Snowflake ARA-C01 Practice Exams
Last updated on Oct 21,2026- Exam Code: ARA-C01
- Exam Name: SnowPro Advanced Architect Certification
- Certification Provider: Snowflake
- Latest update: Oct 21,2026
Question #110
Which of the below commands will use warehouse credits?
- A . SHOW TABLES LIKE ‘SNOWFL%’;
- B . SELECT MAX(FLAKE_ID) FROM SNOWFLAKE;
- C . SELECT COUNT(*) FROM SNOWFLAKE;
- D . SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID;
Correct Answer: B, C, D
B, C, D
Explanation:
Warehouse credits are used to pay for the processing time used by each virtual warehouse in Snowflake. A virtual warehouse is a cluster of compute resources that enables executing queries, loading data, and performing other DML operations. Warehouse credits are charged based on the number of virtual warehouses you use, how long they run, and their size1.
Among the commands listed in the question, the following ones will use warehouse credits:
SELECT MAX(FLAKE_ID) FROM SNOWFLAKE: This command will use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the maximum value of the FLAKE_ID column2. Therefore, option B is correct.
SELECT COUNT(*) FROM SNOWFLAKE: This command will also use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and
return the number of rows in the table3. Therefore, option C is correct.
SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID: This command will also use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the number of rows for each distinct value of the FLAKE_ID column4. Therefore, option D is correct.
The command that will not use warehouse credits is:
SHOW TABLES LIKE ‘SNOWFL%’: This command will not use warehouse credits because it is a metadata operation that does not require a virtual warehouse to execute. The command will return the names of the tables that match the pattern ‘SNOWFL%’ in the current database and schema5. Therefore, option A is incorrect.
Reference: Understanding Compute Cost: MAX Function: COUNT Function: GROUP BY
Clause: SHOW TABLES
B, C, D
Explanation:
Warehouse credits are used to pay for the processing time used by each virtual warehouse in Snowflake. A virtual warehouse is a cluster of compute resources that enables executing queries, loading data, and performing other DML operations. Warehouse credits are charged based on the number of virtual warehouses you use, how long they run, and their size1.
Among the commands listed in the question, the following ones will use warehouse credits:
SELECT MAX(FLAKE_ID) FROM SNOWFLAKE: This command will use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the maximum value of the FLAKE_ID column2. Therefore, option B is correct.
SELECT COUNT(*) FROM SNOWFLAKE: This command will also use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and
return the number of rows in the table3. Therefore, option C is correct.
SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID: This command will also use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the number of rows for each distinct value of the FLAKE_ID column4. Therefore, option D is correct.
The command that will not use warehouse credits is:
SHOW TABLES LIKE ‘SNOWFL%’: This command will not use warehouse credits because it is a metadata operation that does not require a virtual warehouse to execute. The command will return the names of the tables that match the pattern ‘SNOWFL%’ in the current database and schema5. Therefore, option A is incorrect.
Reference: Understanding Compute Cost: MAX Function: COUNT Function: GROUP BY
Clause: SHOW TABLES
Question #112
What actions are permitted when using the Snowflake SQL REST API? (Select TWO).
- A . The use of a GET command
- B . The use of a PUT command
- C . The use of a ROLLBACK command
- D . The use of a CALL command to a stored procedure which returns a table
- E . Submitting multiple SQL statements in a single call
Correct Answer: AD
AD
Explanation:
AD
Explanation:
Question #112
What actions are permitted when using the Snowflake SQL REST API? (Select TWO).
- A . The use of a GET command
- B . The use of a PUT command
- C . The use of a ROLLBACK command
- D . The use of a CALL command to a stored procedure which returns a table
- E . Submitting multiple SQL statements in a single call
Correct Answer: AD
AD
Explanation:
AD
Explanation:
Question #114
Which of the following are characteristics of how row access policies can be applied to external tables? (Choose three.)
- A . An external table can be created with a row access policy, and the policy can be applied to the VALUE column.
- B . A row access policy can be applied to the VALUE column of an existing external table.
- C . A row access policy cannot be directly added to a virtual column of an external table.
- D . External tables are supported as mapping tables in a row access policy.
- E . While cloning a database, both the row access policy and the external table will be cloned.
- F . A row access policy cannot be applied to a view created on top of an external table.
Correct Answer: A, B, C
A, B, C
Explanation:
These three statements are true according to the Snowflake documentation and the web search results. A row access policy is a feature that allows filtering rows based on user-defined conditions. A row access policy can be applied to an external table, which is a table that reads data from external files in a stage. However, there are some limitations and considerations for using row access policies with external tables.
An external table can be created with a row access policy by using the WITH ROW ACCESS POLICY clause in the CREATE EXTERNAL TABLE statement. The policy can be applied to the VALUE column, which is the column that contains the raw data from the external files in a VARIANT data type1.
A row access policy can also be applied to the VALUE column of an existing external table by using the ALTER TABLE statement with the SET ROW ACCESS POLICY clause2.
A row access policy cannot be directly added to a virtual column of an external table. A virtual column is a column that is derived from the VALUE column using an expression. To apply a row access policy to a virtual column, the policy must be applied to the VALUE column and the expression must be repeated in the policy definition3.
External tables are not supported as mapping tables in a row access policy. A mapping table is a table that is used to determine the access rights of users or roles based on some criteria. Snowflake does not support using an external table as a mapping table because it may cause performance issues or errors4.
While cloning a database, Snowflake clones the row access policy, but not the external table. Therefore, the policy in the cloned database refers to a table that is not present in the cloned database. To avoid this issue, the external table must be manually cloned or recreated in the cloned database4.
A row access policy can be applied to a view created on top of an external table. The policy can be applied to the view itself or to the underlying external table. However, if the policy is applied to the view, the view must be a secure view, which is a view that hides the underlying data and the view definition from unauthorized users5.
Reference: CREATE EXTERNAL TABLE | Snowflake Documentation ALTER EXTERNAL TABLE | Snowflake Documentation Understanding Row Access Policies | Snowflake Documentation Snowflake Data Governance: Row Access Policy Overview Secure Views | Snowflake Documentation
A, B, C
Explanation:
These three statements are true according to the Snowflake documentation and the web search results. A row access policy is a feature that allows filtering rows based on user-defined conditions. A row access policy can be applied to an external table, which is a table that reads data from external files in a stage. However, there are some limitations and considerations for using row access policies with external tables.
An external table can be created with a row access policy by using the WITH ROW ACCESS POLICY clause in the CREATE EXTERNAL TABLE statement. The policy can be applied to the VALUE column, which is the column that contains the raw data from the external files in a VARIANT data type1.
A row access policy can also be applied to the VALUE column of an existing external table by using the ALTER TABLE statement with the SET ROW ACCESS POLICY clause2.
A row access policy cannot be directly added to a virtual column of an external table. A virtual column is a column that is derived from the VALUE column using an expression. To apply a row access policy to a virtual column, the policy must be applied to the VALUE column and the expression must be repeated in the policy definition3.
External tables are not supported as mapping tables in a row access policy. A mapping table is a table that is used to determine the access rights of users or roles based on some criteria. Snowflake does not support using an external table as a mapping table because it may cause performance issues or errors4.
While cloning a database, Snowflake clones the row access policy, but not the external table. Therefore, the policy in the cloned database refers to a table that is not present in the cloned database. To avoid this issue, the external table must be manually cloned or recreated in the cloned database4.
A row access policy can be applied to a view created on top of an external table. The policy can be applied to the view itself or to the underlying external table. However, if the policy is applied to the view, the view must be a secure view, which is a view that hides the underlying data and the view definition from unauthorized users5.
Reference: CREATE EXTERNAL TABLE | Snowflake Documentation ALTER EXTERNAL TABLE | Snowflake Documentation Understanding Row Access Policies | Snowflake Documentation Snowflake Data Governance: Row Access Policy Overview Secure Views | Snowflake Documentation
Question #115
What is a characteristic of event notifications in Snowpipe?
- A . The load history is stored In the metadata of the target table.
- B . Notifications identify the cloud storage event and the actual data in the files.
- C . Snowflake can process all older notifications when a paused pipe Is resumed.
- D . When a pipe Is paused, event messages received for the pipe enter a limited retention period.
Correct Answer: D
D
Explanation:
Event notifications in Snowpipe are messages sent by cloud storage providers to notify Snowflake of new or modified files in a stage. Snowpipe uses these notifications to trigger data loading from the stage to the target table. When a pipe is paused, event messages received for the pipe enter a limited retention period, which varies depending on the cloud storage provider. If the pipe is not resumed within the retention period, the event messages will be discarded and the data will not be loaded automatically. To load the data, the pipe must be resumed and the COPY command must be executed manually. This is a characteristic of event notifications in Snowpipe that distinguishes them from other options.
Reference: Snowflake Documentation: Using Snowpipe, Snowflake Documentation: Pausing and Resuming a Pipe
D
Explanation:
Event notifications in Snowpipe are messages sent by cloud storage providers to notify Snowflake of new or modified files in a stage. Snowpipe uses these notifications to trigger data loading from the stage to the target table. When a pipe is paused, event messages received for the pipe enter a limited retention period, which varies depending on the cloud storage provider. If the pipe is not resumed within the retention period, the event messages will be discarded and the data will not be loaded automatically. To load the data, the pipe must be resumed and the COPY command must be executed manually. This is a characteristic of event notifications in Snowpipe that distinguishes them from other options.
Reference: Snowflake Documentation: Using Snowpipe, Snowflake Documentation: Pausing and Resuming a Pipe
Question #116
A company has a Snowflake account named ACCOUNTA in AWS us-east-1 region. The company stores its marketing data in a Snowflake database named MARKET_DB. One of the company’s business partners has an account named PARTNERB in Azure East US 2 region. For marketing purposes the company has agreed to share the database MARKET_DB with the partner account.
Which of the following steps MUST be performed for the account PARTNERB to consume data from the MARKET_DB database?
- A . Create a new account (called AZABC123) in Azure East US 2 region. From account ACCOUNTA create a share of database MARKET_DB, create a new database out of this share locally in AWS us-east-1 region, and replicate this new database to AZABC123 account. Then set up data sharing to the PARTNERB account.
- B . From account ACCOUNTA create a share of database MARKET_DB, and create a new database out of this share locally in AWS us-east-1 region. Then make this database the provider and share it with the PARTNERB account.
- C . Create a new account (called AZABC123) in Azure East US 2 region. From account ACCOUNTA replicate the database MARKET_DB to AZABC123 and from this account set up the data sharing to
the PARTNERB account. - D . Create a share of database MARKET_DB, and create a new database out of this share locally in AWS us-east-1 region. Then replicate this database to the partner’s account PARTNERB.
Correct Answer: C
C
Explanation:
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: Create a new account (called AZABC123) in Azure East US 2 region. This account will act as a bridge between the source and the target accounts. The new account must be linked to the ACCOUNTA account using an organization2.
From the ACCOUNTA account, replicate the MARKET_DB database to the AZABC123 account using the account replication feature. This will create a secondary database in the AZABC123 account that is a replica of the primary database in the ACCOUNTA account3.
From the AZABC123 account, set up the data sharing to the PARTNERB account using the share replication feature. This will create a share of the secondary database in the AZABC123 account and grant access to the PARTNERB account. The PARTNERB account can then create a database from the share and query the data4.
Therefore, option C is the correct answer.
Reference: Replicating Shares Across Regions and Cloud Platforms: Working with Organizations and
Accounts: Replicating Databases Across Multiple Accounts: Replicating Shares Across Multiple Accounts
C
Explanation:
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: Create a new account (called AZABC123) in Azure East US 2 region. This account will act as a bridge between the source and the target accounts. The new account must be linked to the ACCOUNTA account using an organization2.
From the ACCOUNTA account, replicate the MARKET_DB database to the AZABC123 account using the account replication feature. This will create a secondary database in the AZABC123 account that is a replica of the primary database in the ACCOUNTA account3.
From the AZABC123 account, set up the data sharing to the PARTNERB account using the share replication feature. This will create a share of the secondary database in the AZABC123 account and grant access to the PARTNERB account. The PARTNERB account can then create a database from the share and query the data4.
Therefore, option C is the correct answer.
Reference: Replicating Shares Across Regions and Cloud Platforms: Working with Organizations and
Accounts: Replicating Databases Across Multiple Accounts: Replicating Shares Across Multiple Accounts
Question #117
Which data models can be used when modeling tables in a Snowflake environment? (Select THREE).
- A . Graph model
- B . Dimensional/Kimball
- C . Data Lake
- D . lnmon/3NF
- E . Bayesian hierarchical model
- F . Data vault
Correct Answer: B, D, F
B, D, F
Explanation:
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.
Reference: What is Data Modeling? | Snowflake, Snowflake Schema in Data Warehouse Model – GeeksforGeeks, [Data Vault 2.0 Modeling with Snowflake]
B, D, F
Explanation:
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.
Reference: What is Data Modeling? | Snowflake, Snowflake Schema in Data Warehouse Model – GeeksforGeeks, [Data Vault 2.0 Modeling with Snowflake]
Question #118
Files arrive in an external stage every 10 seconds from a proprietary system. The files range in size from 500 K to 3 MB. The data must be accessible by dashboards as soon as it arrives.
How can a Snowflake Architect meet this requirement with the LEAST amount of coding? (Choose two.)
- A . Use Snowpipe with auto-ingest.
- B . Use a COPY command with a task.
- C . Use a materialized view on an external table.
- D . Use the COPY INTO command.
- E . Use a combination of a task and a stream.
Correct Answer: A, E
A, E
Explanation:
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.
A, E
Explanation:
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.
Question #119
Which Snowflake objects can be used in a data share? (Select TWO).
- A . Standard view
- B . Secure view
- C . Stored procedure
- D . External table
- E . Stream
Correct Answer: B, D
B, D
Explanation:
https://docs.snowflake.com/en/user-guide/data-sharing-intro
B, D
Explanation:
https://docs.snowflake.com/en/user-guide/data-sharing-intro
Question #119
Which Snowflake objects can be used in a data share? (Select TWO).
- A . Standard view
- B . Secure view
- C . Stored procedure
- D . External table
- E . Stream
Correct Answer: B, D
B, D
Explanation:
https://docs.snowflake.com/en/user-guide/data-sharing-intro
B, D
Explanation:
https://docs.snowflake.com/en/user-guide/data-sharing-intro