Get Ready with ARA-C01 Exam Dumps (2025) [Q15-Q30]

Get Ready with ARA-C01 Exam Dumps (2025)

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Snowflake ARA-C01 certification exam, also known as the SnowPro Advanced Architect Certification, is designed for professionals who want to demonstrate their expertise in the Snowflake cloud data platform. ARA-C01 exam is intended for individuals who have already earned the SnowPro Core Certification and have advanced knowledge and experience working with Snowflake. The Snowflake ARA-C01 certification exam is a comprehensive and rigorous test that covers a wide range of topics related to Snowflake architecture, design, implementation, optimization, and security.

 

NEW QUESTION # 15
A user, analyst_user has been granted the analyst_role, and is deploying a SnowSQL script to run as a background service to extract data from Snowflake.
What steps should be taken to allow the IP addresses to be accessed? (Select TWO).

• A. ALTERUSERANALYST_USERSETNETWORK_POLICY='10.1.1.20';
• B. USE ROLE SECURITYADMIN;CREATE OR REPLACE NETWORK POLICY
  ANALYST_POLICY ALLOWED_IP_LIST = ('10.1.1.20');
• C. USE ROLE USERADMIN;CREATE OR REPLACE NETWORK POLICYANALYST_POLICYALLOWED_IP_LIST = ('10.1.1.20');
• D. ALTERUSERANALYSTJJSERSETNETWORK_POLICY='ANALYST_POLICY';
• E. ALTERROLEANALYST_ROLESETNETWORK_POLICY='ANALYST_POLICY';

Answer: B,D

Explanation:
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.
References:Snowflake's security management documentation covering network policies and role-based access controls.

NEW QUESTION # 16
An Architect is troubleshooting a query with poor performance using the QUERY_HIST0RY function. The Architect observes that the COMPILATIONJHME is greater than the EXECUTIONJTIME.
What is the reason for this?

• A. The query is processing a very large dataset.
• B. The query is queued for execution.
• C. The query has overly complex logic.
• D. The query is reading from remote storage.

Answer: C

Explanation:
Compilation time is the time it takes for the optimizer to create an optimal query plan for the efficient execution of the query. It also involves some pruning of partition files, making the query execution efficient2 If the compilation time is greater than the execution time, it means that the optimizer spent more time analyzing the query than actually running it. This could indicate that the query has overly complex logic, such as multiple joins, subqueries, aggregations, or expressions. The complexity of the query could also affect the size and quality of the query plan, which could impact the performance of the query3 To reduce the compilation time, the Architect can try to simplify the query logic, use views or common table expressions (CTEs) to break down the query into smaller parts, or use hints to guide the optimizer. The Architect can also use the EXPLAIN command to examine the query plan and identify potential bottlenecks or inefficiencies4 Reference:
1: SnowPro Advanced: Architect | Study Guide 5
2: Snowflake Documentation | Query Profile Overview 6
3: Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time 7
4: Snowflake Documentation | Optimizing Query Performance 8
5: SnowPro Advanced: Architect | Study Guide
6: Query Profile Overview
7: Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time
8: Optimizing Query Performance

NEW QUESTION # 17
A data platform team creates two multi-cluster virtual warehouses with the AUTO_SUSPEND value set to NULL on one. and '0' on the other. What would be the execution behavior of these virtual warehouses?

• A. Setting a '0' or NULL value means the warehouses will suspend after the default of 600 seconds.
• B. Setting a '0' or NULL value means the warehouses will suspend immediately.
• C. Setting a '0' or NULL value means the warehouses will never suspend.
• D. Setting a '0' value means the warehouses will suspend immediately, and NULL means the warehouses will never suspend.

Answer: D

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

NEW QUESTION # 18
An Architect has been asked to clone schema STAGING as it looked one week ago, Tuesday June 1st at 8:00 AM, to recover some objects.
The STAGING schema has 50 days of retention.
The Architect runs the following statement:
CREATE SCHEMA STAGING_CLONE CLONE STAGING at (timestamp => '2021-06-01 08:00:00'); The Architect receives the following error: Time travel data is not available for schema STAGING. The requested time is either beyond the allowed time travel period or before the object creation time.
The Architect then checks the schema history and sees the following:
CREATED_ON|NAME|DROPPED_ON
2021-06-02 23:00:00 | STAGING | NULL
2021-05-01 10:00:00 | STAGING | 2021-06-02 23:00:00
How can cloning the STAGING schema be achieved?

• A. Undrop the STAGING schema and then rerun the CLONE statement.
• B. Modify the statement: CREATE SCHEMA STAGING_CLONE CLONE STAGING at (timestamp =>
  '2021-05-01 10:00:00');
• C. Rename the STAGING schema and perform an UNDROP to retrieve the previous STAGING schema version, then run the CLONE statement.
• D. Cloning cannot be accomplished because the STAGING schema version was not active during the proposed Time Travel time period.

Answer: C

Explanation:
* The error message indicates that the schema STAGING does not have time travel data available for the requested timestamp, because the current version of the schema was created on 2021-06-02 23:00:00, which is after the timestamp of 2021-06-01 08:00:00. Therefore, the CLONE statement cannot access
* the historical data of the schema at that point in time.
* Option A is incorrect, because undropping the STAGING schema will not restore the previous version of the schema that was active on 2021-06-01 08:00:00. Instead, it will create a new version of the schema with the same name and no data or objects.
* Option B is incorrect, because modifying the timestamp to 2021-05-01 10:00:00 will not clone the schema as it looked one week ago, but as it looked when it was first created. This may not reflect the desired state of the schema and its objects.
* Option C is correct, because renaming the STAGING schema and performing an UNDROP to retrieve the previous STAGING schema version will restore the schema that was dropped on 2021-06-02
23:00:00. This schema has time travel data available for the requested timestamp of 2021-06-01
08:00:00, and can be cloned using the CLONE statement.
* Option D is incorrect, because cloning can be accomplished by using the UNDROP command to access the previous version of the schema that was active during the proposed time travel period.
References: : Cloning Considerations : Understanding & Using Time Travel : CREATE <object> ... CLONE

NEW QUESTION # 19
Which Snowflake data modeling approach is designed for BI queries?

• A. 3 NF
• B. Data Vault
• C. Snowflake schema
• D. Star schema

Answer: C

NEW QUESTION # 20
An Architect has chosen to separate their Snowflake Production and QA environments using two separate Snowflake accounts.
The QA account is intended to run and test changes on data and database objects before pushing those changes to the Production account. It is a requirement that all database objects and data in the QA account need to be an exact copy of the database objects, including privileges and data in the Production account on at least a nightly basis.
Which is the LEAST complex approach to use to populate the QA account with the Production account's data and database objects on a nightly basis?

• A. 1) In the Production account, create an external function that connects into the QA account and returns all the data for one specific table
  2) Run the external function as part of a stored procedure that loops through each table in the Production account and populates each table in the QA account
• B. 1) Create a stage in the Production account
  2) Create a stage in the QA account that points to the same external object-storage location
  3) Create a task that runs nightly to unload each table in the Production account into the stage
  4) Use Snowpipe to populate the QA account
• C. 1) Enable replication for each database in the Production account
  2) Create replica databases in the QA account
  3) Create clones of the replica databases on a nightly basis
  4) Run tests directly on those cloned databases
• D. 1) Create a share in the Production account for each database
  2) Share access to the QA account as a Consumer
  3) The QA account creates a database directly from each share
  4) Create clones of those databases on a nightly basis
  5) Run tests directly on those cloned databases

Answer: C

Explanation:
* 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

NEW QUESTION # 21
Which security, governance, and data protection features require, at a MINIMUM, the Business Critical edition of Snowflake? (Choose two.)

• A. AWS, Azure, or Google Cloud private connectivity to Snowflake
• B. Federated authentication and SSO
• C. Periodic rekeying of encrypted data
• D. Customer-managed encryption keys through Tri-Secret Secure
• E. Extended Time Travel (up to 90 days)

Answer: A,D

Explanation:
Explanation
According to the SnowPro Advanced: Architect documents and learning resources, the security, governance, and data protection features that require, at a minimum, the Business Critical edition of Snowflake are:
* Customer-managed encryption keys through Tri-Secret Secure. This feature 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. This provides an additional layer of security and control over the data encryption and decryption process1.
* Periodic rekeying of encrypted data. This feature allows customers to periodically rotate the encryption keys for data at rest in Snowflake, using either Snowflake-managed keys or customer-managed keys. This enhances the security and protection of the data by reducing the risk of key compromise or exposure2.
The other options are incorrect because they do not require the Business Critical edition of Snowflake. Option A is incorrect because extended Time Travel (up to 90 days) is available with the Enterprise edition of Snowflake3. Option D is incorrect because AWS, Azure, or Google Cloud private connectivity to Snowflake is available with the Standard edition of Snowflake4. Option E is incorrect because federated authentication and SSO are available with the Standard edition of Snowflake5. References: Tri-Secret Secure | Snowflake Documentation, Periodic Rekeying of Encrypted Data | Snowflake Documentation, Snowflake Editions | Snowflake Documentation, Snowflake Network Policies | Snowflake Documentation, Configuring Federated Authentication and SSO | Snowflake Documentation

NEW QUESTION # 22
Which of the below are securable objects?

• A. PRIVILEDGE
• B. DATABASE
• C. USER
• D. ROLE
• E. TABLE

Answer: B,C,D,E

NEW QUESTION # 23
A Snowflake Architect is designing an application and tenancy strategy for an organization where strong legal isolation rules as well as multi-tenancy are requirements.
Which approach will meet these requirements if Role-Based Access Policies (RBAC) is a viable option for isolating tenants?

• A. Create a multi-tenant table strategy if row level security is not viable for isolating tenants.
• B. Create accounts for each tenant in the Snowflake organization.
• C. Create an object for each tenant strategy if row level security is not viable for isolating tenants.
• D. Create an object for each tenant strategy if row level security is viable for isolating tenants.

Answer: B

Explanation:
In a scenario where strong legal isolation is required alongside the need for multi-tenancy, the most effective approach is to create separate accounts for each tenant within the Snowflake organization. This approach ensures complete isolation of data, resources, and management, adhering to strict legal and compliance requirements. Role-Based Access Control (RBAC) further enhances security by allowing granular control over who can access what resources within each account. This solution leverages Snowflake's capabilities for managing multiple accounts under a single organization umbrella, ensuring that each tenant's data and operations are isolated from others.
Reference: Snowflake documentation on multi-tenancy and account management, part of the SnowPro Advanced: Architect learning path.

NEW QUESTION # 24
A retail company has over 3000 stores all using the same Point of Sale (POS) system. The company wants to deliver near real-time sales results to category managers. The stores operate in a variety of time zones and exhibit a dynamic range of transactions each minute, with some stores having higher sales volumes than others.
Sales results are provided in a uniform fashion using data engineered fields that will be calculated in a complex data pipeline. Calculations include exceptions, aggregations, and scoring using external functions interfaced to scoring algorithms. The source data for aggregations has over 100M rows.
Every minute, the POS sends all sales transactions files to a cloud storage location with a naming convention that includes store numbers and timestamps to identify the set of transactions contained in the files. The files are typically less than 10MB in size.
How can the near real-time results be provided to the category managers? (Select TWO).

• A. All files should be concatenated before ingestion into Snowflake to avoid micro-ingestion.
• B. An external scheduler should examine the contents of the cloud storage location and issue SnowSQL commands to process the data at a frequency that matches the real-time analytics needs.
• C. A Snowpipe should be created and configured with AUTO_INGEST = true. A stream should be created to process INSERTS into a single target table using the stream metadata to inform the store number and timestamps.
• D. The copy into command with a task scheduled to run every second should be used to achieve the near-real time requirement.
• E. A stream should be created to accumulate the near real-time data and a task should be created that runs at a frequency that matches the real-time analytics needs.

Answer: C,E

Explanation:
To provide near real-time sales results to category managers, the Architect can use the following steps:
* Create an external stage that references the cloud storage location where the POS sends the sales transactions files. The external stage should use the file format and encryption settings that match the source files2
* Create a Snowpipe that loads the files from the external stage into a target table in Snowflake. The Snowpipe should be configured with AUTO_INGEST = true, which means that it will automatically detect and ingest new files as they arrive in the external stage. The Snowpipe should also use a copy option to purge the files from the external stage after loading, to avoid duplicate ingestion3
* Create a stream on the target table that captures the INSERTS made by the Snowpipe. The stream should include the metadata columns that provide information about the file name, path, size, and last modified time. The stream should also have a retention period that matches the real-time analytics needs4
* Create a task that runs a query on the stream to process the near real-time data. The query should use the stream metadata to extract the store number and timestamps from the file name and path, and perform the calculations for exceptions, aggregations, and scoring using external functions. The query should also output the results to another table or view that can be accessed by the category managers. The task should be scheduled to run at a frequency that matches the real-time analytics needs, such as every minute or every 5 minutes.
The other options are not optimal or feasible for providing near real-time results:
* All files should be concatenated before ingestion into Snowflake to avoid micro-ingestion. This option is not recommended because it would introduce additional latency and complexity in the data pipeline.
Concatenating files would require an external process or service that monitors the cloud storage location and performs the file merging operation. This would delay the ingestion of new files into Snowflake and increase the risk of data loss or corruption. Moreover, concatenating files would not avoid micro-ingestion, as Snowpipe would still ingest each concatenated file as a separate load.
* An external scheduler should examine the contents of the cloud storage location and issue SnowSQL commands to process the data at a frequency that matches the real-time analytics needs. This option is not necessary because Snowpipe can automatically ingest new files from the external stage without requiring an external trigger or scheduler. Using an external scheduler would add more overhead and dependency to the data pipeline, and it would not guarantee near real-time ingestion, as it would depend on the polling interval and the availability of the external scheduler.
* The copy into command with a task scheduled to run every second should be used to achieve the near-real time requirement. This option is not feasible because tasks cannot be scheduled to run every second in Snowflake. The minimum interval for tasks is one minute, and even that is not guaranteed, as tasks are subject to scheduling delays and concurrency limits. Moreover, using the copy into command with a task would not leverage the benefits of Snowpipe, such as automatic file detection, load balancing, and micro-partition optimization. References:
* 1: SnowPro Advanced: Architect | Study Guide
* 2: Snowflake Documentation | Creating Stages
* 3: Snowflake Documentation | Loading Data Using Snowpipe
* 4: Snowflake Documentation | Using Streams and Tasks for ELT
* : Snowflake Documentation | Creating Tasks
* : Snowflake Documentation | Best Practices for Loading Data
* : Snowflake Documentation | Using the Snowpipe REST API
* : Snowflake Documentation | Scheduling Tasks
* : SnowPro Advanced: Architect | Study Guide
* : Creating Stages
* : Loading Data Using Snowpipe
* : Using Streams and Tasks for ELT
* : [Creating Tasks]
* : [Best Practices for Loading Data]
* : [Using the Snowpipe REST API]
* : [Scheduling Tasks]

NEW QUESTION # 25
What are characteristics of the use of transactions in Snowflake? (Select TWO).

• A. The autocommit setting can be changed inside a stored procedure.
• B. Explicit transactions should contain only DML statements and query statements. All DDL statements implicitly commit active transactions.
• C. A transaction can be started explicitly by executing a begin transaction statement and end explicitly by executing an end transaction statement.
• D. A transaction can be started explicitly by executing a begin work statement and end explicitly by executing a commit work statement.
• E. Explicit transactions can contain DDL, DML, and query statements.

Answer: C,E

Explanation:
In Snowflake, a transaction is a sequence of SQL statements that are processed as an atomic unit. All statements in the transaction are either applied (i.e. committed) or undone (i.e. rolled back) together.
Snowflake transactions guarantee ACID properties. A transaction can include both reads and writes1.
Explicit transactions are transactions that are started and ended explicitly by using the BEGIN TRANSACTION, COMMIT, and ROLLBACK statements. Snowflake supports the synonyms BEGIN WORK and BEGIN TRANSACTION, and COMMIT WORK and ROLLBACK WORK. Explicit transactions can contain DDL, DML, and query statements. However, explicit transactions should contain only DML statements and query statements, because DDL statements implicitly commit active transactions. This means that any changes made by the previous statements in the transaction are applied, and any changes made by the subsequent statements in the transaction are not part of the same transaction1.
The other options are not correct because:
* B. The autocommit setting can be changed inside a stored procedure, but this does not affect the use of transactions in Snowflake. The autocommit setting determines whether each statement is executed in its own implicit transaction or not. If autocommit is enabled, each statement is committed automatically. If autocommit is disabled, each statement is executed in an implicit transaction until an explicit COMMIT or ROLLBACK is issued. Changing the autocommit setting inside a stored procedure only affects the statements within the stored procedure, and does not affect the statements outside the stored procedure2.
* C. A transaction can be started explicitly by executing a BEGIN WORK statement and end explicitly by executing a COMMIT WORK statement, but this is not a characteristic of the use of transactions in Snowflake. This is just one way of writing the statements that start and end an explicit transaction. Snowflake also supports the synonyms BEGIN TRANSACTION and COMMIT, which are recommended over BEGIN WORK and COMMIT WORK1.
* D. A transaction can be started explicitly by executing a BEGIN TRANSACTION statement and end explicitly by executing an END TRANSACTION statement, but this is not a valid syntax in Snowflake.
Snowflake does not support the END TRANSACTION statement. The correct way to end an explicit transaction is to use the COMMIT or ROLLBACK statement1.
References:
* 1: Transactions | Snowflake Documentation
* 2: AUTOCOMMIT | Snowflake Documentation

NEW QUESTION # 26
Which Snowflake data modeling approach is designed for BI queries?

• A. 3 NF
• B. Data Vault
• C. Star schema
• D. Snowflake schema

Answer: C

Explanation:
A star schema is a Snowflake data modeling approach that is designed for BI queries. A star schema is a type of dimensional modeling that organizes data into fact tables and dimension tables. A fact table contains the measures or metrics of the business process, such as sales amount, order quantity, or profit margin. A dimension table contains the attributes or descriptors of the business process, such as product name, customer name, or order date. A star schema is called so because it resembles a star, with one fact table in the center and multiple dimension tables radiating from it. A star schema can improve the performance and simplicity of BI queries by reducing the number of joins, providing fast access to aggregated data, and enabling intuitive query syntax. A star schema can also support various types of analysis, such as trend analysis, slice and dice, drill down, and roll up12.
References:
* Snowflake Documentation: Dimensional Modeling
* Snowflake Documentation: Star Schema

NEW QUESTION # 27
The endpoint insertFiles of SnowPipe rest API is used to inform Snowflake about the files to be ingested into a table. Select the two statements for this end point.

• A. The post can contain at most 5000 files.
• B. The post can contain unlimited number of files.
• C. Each file path given must be <= 1024 bytes long when serialized as UTF-8

Answer: A,C

NEW QUESTION # 28
A user named USER_01 needs access to create a materialized view on a schema EDW. STG_SCHEMA. How can this access be provided?

• A. GRANT CREATE MATERIALIZED VIEW ON SCHEMA EDW.STG_SCHEMA TO USER
  USER_01;
• B. GRANT ROLE NEW_ROLE TO USER_01;GRANT CREATE MATERIALIZED VIEW ON EDW.STG_SCHEMA TO NEW_ROLE;
• C. GRANT ROLE NEW_ROLE TO USER USER_01;GRANT CREATE MATERIALIZED VIEW ON SCHEMA ECW.STG_SCHEKA TO NEW_ROLE;
• D. GRANT CREATE MATERIALIZED VIEW ON DATABASE EDW TO USER USERJD1;

Answer: A

Explanation:
* The correct answer is A because it grants the specific privilege to create a materialized view on the schema EDW.STG_SCHEMA to the user USER_01 directly.
* Option B is incorrect because it grants the privilege to create a materialized view on the entire database EDW, which is too broad and unnecessary. Also, there is a typo in the user name (USERJD1 instead of USER_01).
* Option C is incorrect because it grants the privilege to create a materialized view on a different schema (ECW.STG_SCHEKA instead of EDW.STG_SCHEMA). Also, there is no need to create a new role for this purpose.
* Option D is incorrect because it grants the privilege to create a materialized view on an invalid object (EDW.STG_SCHEMA is not a valid schema name, it should be EDW.STG_SCHEMA). Also, there is no need to create a new role for this purpose. References:
* Snowflake Documentation: CREATE MATERIALIZED VIEW
* Snowflake Documentation: Working with Materialized Views
* [Snowflake Documentation: GRANT Privileges on a Schema]

NEW QUESTION # 29
In this scenarios, files are constantly getting ingested in an external stage, the files are between 1 and 4 MB. You want to load the data immediately into a table without any human intervention.
What will you use?

• A. Create a materialize view on the external stage
• B. Use Snowpipe with auto ingest
• C. Use Snowpipe rest APIs

Answer: B

NEW QUESTION # 30
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