Pass the Snowflake SnowPro Advanced: Architect ARA-C01 Questions and answers with CertsForce

 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.

The VALIDATION_MODE parameter is used to specify the behavior of the COPY statement when loading data into a table. It is used to specify whether the COPY statement should return an error if any of the rows in the file are invalid or if it should continue loading the valid rows. The VALIDATION_MODE parameter is only supported for COPY statements that load data from external stages1.

The query in the question uses a data transformation query to load data from an internal stage. A data transformation query is a query that transforms the data during the load process, such as parsing JSON or XML data, applying functions, or joining with other tables2.

According to the documentation, VALIDATION_MODE does not support COPY statements that transform data during a load. If the parameter is specified, the COPY statement returns an error1. Therefore, option C is the correct answer.

 COPY INTO <title> : Transforming Data During a Load

 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.

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 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.

Snowflake Documentation: Column Masking

Snowflake Learning: Column Masking

 Role-Based Access Control (RBAC) is the Snowflake Access Control Framework that allows privileges to be granted by object owners to roles, and roles, in turn, can be assigned to users to restrict or allow actions to be performed on objects. A characteristic of RBAC as used in Snowflake is:

Privileges can be granted at the database level and can be inherited by all underlying objects. This means that a role that has a certain privilege on a database, such as CREATE SCHEMA or USAGE, can also perform the same action on any schema, table, view, or other object within that database, unless explicitly revoked. This simplifies the access control management and reduces the number of grants required.

A user can create managed access schemas to support future grants and ensure only schema owners can grant privileges to other roles. This means that a user can create a schema with the MANAGED ACCESS option, which changes the default behavior of object ownership and privilege granting within the schema. In a managed access schema, object owners lose the ability to grant privileges on their objects to other roles, and only the schema owner or a role with the MANAGE GRANTS privilege can do so. This enhances the security and governance of the schema and its objects.

The other options are not characteristics of RBAC as used in Snowflake:

A user can use a “super-user” access along with securityadmin to bypass authorization checks and access all databases, schemas, and underlying objects. This is not true, as there is no such thing as a “super-user” access in Snowflake. The securityadmin role is a predefined role that can manage users and roles, but it does not have any privileges on any database objects by default. To access any object, the securityadmin role must be explicitly granted the appropriate privilege by the object owner or another role with the grant option.

A user can create managed access schemas to support current and future grants and ensure only object owners can grant privileges to other roles. This is not true, as this contradicts the definition of a managed access schema. In a managed access schema, object owners cannot grant privileges on their objects to other roles, and only the schema owner or a role with the MANAGE GRANTS privilege can do so.

Overview of Access Control

A Functional Approach For Snowflake’s Role-Based Access Controls

Snowflake Role-Based Access Control simplified

Snowflake RBAC security prefers role inheritance to role composition

Overview of Snowflake Role Based Access Control

The purpose of creating a storage integration in Snowflake includes:

B.Store a generated identity and access management (IAM) entity for an external cloud provider- This helps in managing authentication and authorization with external cloud storage without embedding credentials in Snowflake. It supports various cloud providers like AWS, Azure, or GCP, ensuring that the identity management is streamlined across platforms.

C.Support multiple external stages using one single Snowflake object- Storage integrations allow you to set up access configurations that can be reused across multiple external stages, simplifying the management of external data integrations.

D.Avoid supplying credentials when creating a stage or when loading or unloading data- By using a storage integration, Snowflake can interact with external storage without the need to continuously manage or expose sensitive credentials, enhancing security and ease of operations.

The Data Vault modeling approach separates data into hubs (business keys), links, and satellites, often requiring simultaneous insertion of related records derived from the same source dataset. Snowflake’s conditional multi-table INSERT enables a single source query to populate multiple target tables in parallel based on conditional logic. This capability aligns well with Data Vault patterns, where hubs and satellites are typically loaded together from the same staging data.

By inserting into hubs and satellites in parallel using surrogate keys (Answer A), architects ensure consistency, atomicity, and efficient processing without re-scanning source data multiple times. This approach reduces compute usage and simplifies ETL logic, which is particularly valuable in large-scale, near–real-time ingestion pipelines.

Options involving dimensions and facts relate more closely to star schema modeling, not Data Vault. Sequential insertion is also less efficient and not the defining advantage of conditional multi-table inserts. For SnowPro Architect candidates, this question emphasizes understanding how Snowflake SQL features support modern data modeling techniques such as Data Vault.

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QUESTION NO: 42 [Architecting Snowflake Solutions]

An Architect wants to alter a virtual warehouse for horizontal scaling but cannot find minimum and maximum cluster settings in the interface, even with the ACCOUNTADMIN role.

What is the MOST likely issue?

A. Missing CREATE WAREHOUSE privilege.

B. Incorrect SQL command used.

C. Using Standard edition where multi-cluster is not available.

D. A Snowflake Support ticket is required.

Answer: C

Multi-cluster virtual warehouses are only available in Snowflake Enterprise edition and higher. If the account is running on Standard edition, the options to configure minimum and maximum clusters for horizontal scaling will not appear in the UI or be available via SQL (Answer C).

This limitation is independent of role privileges; even ACCOUNTADMIN cannot enable multi-cluster functionality on unsupported editions. Incorrect SQL or missing privileges would result in errors rather than missing configuration options.

For SnowPro Architect candidates, this question tests awareness of edition-based feature availability, which is critical when designing scalable architectures and recommending Snowflake editions to meet workload requirements.

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QUESTION NO: 43 [Performance Optimization and Monitoring]

A 2 TB table with 400 columns is queried frequently to calculate average employee tenure by country and career level. The query exhibits poor partition pruning and runs on an X-Small warehouse.

What improvement meets the requirements with the LEAST operational overhead?

A. Add clustering keys on COUNTRY and EMPLOYMENT_STATUS.

B. Enable Query Acceleration Service (QAS).

C. Enable search optimization on equality predicates for COUNTRY and EMPLOYMENT_STATUS.

D. Build a materialized view for latest active employee records per country.

Answer: C

The query filters on equality predicates for COUNTRY, EMPLOYMENT_STATUS, and a specific EFFECTIVE_DATE value representing the latest record. Search Optimization Service (SOS) is specifically designed to accelerate highly selective equality predicates without requiring data reorganization (Answer C).

Clustering would introduce ongoing maintenance overhead and is less effective when cardinality is low or when multiple filter combinations exist. Query Acceleration Service helps with overall query execution time but does not directly improve partition pruning. Materialized views would require ongoing maintenance and additional storage, increasing operational complexity.

For SnowPro Architect candidates, this question highlights choosing the simplest effective optimization tool—SOS—when dealing with selective filters and large tables.

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QUESTION NO: 44 [Snowflake Data Engineering]

A team needs to recover data after pipeline failures or business rule violations.

Requirements:

• Database recoverable for 48 hours

• Analytics schema recoverable for 5 days

What is the correct approach?

A. Use custom stored procedure backups.

B. Use tasks with database and schema clones.

C. Use Time Travel with MIN_DATA_RETENTION_TIME_IN_DAYS = 2 and DATA_RETENTION_TIME_IN_DAYS = 5 on the ANALYTICS schema.

D. Use Time Travel with DATA_RETENTION_TIME_IN_DAYS = 2 and MIN_DATA_RETENTION_TIME_IN_DAYS = 5 on the ANALYTICS schema.

Answer: C

Snowflake Time Travel enables point-in-time recovery without manual backups. Setting MIN_DATA_RETENTION_TIME_IN_DAYS = 2 ensures that all database objects retain at least 48 hours of recoverable history. Extending DATA_RETENTION_TIME_IN_DAYS = 5 at the schema level allows longer recovery specifically for the analytics schema (Answer C).

This approach meets both recovery requirements with minimal operational overhead and aligns with Snowflake best practices. Cloning via tasks introduces unnecessary complexity and storage management. Custom backups are not required due to built-in Time Travel capabilities.

SnowPro Architect exams emphasize using native Snowflake features such as Time Travel for data recovery scenarios.

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QUESTION NO: 45 [Security and Access Management]

A financial services company needs to isolate sensitive production data from development data within the same region and support secure data transfer between environments.

What is the best solution?

A. Create two accounts with network policies and use data sharing.

B. Create two accounts with federated authentication and use cloning.

C. Create two databases in one account and use replication.

D. Create two databases in one account and use user-level network policies and shares.

Answer: A

Strong isolation of sensitive production data from development environments is best achieved using separate Snowflake accounts. Account-level isolation ensures independent security boundaries, network policies, and governance controls. Secure Data Sharing allows controlled, read-only access to production data without copying it, supporting safe data transfer between environments (Answer A).

Using cloning across accounts is not supported; cloning works only within the same account. Database-level separation does not provide the same security guarantees as account-level isolation, and user-level network policies are not supported in Snowflake.

This design aligns with SnowPro Architect best practices for regulated industries, emphasizing strong isolation, least privilege, and secure data access mechanisms.

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

Option B: This alters the network policy directly linked toanalyst_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.SECURITYADMINis 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 (USERADMINtypically 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.

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.

 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.

Snowflake Documentation: Multi-cluster Warehouses

Snowflake Documentation: Scaling Policy for Multi-cluster Warehouses