ARA-C01 SnowPro Advanced: Architect Certification Exam Questions and Answers

In a managed access schema, the privilege management is centralized with the schema owner, who has the authority to grant object privileges within the schema. Additionally, the SECURITYADMIN role has the capability to manage object grants globally, which includes within managed access schemas. Other roles, such as SYSADMIN or database owners, do not inherently have this privilege unless explicitly granted.

References: The verified answers are based on Snowflake’s official documentation, which outlines the roles and privileges associated with managed access schemas12.

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 accommodate the diverse needs of different teams and use cases within a company, a flexible and multi-faceted approach to data modeling is required.

Option B: By creating a raw database for landing and persisting raw data, you ensure that the Data Science team has access to unprocessed data for machine learning model development. This aligns with the best practices of having a staging area or raw data zone in a modern data architecture where raw data is ingested before being transformed or processed for different use cases.

Option C: Having profile-specific databases means creating targeted databases that are designed to meet the specific requirements of each user profile or team within the company. For the Finance and Vendor Management teams, the data can be structured and optimized for reporting and visualization. For the Sales team, the database can include engineered and protected data that is suitable for data monetization efforts. This strategy not only aligns data with usage patterns but also helps in managing data access and security policies effectively.

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

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

 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]

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

The correct answer is option B. This query is designed to assess the need for a multi-cluster warehouse by examining the queuing time (AVG_QUEUED_LOAD) on different days and virtual warehouses. When the AVG_QUEUED_LOAD is greater than zero, it suggests that queries are waiting for resources, which can be an indicator that performance might be improved by using a multi-cluster warehouse to handle the workload more efficiently. By grouping by date and warehouse name and filtering on the sum of the average queued load being greater than zero, the query identifies specific days and warehouses where the workload exceeded the available compute resources. This information is valuable when considering scaling out warehouses to multi-cluster configurations for improved performance.

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

This approach is the least complex because it uses Snowflake’s built-in replication feature to copy the data and database objects from the Production account to the QA account. Replication is a fast and efficient way to synchronize data across accounts, regions, and cloud platforms. It also preserves the privileges and metadata of the replicated objects. By creating clones of the replica databases, the QA account can run tests on the cloned data without affecting the original data. Clones are also zero-copy, meaning they do not consume any additional storage space unless the data is modified. This approach does not require any external stages, tasks, Snowpipe, or external functions, which can add complexity and overhead to the data transfer process.

References:

Introduction to Replication and Failover

Replicating Databases Across Multiple Accounts

Cloning Considerations

 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, when a user activates a secondary role during a session, certain privileges associated with DDL (Data Definition Language) operations are restricted. The CREATE statement, which falls under DDL operations, cannot be executed using a secondary role. This limitation is designed to enforce role-based access control and ensure that schema modifications are managed carefully, typically reserved for primary roles that have explicit permissions to modify database structures.References: Snowflake's security and access control documentation specifying the limitations and capabilities of primary versus secondary roles in session management.

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

In Snowflake, the characteristics of result set caches include persistence of data results for 24 hours (B), each use of persisted results resets the 24-hour retention period (C), and result set caches are not shared between different warehouses (F). The result set cache is specifically designed to avoid repeated execution of the same query within this timeframe, reducing computational overhead and speeding up query responses. These caches do not contribute to storage costs, and their retention period cannot be extended beyond the default duration nor up to 31 days, as might be misconstrued.References: Snowflake Documentation on Result Set Caching.

When a new table (table_6) is added to a schema in the provider's account that is part of a data share, the consumer will not automatically see the new table. The consumer will only be able to access the new table once the appropriate privileges are granted by the provider. The correct process, as outlined in option D, involves using the provider’s ACCOUNTADMIN role to grant USAGE privileges on the database and schema, followed by SELECT privileges on the new table, specifically to the share that includes the consumer's database. This ensures that the consumer account can access the new table under the established data sharing setup.References:

Snowflake Documentation on Managing Access Control

Snowflake Documentation on Data Sharing

Zero-copy cloning is a feature that allows creating a clone of a table, schema, or database without physically copying the data. Zero-copy cloning is suitable for scenarios where the cloned object needs to have the same data and metadata as the original object, and where the cloned object does not need to be modified or updated frequently. Zero-copy cloning is also suitable for scenarios where the cloned object needs to be shared within the same Snowflake account or across different accounts in the same cloud region2

However, zero-copy cloning is not suitable for scenarios where the cloned object needs to have different data or metadata than the original object, or where the cloned object needs to be modified or updated frequently. Zero-copy cloning is also not suitable for scenarios where the cloned object needs to be shared across different accounts in different cloud regions. In these scenarios, copying of data would be required, either by using the COPY INTO command or by using data sharing with secure views3

The following are examples of development and testing scenarios where copying of data would be required, and zero-copy cloning would not be suitable:

Developers create their own datasets to work against transformed versions of the live data. This scenario requires copying of data because the developers need to modify the data or metadata of the cloned object to perform transformations, such as adding, deleting, or updating columns, rows, or values. Zero-copy cloning would not be suitable because it would create a read-only clone that shares the same data and metadata as the original object, and any changes made to the clone would affect the original object as well4

Data is in a production Snowflake account that needs to be provided to Developers in a separate development/testing Snowflake account in the same cloud region. This scenario requires copying of data because the data needs to be shared across different accounts in the same cloud region. Zero-copy cloning would not be suitable because it would create a clone within the same account as the original object, and it would not allow sharing the clone with another account. To share data across different accounts in the same cloud region, data sharing with secure views or COPY INTO command can be used5

The following are examples of development and testing scenarios where zero-copy cloning would be suitable, and copying of data would not be required:

Production and development run in different databases in the same account, and Developers need to see production-like data but with specific columns masked. This scenario can use zero-copy cloning because the data needs to be shared within the same account, and the cloned object does not need to have different data or metadata than the original object. Zero-copy cloning can create a clone of the production database in the development database, and the clone can have the same data and metadata as the original database. To mask specific columns, secure views can be created on top of the clone, and the developers can access the secure views instead of the clone directly6

Developers create their own copies of a standard test database previously created for them in the development account, for their initial development and unit testing. This scenario can use zero-copy cloning because the data needs to be shared within the same account, and the cloned object does not need to have different data or metadata than the original object. Zero-copy cloning can create a clone of the standard test database for each developer, and the clone can have the same data and metadata as the original database. The developers can use the clone for their initial development and unit testing, and any changes made to the clone would not affect the original database or other clones7

The release process requires pre-production testing of changes with data of production scale and complexity. For security reasons, pre-production also runs in the production account. This scenario can use zero-copy cloning because the data needs to be shared within the same account, and the cloned object does not need to have different data or metadata than the original object. Zero-copy cloning can create a clone of the production database in the pre-production database, and the clone can have the same data and metadata as the original database. The pre-production testing can use the clone to test the changes with data of production scale and complexity, and any changes made to the clone would not affect the original database or the production environment8 References:

1: SnowPro Advanced: Architect | Study Guide 9

2: Snowflake Documentation | Cloning Overview

3: Snowflake Documentation | Loading Data Using COPY into a Table

4: Snowflake Documentation | Transforming Data During a Load

5: Snowflake Documentation | Data Sharing Overview

6: Snowflake Documentation | Secure Views

7: Snowflake Documentation | Cloning Databases, Schemas, and Tables

8: Snowflake Documentation | Cloning for Testing and Development

: SnowPro Advanced: Architect | Study Guide

: Cloning Overview

: Loading Data Using COPY into a Table

: Transforming Data During a Load

: Data Sharing Overview

: Secure Views

: Cloning Databases, Schemas, and Tables

: Cloning for Testing and Development

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.

 A standard virtual warehouse policy is one of the two scaling policies available for multi-cluster warehouses in Snowflake. The other policy is economic. A standard policy aims to prevent or minimize queuing by starting additional clusters as soon as the current cluster is fully loaded, regardless of the number of queries in the queue. This policy can improve query performance and concurrency, but it may also consume more credits than an economic policy, which tries to conserve credits by keeping the running clusters fully loaded before starting additional clusters. The scaling policy can be set when creating or modifying a warehouse, and it can be changed at any time.

References:

Snowflake Documentation: Multi-cluster Warehouses

Snowflake Documentation: Scaling Policy for Multi-cluster Warehouses

 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

 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

The Snowpipe REST API provides two endpoints for retrieving the data load history: insertReport and loadHistoryScan. The insertReport endpoint returns the status of the files that were submitted to the insertFiles endpoint, while the loadHistoryScan endpoint returns the history of the files that were actually loaded into the table by Snowpipe. To keep a log of data load history, it is recommended to use the loadHistoryScan endpoint, which provides more accurate and complete information about the data ingestion process. The loadHistoryScan endpoint accepts a start time and an end time as parameters, and returns the files that were loaded within that time range. The maximum time range that can be specified is 15 minutes, and the maximum number of files that can be returned is 10,000. Therefore, to keep a log of data load history, the best option is to call the loadHistoryScan endpoint every 10 minutes for a 15-minute time range, and store the results in a log file or a table. This way, the log will capture all the files that were loaded by Snowpipe, and avoid any gaps or overlaps in the time range. The other options are incorrect because:

Calling insertReport every 20 minutes, fetching the last 10,000 entries, will not provide a complete log of data load history, as some files may be missed or duplicated due to the asynchronous nature of Snowpipe. Moreover, insertReport only returns the status of the files that were submitted, not the files that were loaded.

Calling loadHistoryScan every minute for the maximum time range will result in too many API calls and unnecessary overhead, as the same files will be returned multiple times. Moreover, the maximum time range is 15 minutes, not 1 minute.

Calling insertReport every 8 minutes for a 10-minute time range will suffer from the same problems as option A, and also create gaps or overlaps in the time range.

References:

Snowpipe REST API

Option 1: Loading Data Using the Snowpipe REST API

PIPE_USAGE_HISTORY

The use of the search optimization service in Snowflake is particularly effective when queries involve operations that match exact substrings or start from the beginning of a string. The ALTER TABLE command adding search optimization specifically for substrings on the CA_ADDRESS_ID field allows the service to create an optimized search path for queries using substring matches.

Option A benefits because it directly matches a substring from the start of the CA_ADDRESS_ID, aligning with the optimization's capability to quickly locate records based on the beginning segments of strings.

Option B also benefits, despite performing a full equality check, because it essentially compares the full length of CA_ADDRESS_ID to a substring, which can leverage the substring index for efficient retrieval.Options C, D, and E involve patterns that do not start from the beginning of the string or use negations, which are not optimized by the search optimization service configured for starting substring matches.References: Snowflake's documentation on the use of search optimization for substring matching in SQL queries.

 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

 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

According to the Snowflake documentation, unstructured data files can be shared by using a secure view and Secure Data Sharing. A secure view allows the result of a query to be accessed like a table, and a secure view is specifically designated for data privacy. A scoped URL is an encoded URL that permits temporary access to a staged file without granting privileges to the stage. The URL expires when the persisted query result period ends, which is currently 24 hours. A scoped URL is recommended for file administrators to give scoped access to data files to specific roles in the same account. Snowflake records information in the query history about who uses a scoped URL to access a file, and when. Therefore, a scoped URL is the best option to share unstructured data within Snowflake, as it provides security, accountability, and control over the data access. References:

Sharing unstructured Data with a secure view

Introduction to Loading Unstructured Data

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.

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

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.

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?

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

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

According to the SnowPro Advanced: Architect documents and learning resources, the ways that Snowflake databases that are created from shares differ from standard databases that are not created from shares are:

Shared databases are read-only. This means that the data consumers who access the shared databases cannot modify or delete the data or the objects in the databases. The data providers who share the databases have full control over the data and the objects, and can grant or revoke privileges on them1.

Shared databases cannot be cloned. This means that the data consumers who access the shared databases cannot create a copy of the databases or the objects in the databases. The data providers who share the databases can clone the databases or the objects, but the clones are not automatically shared2.

Shared databases are not supported by Time Travel. This means that the data consumers who access the shared databases cannot use the AS OF clause to query historical data or restore deleted data. The data providers who share the databases can use Time Travel on the databases or the objects, but the historical data is not visible to the data consumers3.

The other options are incorrect because they are not ways that Snowflake databases that are created from shares differ from standard databases that are not created from shares. Option B is incorrect because shared databases do not need to be refreshed in order for new data to be visible. The data consumers who access the shared databases can see the latest data as soon as the data providers update the data1. Option E is incorrect because shared databases will not have the PUBLIC or INFORMATION_SCHEMA schemas without explicitly granting these schemas to the share. The data consumers who access the shared databases can only see the objects that the data providers grant to the share, and the PUBLIC and INFORMATION_SCHEMA schemas are not granted by default4. Option F is incorrect because shared databases cannot be created as transient databases. Transient databases are databases that do not support Time Travel or Fail-safe, and can be dropped without affecting the retention period of the data. Shared databases are always created as permanent databases, regardless of the type of the source database5. References: Introduction to Secure Data Sharing | Snowflake Documentation, Cloning Objects | Snowflake Documentation, Time Travel | Snowflake Documentation, Working with Shares | Snowflake Documentation, CREATE DATABASE | Snowflake Documentation

Event notifications in Snowpipe are messages sent by cloud storage providers to notify Snowflake of new or modified files in a stage. Snowpipe uses these notifications to trigger data loading from the stage to the target table. When a pipe is paused, event messages received for the pipe enter a limited retention period, which varies depending on the cloud storage provider. If the pipe is not resumed within the retention period, the event messages will be discarded and the data will not be loaded automatically. To load the data, the pipe must be resumed and the COPY command must be executed manually. This is a characteristic of event notifications in Snowpipe that distinguishes them from other options. References: Snowflake Documentation: Using Snowpipe, Snowflake Documentation: Pausing and Resuming a Pipe

 The warehouse MY_WH will only be active when there are results in the stream. This is because the task is created based on a stream, which means that the task will only be executed when there are new data in the stream. Additionally, the warehouse is set to auto_suspend - 60, which means that the warehouse will automatically suspend after 60 seconds of inactivity. Therefore, the warehouse will only be active when there are results in the stream. References:

[CREATE TASK | Snowflake Documentation]

[Using Streams and Tasks | Snowflake Documentation]

[CREATE WAREHOUSE | 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

 Option A is the best design to meet the requirements because it uses a materialized view on top of an external table against the S3 bucket in AWS Singapore. A materialized view is a database object that contains the results of a query and can be refreshed periodically to reflect changes in the underlying data1. An external table is a table that references data files stored in a cloud storage service, such as Amazon S32. By using a materialized view on top of an external table, the company can provide access to frequently changing data, keep egress costs to a minimum, and maintain low latency. This is because the materialized view will cache the query results in Snowflake, reducing the need to access the external data files and incur network charges. The materialized view will also improve the query performance by avoiding scanning the external data files every time. The materialized view can be refreshed on a schedule or on demand to capture the changes in the external data files1.

Option B is not the best design because it uses an external table against the S3 bucket in AWS Singapore and copies the data into transient tables. A transient table is a table that is not subject to the Time Travel and Fail-safe features of Snowflake, and is automatically purged after a period of time3. By using an external table and copying the data into transient tables, the company will incur more egress costs and operational overhead than using a materialized view. This is because the external table will access the external data files every time a query is executed, and the copy operation will also transfer data from S3 to Snowflake. The transient tables will also consume more storage space in Snowflake and require manual maintenance to ensure they are up to date.

Option C is not the best design because it copies the data between providers from S3 to Azure Blob storage to collocate, then uses Snowpipe for data ingestion. Snowpipe is a service that automates the loading of data from external sources into Snowflake tables4. By copying the data between providers, the company will incur high egress costs and latency, as well as operational complexity and maintenance of the infrastructure. Snowpipe will also add another layer of processing and storage in Snowflake, which may not be necessary if the external data files are already in a queryable format.

Option D is not the best design because it uses AWS Transfer Family to replicate data between the S3 bucket in AWS Singapore and an Azure Netherlands Blob storage, then uses an external table against the Blob storage. AWS Transfer Family is a service that enables secure and seamless transfer of files over SFTP, FTPS, and FTP to and from Amazon S3 or Amazon EFS5. By using AWS Transfer Family, the company will incur high egress costs and latency, as well as operational complexity and maintenance of the infrastructure. The external table will also access the external data files every time a query is executed, which may affect the query performance.

References: 1: Materialized Views 2: External Tables 3: Transient Tables 4: Snowpipe Overview 5: AWS Transfer Family

The AUTO_SUSPEND parameter controls the amount of time, in seconds, of inactivity after which a warehouse is automatically suspended. If the parameter is set to NULL, the warehouse never suspends. If the parameter is set to ‘0’, the warehouse suspends immediately after executing a query. Therefore, the execution behavior of the two virtual warehouses will be different depending on the AUTO_SUSPEND value. The warehouse with NULL value will keep running until it is manually suspended or the resource monitor limits are reached. The warehouse with ‘0’ value will suspend as soon as it finishes a query and release the compute resources. References:

ALTER WAREHOUSE

Parameters

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.

https://learn.snowflake.com/en/certifications/snowpro-advanced-architect/

 A materialized view is a feature that provides the capability to define an alternate cluster key for a table with an existing cluster key. A materialized view is a pre-computed result set that is stored in Snowflake and can be queried like a regular table. A materialized view can have a different cluster key than the base table, which can improve the performance and efficiency of queries on the materialized view. A materialized view can also support aggregations, joins, and filters on the base table data. A materialized view is automatically refreshed when the underlying data in the base table changes, as long as the AUTO_REFRESH parameter is set to true1.

References:

Materialized Views | Snowflake Documentation

A. The Snowflake SQL REST API does support the use of a GET command, which can be used to retrieve the status of a previously submitted query or to fetch the results of a query once it has been executed.D. The use of a CALL command to a stored procedure is supported, which can return a result set, including a table. This allows the invocation of stored procedures within Snowflake through the SQL REST API.

According to the Snowflake documentation, the following are some considerations for choosing clustering for a table1:

Clustering is optimal when either:

You require the fastest possible response times, regardless of cost.

Your improved query performance offsets the credits required to cluster and maintain the table.

Clustering is most effective when the clustering key is used in the following types of query predicates:

Filter predicates (e.g. WHERE clauses)

Join predicates (e.g. ON clauses)

Grouping predicates (e.g. GROUP BY clauses)

Sorting predicates (e.g. ORDER BY clauses)

Clustering is less effective when the clustering key is not used in any of the above query predicates, or when the clustering key is used in a predicate that requires a function or expression to be applied to the key (e.g. DATE_TRUNC, TO_CHAR, etc.).

For most tables, Snowflake recommends a maximum of 3 or 4 columns (or expressions) per key. Adding more than 3-4 columns tends to increase costs more than benefits.

Based on these considerations, the best option for the clustering key columns is C. C1, C3, C2, because:

These columns are heavily used in filter and join conditions of SELECT queries, which are the most effective types of predicates for clustering.

These columns have high cardinality, which means they have many distinct values and can help reduce the clustering skew and improve the compression ratio.

These columns are likely to be correlated with each other, which means they can help co-locate similar rows in the same micro-partitions and improve the scan efficiency.

These columns do not require any functions or expressions to be applied to them, which means they can be directly used in the predicates without affecting the clustering.

References: 1: Considerations for Choosing Clustering for a Table | Snowflake Documentation

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:

pipe: The fully-qualified name of the pipe to query.

startTimeInclusive: The start of the time range to query, in ISO 8601 format. The value must be within the past 14 days.

endTimeExclusive: The end of the time range to query, in ISO 8601 format. The value must be later than the start time and within the past 14 days.

recentFirst: A boolean flag that indicates whether to return the most recent files first or last. The default value is false, which means the oldest files are returned first.

showSkippedFiles: A boolean flag that indicates whether to include files that were skipped by Snowpipe in the response. The default value is false, which means only files that were loaded are returned.

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

The get command in SnowSQL is a convenient way to download files from an internal stage to a local directory. The get command can be used in interactive mode or in a script, and it supports wildcards and parallel downloads. The get command also allows specifying the overwrite option, which determines how to handle existing files with the same name2

The Snowflake Connector for Python, the Snowflake API endpoint, and the get command in Snowsight are not recommended methods for downloading files from an internal stage, because they require more operational overhead than the get command in SnowSQL. The Snowflake Connector for Python and the Snowflake API endpoint require writing and maintaining code to handle the connection, authentication, and file transfer. The get command in Snowsight requires using the web interface and manually selecting the files to download34 References:

1: SnowPro Advanced: Architect | Study Guide

2: Snowflake Documentation | Using the GET Command

3: Snowflake Documentation | Using the Snowflake Connector for Python

4: Snowflake Documentation | Using the Snowflake API

: Snowflake Documentation | Using the GET Command in Snowsight

: SnowPro Advanced: Architect | Study Guide

: Using the GET Command

: Using the Snowflake Connector for Python

: Using the Snowflake API

: [Using the GET Command in Snowsight]

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.

These two configuration options can enhance the performance of the workload that consists of a huge number of concurrent queries that are smaller and faster.

Enabling a multi-clustered virtual warehouse in maximized mode allows the warehouse to scale out automatically by adding more clusters as soon as the current cluster is fully loaded, regardless of the number of queries in the queue. This can improve the concurrency and throughput of the workload by minimizing or preventing queuing. The maximized mode is suitable for workloads that require high performance and low latency, and are less sensitive to credit consumption1.

Setting the MAX_CONCURRENCY_LEVEL to a higher value than its default value of 8 at the virtual warehouse level allows the warehouse to run more queries concurrently on each cluster. This can improve the utilization and efficiency of the warehouse resources, especially for smaller and faster queries that do not require a lot of processing power. The MAX_CONCURRENCY_LEVEL parameter can be set when creating or modifying a warehouse, and it can be changed at any time2.

References:

Snowflake Documentation: Scaling Policy for Multi-cluster Warehouses

Snowflake Documentation: MAX_CONCURRENCY_LEVEL