ARA-R01 Sample Questions Answers

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 Snowflake Connector for Kafka is a Kafka Connect sink connector that reads data from one or more Apache Kafka topics and loads the data into a Snowflake table. The connector supports different authentication methods to connect to Snowflake, such as key pair authentication, OAUTH, and basic authentication (for example, username and password). The connector also supports different encryption methods, such as HTTPS and SSL1. The connector does not require that every Kafka message is in JSON or Avro format, as it can handle other formats such as CSV, XML, and Parquet2. The default retention time for Kafka topics is not relevant for the connector, as it only consumes the messages that are available in the topics and does not store them in Kafka. The connector will create one table and one pipe to ingest data for each topic by default, but this behavior can be customized by using the snowflake.topic2table.map configuration property3. If the connector cannot create the table or the pipe, it will log an error and retry the operation until it succeeds or the connector is stopped4. References:

• Installing and Configuring the Kafka Connector
• Overview of the Kafka Connector
• Managing the Kafka Connector
• Troubleshooting the Kafka Connector

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.

 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]

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

• Snowflake supports data sharing across regions and cloud platforms using account replication and share replication features. Account replication enables the replication of objects from a source account to one or more target accounts in the same organization. Share replication enables the replication of shares from a source account to one or more target accounts in the same organization1.
• To share data from the MARKET_DB database in the ACCOUNTA account in AWS us-east-1 region with the PARTNERB account in Azure East US 2 region, the following steps must be performed:
• Therefore, option C is the correct answer.

References: : Replicating Shares Across Regions and Cloud Platforms : Working with Organizations and Accounts : Replicating Databases Across Multiple Accounts : Replicating Shares Across Multiple Accounts

These three design elements are required to meet the security, compliance, and governance requirements for the project.

• To use Tri-Secret Secure in Snowflake, the Business Critical edition of Snowflake is required. This edition provides enhanced data protection features, such as customer-managed encryption keys, that are not available in lower editions. Tri-Secret Secure is a feature that combines a Snowflake-maintained key and a customer-managed key to create a composite master key to encrypt the data in Snowflake1.
• To share some information stored in a view with another Snowflake customer, a secure view is recommended. A secure view is a view that hides the underlying data and the view definition from unauthorized users. Only the owner of the view and the users who are granted the owner’s role can see the view definition and the data in the base tables of the view2. A secure view can be shared with another Snowflake account using a data share3.
• To hide portions of sensitive information from some columns, Dynamic Data Masking can be used. Dynamic Data Masking is a feature that allows applying masking policies to columns to selectively mask plain-text data at query time. Depending on the masking policy conditions and the user’s role, the data can be fully or partially masked, or shown as plain-text4.

Transient databases in Snowflake are designed for situations where data retention is not critical, and they do not have the fail-safe period that regular databases have. This means that data in a transient database is not recoverable after the Time Travel retention period. Using a transient database is efficient because it minimizes storage costs while still providing most functionalities of a standard database without the overhead of data protection features that are not needed when data retention is not a concern.

The error message indicates that the IP address in the error message is not allowed to access Snowflake because it is not in the allowed list of the network policy. The network policy is a feature that allows restricting access to Snowflake based on IP addresses or ranges. To resolve this error, the Architect should take the following steps:

• Add the IP address in the error message to the allowed list in the network policy. This will allow the IP address to access Snowflake using the Private Link URLs. Alternatively, the Architect can disable the network policy if it is not required for security reasons.
• Update the configuration of the Azure AD SSO to use the Private Link URLs. This will ensure that the SSO authentication process uses the Private Link URLs instead of the public URLs. The configuration can be updated by following the steps in the Azure documentation1.

These two steps should resolve the error and ensure that the account is accessed using only Private Link. The other options are not necessary or relevant for this scenario. Altering the Azure security integration to use the Private Link URLs is not required because the security integration is used for SCIM provisioning, not for SSO authentication. Generating a new SCIM access token using system$generate_scim_access_token and saving it to Azure AD is not required because the SCIM access token is used for SCIM provisioning, not for SSO authentication. Opening a case with Snowflake Support to authorize the Private Link URLs’ access to the account is not required because the authorization can be done by the account administrator using the SYSTEM$AUTHORIZE_PRIVATELINK function2.

When using the COPY command to load data into Snowflake, if a column has a default value set to CURRENT_TIME() or CURRENT_TIMESTAMP(), all rows loaded by that specific COPY command will have the same timestamp. This is because the default value for the timestamp is evaluated at the start of the COPY operation, and that same value is applied to all rows loaded by that operation.

References: This behavior is consistent with Snowflake’s documentation on the CURRENT_TIMESTAMP function, which specifies that the timestamp is captured at the time the statement is executed1.

The query is executing a clone operation on an existing table t_sales with an offset to account for the retention time. The syntax used is correct for cloning a table in Snowflake, and the use of the at(offset => -60*30) clause is valid. This specifies that the clone should be based on the state of the table 30 minutes prior (60 seconds * 30). Assuming the table t_sales exists and has been modified within the last 30 minutes, and considering the data_retention_time_in_days is set to 1 day (which enables time travel queries for the past 24 hours), the table t_sales_clone would be successfully created based on the state of t_sales 30 minutes before the clone command was issued.

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

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.

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

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

References:

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

The 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

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 minimum value supported for the buffer.flush.time property is 1 (in seconds). For higher average data flow rates, we suggest that you decrease the default value for improved latency. If cost is a greater concern than latency, you could increase the buffer flush time. Be careful to flush the Kafka memory buffer before it becomes full to avoid out of memory exceptions. https://docs.snowflake.com/en/user-guide/data-load-snowpipe-streaming-kafka

• According to the Principle of Least Privilege, the Architect should grant the minimum privileges necessary for the USER1 to find the tables in the SANDBOX database.
• The USER1 needs to have USAGE privilege on the SANDBOX database and the SANDBOX.PUBLIC schema to be able to access the tables in the PUBLIC schema. Therefore, the commands B and C are the correct ones to run.
• The command A is not correct because the PUBLIC role is automatically granted to every user and role in the account, and it does not have any privileges on the SANDBOX database by default.
• The command D is not correct because it would transfer the ownership of the SANDBOX database from the Architect to the USER1, which is not necessary and violates the Principle of Least Privilege.
• The command E is not correct because it would grant all the possible privileges on the SANDBOX database to the USER1, which is also not necessary and violates the Principle of Least Privilege.

References: : Snowflake - Principle of Least Privilege : Snowflake - Access Control Privileges : Snowflake - Public Role : Snowflake - Ownership and Grants

Snowflake is a cloud data platform that supports various data models for modeling tables in a Snowflake environment. The data models can be classified into two categories: dimensional and normalized. Dimensional data models are designed to optimize query performance and ease of use for business intelligence and analytics. Normalized data models are designed to reduce data redundancy and ensure data integrity for transactional and operational systems. The following are some of the data models that can be used in Snowflake:

• Dimensional/Kimball: This is a popular dimensional data model that uses a star or snowflake schema to organize data into fact and dimension tables. Fact tables store quantitative measures and foreign keys to dimension tables. Dimension tables store descriptive attributes and hierarchies. A star schema has a single denormalized dimension table for each dimension, while a snowflake schema has multiple normalized dimension tables for each dimension. Snowflake supports both star and snowflake schemas, and allows users to create views and joins to simplify queries.
• Inmon/3NF: This is a common normalized data model that uses a third normal form (3NF) schema to organize data into entities and relationships. 3NF schema eliminates data duplication and ensures data consistency by applying three rules: 1) every column in a table must depend on the primary key, 2) every column in a table must depend on the whole primary key, not a part of it, and 3) every column in a table must depend only on the primary key, not on other columns. Snowflake supports 3NF schema and allows users to create referential integrity constraints and foreign key relationships to enforce data quality.
• Data vault: This is a hybrid data model that combines the best practices of dimensional and normalized data models to create a scalable, flexible, and resilient data warehouse. Data vault schema consists of three types of tables: hubs, links, and satellites. Hubs store business keys and metadata for each entity. Links store associations and relationships between entities. Satellites store descriptive attributes and historical changes for each entity or relationship. Snowflake supports data vault schema and allows users to leverage its features such as time travel, zero-copy cloning, and secure data sharing to implement data vault methodology.

References: What is Data Modeling? | Snowflake, Snowflake Schema in Data Warehouse Model - GeeksforGeeks, [Data Vault 2.0 Modeling with Snowflake]

• The SQL statement is a command for creating a pipe in Snowflake, which is an object that defines the COPY INTO

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.

 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 data formats that are supported for the messages when using the Snowflake Connector for Kafka are Avro and JSON. These are the two formats that the connector can parse and convert into Snowflake table rows. The connector supports both schemaless and schematized JSON, as well as Avro with or without a schema registry1. The other options are incorrect because they are not supported data formats for the messages. CSV, XML, and Parquet are not formats that the connector can parse and convert into Snowflake table rows. If the messages are in these formats, the connector will load them as VARIANT data type and store them as raw strings in the table2. References: Snowflake Connector for Kafka | Snowflake Documentation, Loading Protobuf Data using the Snowflake Connector for Kafka | Snowflake Documentation

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.

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

References

•Snowflake Documentation on Search Optimization Service1.

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

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

References:

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

According to the Snowflake 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

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.

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

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.

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

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

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

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

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

References:

• Semi-structured Data
• Snowflake for Data Lake

According to the SnowPro Advanced: Architect documents and learning resources, the Snowflake functionality that should be used to meet these requirements are:

• Use private connectivity from a cloud provider. This feature allows customers to connect to Snowflake from their own private network without exposing their data to the public Internet. Snowflake integrates with AWS PrivateLink, Azure Private Link, and Google Cloud Private Service Connect to offer private connectivity from customers’ VPCs or VNets to Snowflake endpoints. Customers can control how traffic reaches the Snowflake endpoint and avoid the need for proxies or public IP addresses123.
• Set up SSO for federated authentication. This feature allows customers to use their existing identity provider (IdP) to authenticate users for SSO access to Snowflake. Snowflake supports most SAML 2.0-compliant vendors as an IdP, including Okta, Microsoft AD FS, Google G Suite, Microsoft Azure Active Directory, OneLogin, Ping Identity, and PingOne. By setting up SSO for federated authentication, customers can leverage their existing user credentials and profile information, and provide stronger security than username/password authentication4.

The other options are incorrect because they do not meet the requirements or are not feasible. Option A is incorrect because setting up replication does not allow users to connect from outside the company VPN. Replication is a feature of Snowflake that enables copying databases across accounts in different regions and cloud platforms. Replication does not affect the connectivity or visibility of the accounts5. Option B is incorrect because provisioning a unique company Tri-Secret Secure key does not affect the network or authentication requirements. Tri-Secret Secure is a feature of Snowflake that allows customers to manage their own encryption keys for data at rest in Snowflake, using a combination of three secrets: a master key, a service key, and a security password. Tri-Secret Secure provides an additional layer of security and control over the data encryption and decryption process, but it does not enable private connectivity or SSO6. Option E is incorrect because using a proxy Snowflake account outside the VPN, enabling client redirect for user logins, is not a supported or recommended way of meeting the requirements. Client redirect is a feature of Snowflake that allows customers to connect to a different Snowflake account than the one specified in the connection string. This feature is useful for scenarios such as cross-region failover, data sharing, and account migration, but it does not provide private connectivity or SSO7. References: AWS PrivateLink & Snowflake | Snowflake Documentation, Azure Private Link & Snowflake | Snowflake Documentation, Google Cloud Private Service Connect & Snowflake | Snowflake Documentation, Overview of Federated Authentication and SSO | Snowflake Documentation, Replicating Databases Across Multiple Accounts | Snowflake Documentation, Tri-Secret Secure | Snowflake Documentation, Redirecting Client Connections | Snowflake Documentation

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

The Snowflake architecture recommendation that necessitates multiple Snowflake accounts for implementation is the separation of development, test, and production environments. This approach, known as Account per Tenant (APT), isolates tenants into separate Snowflake accounts, ensuring dedicated resources and security isolation12.

References

•Snowflake’s white paper on “Design Patterns for Building Multi-Tenant Applications on Snowflake” discusses the APT model and its requirement for separate Snowflake accounts for each tenant1.

•Snowflake Documentation on Secure Data Sharing, which mentions the possibility of sharing data across multiple accounts3.

The best way to integrate an enterprise identity provider with federated authentication and enable automatic user creation and role assignment in Snowflake is to use SCIM (System for Cross-domain Identity Management). SCIM allows Snowflake to synchronize with the identity provider and create users and groups based on the information provided by the identity provider. The groups are mapped to roles in Snowflake, and the users are assigned the roles based on their group membership. This way, the identity provider remains the source of truth for user and group management, and Snowflake automatically reflects the changes without manual intervention. The other options are either incorrect or incomplete, as they involve using OAuth, which is a protocol for authorization, not authentication or user provisioning, and require additional configuration of authorization and resource servers.

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

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.

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

References =

•Snowflake’s documentation on multi-table inserts1

•Blog post on optimizing Data Vault architecture on Snowflake2

 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

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.

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