRUNCATE (counted as a single operation), and the subsequent two inserts before the delete, totaling 4. STREAM_2 will only count the three initial inserts and the two after the TRUNCATE, totaling 3, as it does not count the TRUNCATE or the delete operation.

References: The explanation is based on the Snowflake documentation on streams, which details how streams track changes and the difference between standard and APPEND_ONLY streams12.

The most performant design for processing changed records, considering the need to both update records in the f_orders fact table and load changes into the order_repairs table, is to use one stream and two tasks. The stream will monitor changes in the orders table, capturing both inserts and updates. The first task would apply these changes to the f_orders fact table, ensuring all dimensions are accurately represented. The second task would use the same stream to insert relevant changes into the order_repairs table, which is critical for the Accounting department's monthly review. This method ensures efficient processing by minimizing the overhead of managing multiple streams and synchronizing between them, while also allowing specific tasks to optimize for their target operations.References: Snowflake's documentation on streams and tasks for handling data changes efficiently.

According to the Snowflake documentation1, the Business Critical edition offers the following features that are relevant to the question:

• Support for Multi-Factor Authentication (MFA): This is a standard feature available in all Snowflake editions1.
• A minimum of 2 months of Time Travel availability: This is an enterprise feature that allows users to access historical data for up to 90 days1.
• Database replication in between different regions: This is an enterprise feature that enables users to replicate databases across different regions or cloud platforms1.
• Native support for JDBC and ODBC: This is a standard feature available in all Snowflake editions1.
• Customer-managed encryption keys using Tri-Secret Secure: This is a business critical feature that provides enhanced security and data protection by allowing customers to manage their own encryption keys1.
• Support for Payment Card Industry Data Security Standards (PCI DSS): This is a business critical feature that ensures compliance with PCI DSS regulations for handling sensitive cardholder data1.

Therefore, the minimum Snowflake edition that should be selected during account creation to provide all the listed services is the Business Critical edition.

References:

• Snowflake Editions | Snowflake Documentation

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.

Zero-copy cloning is a feature in Snowflake that allows for the creation of a clone of a database, schema, or table without duplicating any data, which is cost-effective as it saves on storage costs. Transient tables are temporary and do not incur storage costs for the time they are not accessed, making them a cost-effective option for development, test, and pre-production environments that do not require the durability of permanent tables123.

References

•Snowflake Documentation on Zero-Copy Cloning3.

•Articles discussing the cost-effectiveness and performance benefits of zero-copy cloning12.

 Snowflake context functions are functions that return information about the current session, user, role, warehouse, database, schema, or object. They can be used to help determine whether a user is authorized to see data that has column-level security enforced by setting masking policy conditions based on the context functions. The following context functions are relevant for column-level security:

• current_role: This function returns the name of the role in use for the current session. It can be used to set masking policy conditions that target the current session and are not affected by the execution context of the SQL statement. For example, a masking policy condition using current_role can allow or deny access to a column based on the role that the user activated in the session.
• invoker_role: This function returns the name of the executing role in a SQL statement. It can be used to set masking policy conditions that target the executing role and are affected by the execution context of the SQL statement. For example, a masking policy condition using invoker_role can allow or deny access to a column based on the role that the user specified in the SQL statement, such as using the AS ROLE clause or a stored procedure.
• is_role_in_session: This function returns TRUE if the user’s current role in the session (i.e. the role returned by current_role) inherits the privileges of the specified role. It can be used to set masking policy conditions that involve role hierarchy and privilege inheritance. For example, a masking policy condition using is_role_in_session can allow or deny access to a column based on whether the user’s current role is a lower privilege role in the specified role hierarchy.

The other options are not valid ways to use the Snowflake context functions for column-level security:

• Set masking policy conditions using is_role_in_session targeting the role in use for the current account. This option is incorrect because is_role_in_session does not target the role in use for the current account, but rather the role in use for the current session. Also, the current account is not a role, but rather a logical entity that contains users, roles, warehouses, databases, and other objects.
• Determine if there are ownership privileges on the masking policy that would allow the use of any function. This option is incorrect because ownership privileges on the masking policy do not affect the use of any function, but rather the ability to create, alter, or drop the masking policy. Also, this is not a way to use the Snowflake context functions, but rather a way to check the privileges on the masking policy object.
• Assign the accountadmin role to the user who is executing the object. This option is incorrect because assigning the accountadmin role to the user who is executing the object does not involve using the Snowflake context functions, but rather granting the highest-level role to the user. Also, this is not a recommended practice for column-level security, as it would give the user full access to all objects and data in the account, which could compromise data security and governance.

References:

• Context Functions
• Advanced Column-level Security topics
• Snowflake Data Governance: Column Level Security Overview
• Data Security Snowflake Part 2 - Column Level Security

The best way to address the performance issues and time-outs faced by the Store Manager team is to configure a dedicated virtual warehouse for them and make it multi-clustered. This will allow them to run their reports independently from other workloads and scale up or down the compute resources as needed. A dedicated virtual warehouse will also enable them to apply specific security and access policies for their data. A multi-clustered virtual warehouse will provide high availability and concurrency for their queries and avoid queuing or throttling.

Using a Business Intelligence tool for in-memory computation may improve performance, but it will not solve the underlying issue of insufficient compute resources in the shared virtual warehouse. It will also introduce additional costs and complexity for the data architecture.

Configuring the virtual warehouse to size 4-XL may increase the performance, but it will also increase the cost and may not be optimal for the workload. It will also not address the concurrency and availability issues that may arise from sharing the virtual warehouse with other workloads.

Advising the Store Manager team to defer report execution to off-business hours may reduce the load on the shared virtual warehouse, but it will also reduce the timeliness and usefulness of the reports for the business. It will also not guarantee that the performance issues and time-outs will not occur at other times.

References:

• Snowflake Architect Training
• Snowflake SnowPro Advanced Architect Certification - Preparation Guide
• SnowPro Advanced: Architect Exam Study Guide

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

This is the correct command to create a clone of the table Data as it was 5 minutes ago using cloning and Time Travel. Cloning is a feature that allows creating a copy of a database, schema, table, or view without duplicating the data or metadata. Time Travel is a feature that enables accessing historical data (i.e. data that has been changed or deleted) at any point within a defined period. To create a clone of a table at a point in time in the past, the syntax is:

CREATE TABLE CLONE AT (OFFSET => );

The OFFSET parameter specifies the time difference in seconds from the present time. A negative value indicates a point in the past. For example, -60*5 means 5 minutes ago. Alternatively, the TIMESTAMP parameter can be used to specify an exact timestamp in the past. The clone will contain the data as it existed in the source table at the specified point in time12.

References:

• Snowflake Documentation: Cloning Objects
• Snowflake Documentation: Cloning Objects at a Point in Time in the Past

• 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

To change the edition of a Snowflake account, an organization administrator (ORGADMIN) cannot directly alter the account settings through SQL commands or the Snowflake interface. The proper procedure is to contact Snowflake Support to request an edition change for the account. This ensures that the change is managed correctly and aligns with Snowflake’s operational protocols.

References: This process is outlined in the Snowflake documentation, which specifies that changes to an account’s edition should be facilitated through Snowflake Support1.

Cloning a table with a masking policy can cause the cloning operation to fail because the masking policy is not automatically cloned with the table. This is due to the fact that the masking policy is considered a separate object with its own set of privileges1.

References

Snowflake Documentation on Cloning Considerations1.

By default, Snowflake does not allow sharing data from a Business Critical edition to a non-Business Critical edition. This is because Business Critical edition provides enhanced security and data protection features that are not available in lower editions. However, this restriction can be overridden by setting the share_restriction parameter on the shared object (database, schema, or table) to false. This parameter allows the data provider to explicitly allow sharing data with lower edition accounts. Note that this parameter can only be set by the data provider, not the data consumer. Also, setting this parameter to false may reduce the level of security and data protection for the shared data.

References:

• Enable Data Share:Business Critical Account to Lower Edition
• Sharing Is Not Allowed From An Account on BUSINESS CRITICAL Edition to an Account On A Lower Edition
• SQL Execution Error: Sharing is Not Allowed from an Account on BUSINESS CRITICAL Edition to an Account on a Lower Edition
• Snowflake Editions | Snowflake Documentation

To improve the performance of queries on semi-structured data, such as JSON stored in a VARIANT column, Snowflake’s search optimization service can be utilized. By adding search optimization specifically for the longitude and latitude fields within the VARIANT column, the system can perform point lookups and substring queries more efficiently. This will allow for faster retrieval of weather statistics, which is critical for the drivers to receive timely updates.

References: The solution is supported by Snowflake documentation that details how search optimization can enhance query performance for semi-structured data1.

The object type level at which the APPLY MASKING POLICY, APPLY ROW ACCESS POLICY and APPLY SESSION POLICY privileges can be granted is global. These are account-level privileges that control who can apply or unset these policies on objects such as columns, tables, views, accounts, or users. These privileges are granted to the ACCOUNTADMIN role by default, and can be granted to other roles as needed. The other options are incorrect because they are not the object type level at which these privileges can be granted. Database, schema, and table are lower-level object types that do not support these privileges. References: Access Control Privileges | Snowflake Documentation, Using Dynamic Data Masking | Snowflake Documentation, Using Row Access Policies | Snowflake Documentation, Using Session Policies | Snowflake Documentation

According to the Snowflake documentation, these two system functions are provided by Snowflake to monitor clustering information within a table. A system function is a type of function that allows executing actions or returning information about the system. A clustering key is a feature that allows organizing data across micro-partitions based on one or more columns in the table. Clustering can improve query performance by reducing the number of files to scan.

• SYSTEM$CLUSTERING_INFORMATION is a system function that returns clustering information, including average clustering depth, for a table based on one or more columns in the table. The function takes a table name and an optional column name or expression as arguments, and returns a JSON string with the clustering information. The clustering information includes the cluster by keys, the total partition count, the total constant partition count, the average overlaps, and the average depth1.
• SYSTEM$CLUSTERING_DEPTH is a system function that returns the clustering depth for a table based on one or more columns in the table. The function takes a table name and an optional column name or expression as arguments, and returns an integer value with the clustering depth. The clustering depth is the maximum number of overlapping micro-partitions for any micro-partition in the table. A lower clustering depth indicates a better clustering2.

References:

• SYSTEM$CLUSTERING_INFORMATION | Snowflake Documentation
• SYSTEM$CLUSTERING_DEPTH | Snowflake Documentation

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

For the configuration of data unloading in Snowflake, the valid option among the provided choices is "C." This is because Snowflake supports unloading data into Google Cloud Storage using the COPY INTO command with specific configurations. The configurations listed in option C, such as Parquet file format with UTF-8 encoding and gzip compression, are all supported by Snowflake. Notably, Parquet is a columnar storage file format, which is optimal for high-performance data processing tasks in Snowflake. The UTF-8 file encoding and gzip compression are both standard and widely used settings that are compatible with Snowflake’s capabilities for data unloading to cloud storage platforms.References:

• Snowflake Documentation on COPY INTO command
• Snowflake Documentation on Supported File Formats
• Snowflake Documentation on Compression and Encoding Options

The correct way to securely share data with a vendor using a Snowflake account on a different cloud platform and region is to create a share, add objects to the share, and add a consumer account to the share for the vendor to access. This way, the company can control what data is shared, who can access it, and how long the share is valid. The vendor can then query the shared data without copying or moving it to their own account. The other options are either incorrect or inefficient, as they involve creating unnecessary reader accounts, users, roles, or database replication.

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

The SHOW GRANTS command in Snowflake can be used to list all the access control privileges that have been explicitly granted to roles, users, and shares. The syntax and the output of the command vary depending on the object type and the grantee type specified in the command1. In this question, the two commands have the following meanings:

• Command 1: show grants to user user_01; This command lists all the roles granted to the user user_01. The output includes the role name, the grantee name, and the granted by role name for each grant. This command is equivalent to show grants to user current_user if user_01 is the current user1.
• Command 2: show grants on user user_01; This command lists all the privileges that have been granted on the user object user_01. The output includes the privilege name, the grantee name, and the granted by role name for each grant. This command shows which role owns the user object user_01, as the owner role has the privilege to modify or drop the user object2.

Therefore, the correct inference is that command 1 defines all the grants which are given to user_01, and command 2 defines which role owns user_01.

References:

• SHOW GRANTS
• Understanding Access Control in Snowflake

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