Snowflake ARA-C01 Practice Exams
Last updated on Oct 22,2026- Exam Code: ARA-C01
- Exam Name: SnowPro Advanced Architect Certification
- Certification Provider: Snowflake
- Latest update: Oct 22,2026
Question #211
How can an Architect enable optimal clustering to enhance performance for different access paths on a given table?
- A . Create multiple clustering keys for a table.
- B . Create multiple materialized views with different cluster keys.
- C . Create super projections that will automatically create clustering.
- D . Create a clustering key that contains all columns used in the access paths.
Correct Answer: B
B
Explanation:
According to the SnowPro Advanced: Architect documents and learning resources, the best way to enable optimal clustering to enhance performance for different access paths on a given table is to create multiple materialized views with different cluster keys. A materialized view is a pre-computed result set that is derived from a query on one or more base tables. A materialized view can be clustered by specifying a clustering key, which is a subset of columns or expressions that determines how the data in the materialized view is co-located in micro-partitions. By creating multiple materialized views with different cluster keys, an Architect can optimize the performance of queries that use different access paths on the same base table. For example, if a base table has columns A, B, C, and D, and there are queries that filter on A and B, or on C and D, or on A and C, the Architect can create three materialized views, each with a different cluster key: (A, B), (C, D), and (A, C). This way, each query can leverage the optimal clustering of the corresponding materialized view and achieve faster scan efficiency and better compression.
Reference: Snowflake Documentation: Materialized Views
Snowflake Learning: Materialized Views
https://www.snowflake.com/blog/using-materialized-views-to-solve-multi-clustering-performance-problems/
B
Explanation:
According to the SnowPro Advanced: Architect documents and learning resources, the best way to enable optimal clustering to enhance performance for different access paths on a given table is to create multiple materialized views with different cluster keys. A materialized view is a pre-computed result set that is derived from a query on one or more base tables. A materialized view can be clustered by specifying a clustering key, which is a subset of columns or expressions that determines how the data in the materialized view is co-located in micro-partitions. By creating multiple materialized views with different cluster keys, an Architect can optimize the performance of queries that use different access paths on the same base table. For example, if a base table has columns A, B, C, and D, and there are queries that filter on A and B, or on C and D, or on A and C, the Architect can create three materialized views, each with a different cluster key: (A, B), (C, D), and (A, C). This way, each query can leverage the optimal clustering of the corresponding materialized view and achieve faster scan efficiency and better compression.
Reference: Snowflake Documentation: Materialized Views
Snowflake Learning: Materialized Views
https://www.snowflake.com/blog/using-materialized-views-to-solve-multi-clustering-performance-problems/
Question #211
How can an Architect enable optimal clustering to enhance performance for different access paths on a given table?
- A . Create multiple clustering keys for a table.
- B . Create multiple materialized views with different cluster keys.
- C . Create super projections that will automatically create clustering.
- D . Create a clustering key that contains all columns used in the access paths.
Correct Answer: B
B
Explanation:
According to the SnowPro Advanced: Architect documents and learning resources, the best way to enable optimal clustering to enhance performance for different access paths on a given table is to create multiple materialized views with different cluster keys. A materialized view is a pre-computed result set that is derived from a query on one or more base tables. A materialized view can be clustered by specifying a clustering key, which is a subset of columns or expressions that determines how the data in the materialized view is co-located in micro-partitions. By creating multiple materialized views with different cluster keys, an Architect can optimize the performance of queries that use different access paths on the same base table. For example, if a base table has columns A, B, C, and D, and there are queries that filter on A and B, or on C and D, or on A and C, the Architect can create three materialized views, each with a different cluster key: (A, B), (C, D), and (A, C). This way, each query can leverage the optimal clustering of the corresponding materialized view and achieve faster scan efficiency and better compression.
Reference: Snowflake Documentation: Materialized Views
Snowflake Learning: Materialized Views
https://www.snowflake.com/blog/using-materialized-views-to-solve-multi-clustering-performance-problems/
B
Explanation:
According to the SnowPro Advanced: Architect documents and learning resources, the best way to enable optimal clustering to enhance performance for different access paths on a given table is to create multiple materialized views with different cluster keys. A materialized view is a pre-computed result set that is derived from a query on one or more base tables. A materialized view can be clustered by specifying a clustering key, which is a subset of columns or expressions that determines how the data in the materialized view is co-located in micro-partitions. By creating multiple materialized views with different cluster keys, an Architect can optimize the performance of queries that use different access paths on the same base table. For example, if a base table has columns A, B, C, and D, and there are queries that filter on A and B, or on C and D, or on A and C, the Architect can create three materialized views, each with a different cluster key: (A, B), (C, D), and (A, C). This way, each query can leverage the optimal clustering of the corresponding materialized view and achieve faster scan efficiency and better compression.
Reference: Snowflake Documentation: Materialized Views
Snowflake Learning: Materialized Views
https://www.snowflake.com/blog/using-materialized-views-to-solve-multi-clustering-performance-problems/
Question #211
How can an Architect enable optimal clustering to enhance performance for different access paths on a given table?
- A . Create multiple clustering keys for a table.
- B . Create multiple materialized views with different cluster keys.
- C . Create super projections that will automatically create clustering.
- D . Create a clustering key that contains all columns used in the access paths.
Correct Answer: B
B
Explanation:
According to the SnowPro Advanced: Architect documents and learning resources, the best way to enable optimal clustering to enhance performance for different access paths on a given table is to create multiple materialized views with different cluster keys. A materialized view is a pre-computed result set that is derived from a query on one or more base tables. A materialized view can be clustered by specifying a clustering key, which is a subset of columns or expressions that determines how the data in the materialized view is co-located in micro-partitions. By creating multiple materialized views with different cluster keys, an Architect can optimize the performance of queries that use different access paths on the same base table. For example, if a base table has columns A, B, C, and D, and there are queries that filter on A and B, or on C and D, or on A and C, the Architect can create three materialized views, each with a different cluster key: (A, B), (C, D), and (A, C). This way, each query can leverage the optimal clustering of the corresponding materialized view and achieve faster scan efficiency and better compression.
Reference: Snowflake Documentation: Materialized Views
Snowflake Learning: Materialized Views
https://www.snowflake.com/blog/using-materialized-views-to-solve-multi-clustering-performance-problems/
B
Explanation:
According to the SnowPro Advanced: Architect documents and learning resources, the best way to enable optimal clustering to enhance performance for different access paths on a given table is to create multiple materialized views with different cluster keys. A materialized view is a pre-computed result set that is derived from a query on one or more base tables. A materialized view can be clustered by specifying a clustering key, which is a subset of columns or expressions that determines how the data in the materialized view is co-located in micro-partitions. By creating multiple materialized views with different cluster keys, an Architect can optimize the performance of queries that use different access paths on the same base table. For example, if a base table has columns A, B, C, and D, and there are queries that filter on A and B, or on C and D, or on A and C, the Architect can create three materialized views, each with a different cluster key: (A, B), (C, D), and (A, C). This way, each query can leverage the optimal clustering of the corresponding materialized view and achieve faster scan efficiency and better compression.
Reference: Snowflake Documentation: Materialized Views
Snowflake Learning: Materialized Views
https://www.snowflake.com/blog/using-materialized-views-to-solve-multi-clustering-performance-problems/
Question #214
A company needs to share its product catalog data with one of its partners. The product catalog data is stored in two database tables: product_category, and product_details. Both tables can be joined by the product_id column. Data access should be governed, and only the partner should have access to the records.
The partner is not a Snowflake customer. The partner uses Amazon S3 for cloud storage.
Which design will be the MOST cost-effective and secure, while using the required Snowflake features?
- A . Use Secure Data Sharing with an S3 bucket as a destination.
- B . Publish product_category and product_details data sets on the Snowflake Marketplace.
- C . Create a database user for the partner and give them access to the required data sets.
- D . Create a reader account for the partner and share the data sets as secure views.
Correct Answer: D
D
Explanation:
A reader account is a type of Snowflake account that allows external users to access data shared by a provider account without being a Snowflake customer. A reader account can be created and managed by the provider account, and can use the Snowflake web interface or JDBC/ODBC drivers to query the shared data. A reader account is billed to the provider account based on the credits consumed by the queries1. A secure view is a type of view that applies row-level security filters to the underlying tables, and masks the data that is not accessible to the user. A secure view can be shared with a reader account to provide granular and governed access to the data2. In this scenario, creating a reader account for the partner and sharing the data sets as secure views would be the most cost-effective and secure design, while using the required Snowflake features, because:
It would avoid the data transfer and storage costs of using an S3 bucket as a destination, and the potential security risks of exposing the data to unauthorized access or modification.
It would avoid the complexity and overhead of publishing the data sets on the Snowflake Marketplace, and the potential loss of control over the data ownership and pricing.
It would avoid the need to create a database user for the partner and grant them access to the required data sets, which would require the partner to have a Snowflake account and consume the
provider’s resources.
Reference: Reader Accounts
Secure Views
D
Explanation:
A reader account is a type of Snowflake account that allows external users to access data shared by a provider account without being a Snowflake customer. A reader account can be created and managed by the provider account, and can use the Snowflake web interface or JDBC/ODBC drivers to query the shared data. A reader account is billed to the provider account based on the credits consumed by the queries1. A secure view is a type of view that applies row-level security filters to the underlying tables, and masks the data that is not accessible to the user. A secure view can be shared with a reader account to provide granular and governed access to the data2. In this scenario, creating a reader account for the partner and sharing the data sets as secure views would be the most cost-effective and secure design, while using the required Snowflake features, because:
It would avoid the data transfer and storage costs of using an S3 bucket as a destination, and the potential security risks of exposing the data to unauthorized access or modification.
It would avoid the complexity and overhead of publishing the data sets on the Snowflake Marketplace, and the potential loss of control over the data ownership and pricing.
It would avoid the need to create a database user for the partner and grant them access to the required data sets, which would require the partner to have a Snowflake account and consume the
provider’s resources.
Reference: Reader Accounts
Secure Views
Question #215
What built-in Snowflake features make use of the change tracking metadata for a table? (Choose two.)
- A . The MERGE command
- B . The UPSERT command
- C . The CHANGES clause
- D . A STREAM object
- E . The CHANGE_DATA_CAPTURE command
Correct Answer: A, D
A, D
Explanation:
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.
Reference: Snowflake Documentation on MERGE and STREAM Objects.
A, D
Explanation:
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.
Reference: Snowflake Documentation on MERGE and STREAM Objects.
Question #216
What is a characteristic of Role-Based Access Control (RBAC) as used in Snowflake?
- A . Privileges can be granted at the database level and can be inherited by all underlying objects.
- B . A user can use a "super-user" access along with securityadmin to bypass authorization checks and access all databases, schemas, and underlying objects.
- C . A user can create managed access schemas to support future grants and ensure only schema owners can grant privileges to other roles.
- D . A user can create managed access schemas to support current and future grants and ensure only object owners can grant privileges to other roles.
Correct Answer: C
C
Explanation:
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.
Reference: 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
C
Explanation:
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.
Reference: 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
Question #217
Which of the following ingestion methods can be used to load near real-time data by using the messaging services provided by a cloud provider?
- A . Snowflake Connector for Kafka
- B . Snowflake streams
- C . Snowpipe
- D . Spark
Correct Answer: A
A
Explanation:
Snowflake Connector for Kafka and Snowpipe are two ingestion methods that can be used to load near real-time data by using the messaging services provided by a cloud provider. Snowflake Connector for Kafka enables you to stream structured and semi-structured data from Apache Kafka topics into Snowflake tables. Snowpipe enables you to load data from files that are continuously added to a cloud storage location, such as Amazon S3 or Azure Blob Storage. Both methods leverage Snowflake’s micro-partitioning and columnar storage to optimize data ingestion and query performance. Snowflake streams and Spark are not ingestion methods, but rather components of the Snowflake architecture. Snowflake streams provide change data capture (CDC) functionality by tracking data changes in a table. Spark is a distributed computing framework that can be used to process large-scale data and write it to Snowflake using the Snowflake Spark Connector.
Reference: Snowflake Connector for Kafka
Snowpipe
Snowflake Streams
Snowflake Spark Connector
A
Explanation:
Snowflake Connector for Kafka and Snowpipe are two ingestion methods that can be used to load near real-time data by using the messaging services provided by a cloud provider. Snowflake Connector for Kafka enables you to stream structured and semi-structured data from Apache Kafka topics into Snowflake tables. Snowpipe enables you to load data from files that are continuously added to a cloud storage location, such as Amazon S3 or Azure Blob Storage. Both methods leverage Snowflake’s micro-partitioning and columnar storage to optimize data ingestion and query performance. Snowflake streams and Spark are not ingestion methods, but rather components of the Snowflake architecture. Snowflake streams provide change data capture (CDC) functionality by tracking data changes in a table. Spark is a distributed computing framework that can be used to process large-scale data and write it to Snowflake using the Snowflake Spark Connector.
Reference: Snowflake Connector for Kafka
Snowpipe
Snowflake Streams
Snowflake Spark Connector
Question #218
What are some of the characteristics of result set caches? (Choose three.)
- A . Time Travel queries can be executed against the result set cache.
- B . Snowflake persists the data results for 24 hours.
- C . Each time persisted results for a query are used, a 24-hour retention period is reset.
- D . The data stored in the result cache will contribute to storage costs.
- E . The retention period can be reset for a maximum of 31 days.
- F . The result set cache is not shared between warehouses.
Correct Answer: B, C, F
B, C, F
Explanation:
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.
Reference: Snowflake Documentation on Result Set Caching.
B, C, F
Explanation:
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.
Reference: Snowflake Documentation on Result Set Caching.
Question #219
What Snowflake features should be leveraged when modeling using Data Vault?
- A . Snowflake’s support of multi-table inserts into the data model’s Data Vault tables
- B . Data needs to be pre-partitioned to obtain a superior data access performance
- C . Scaling up the virtual warehouses will support parallel processing of new source loads
- D . Snowflake’s ability to hash keys so that hash key joins can run faster than integer joins
Correct Answer: A
A
Explanation:
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.
Reference: 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
A
Explanation:
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.
Reference: 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