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[General] Vce ARA-C01 File, Exam ARA-C01 Book

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【General】 Vce ARA-C01 File, Exam ARA-C01 Book

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Snowflake ARA-C01: SnowPro Advanced Architect Certification Exam is a comprehensive assessment designed to measure an individual's knowledge and expertise in advanced Snowflake architecture. It is the highest-level certification offered by Snowflake and is an essential step for professionals looking to advance their careers in the field of data warehousing and cloud computing.
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Snowflake ARA-C01 exam is a performance-based exam that tests the candidate’s ability to solve real-world problems using Snowflake’s cloud data platform. ARA-C01 exam consists of a series of scenarios that require the candidate to design, implement, and optimize Snowflake solutions based on specific business requirements. ARA-C01 Exam is conducted online and can be taken from anywhere in the world.
Snowflake SnowPro Advanced Architect Certification Sample Questions (Q10-Q15):NEW QUESTION # 10
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 DATABASE EDW TO USER USERJD1;
  • 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 SCHEMA EDW.STG_SCHEMA TO USER USER_01;
Answer: D
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. Reference:
Snowflake Documentation: CREATE MATERIALIZED VIEW
Snowflake Documentation: Working with Materialized Views
[Snowflake Documentation: GRANT Privileges on a Schema]

NEW QUESTION # 11
While creating a clustering key, what is the recommendation for maximum number of columns that you can include as part of the key?
  • A. 0
  • B. Unlimited
  • C. Not more than 16
  • D. 3 to 4
Answer: D

NEW QUESTION # 12
How can the Snowflake context functions be used to help determine whether a user is authorized to see data that has column-level security enforced? (Select TWO).
  • A. Set masking policy conditions using invoker_role targeting the executing role in a SQL statement.
  • B. Assign the accountadmin role to the user who is executing the object.
  • C. Set masking policy conditions using current_role targeting the role in use for the current session.
  • D. Determine if there are ownership privileges on the masking policy that would allow the use of any function.
  • E. Set masking policy conditions using is_role_in_session targeting the role in use for the current account.
Answer: A,C
Explanation:
Snowflake context functions are functions that return information about the current session, user, role, warehouse, database, schema, or object. They can be used to help determine whether a user is authorized to see data that has column-level security enforced by setting masking policy conditions based on the context functions. The following context functions are relevant for column-level security:
* current_role: This function returns the name of the role in use for the current session. It can be used to set masking policy conditions that target the current session and are not affected by the execution context of the SQL statement. For example, a masking policy condition using current_role can allow or deny access to a column based on the role that the user activated in the session.
* invoker_role: This function returns the name of the executing role in a SQL statement. It can be used to set masking policy conditions that target the executing role and are affected by the execution context of the SQL statement. For example, a masking policy condition using invoker_role can allow or deny access to a column based on the role that the user specified in the SQL statement, such as using the AS ROLE clause or a stored procedure.
* is_role_in_session: This function returns TRUE if the user's current role in the session (i.e. the role returned by current_role) inherits the privileges of the specified role. It can be used to set masking policy conditions that involve role hierarchy and privilege inheritance. For example, a masking policy condition using is_role_in_session can allow or deny access to a column based on whether the user's current role is a lower privilege role in the specified role hierarchy.
The other options are not valid ways to use the Snowflake context functions for column-level security:
* Set masking policy conditions using is_role_in_session targeting the role in use for the current account.
This option is incorrect because is_role_in_session does not target the role in use for the current account, but rather the role in use for the current session. Also, the current account is not a role, but rather a logical entity that contains users, roles, warehouses, databases, and other objects.
* Determine if there are ownership privileges on the masking policy that would allow the use of any function. This option is incorrect because ownership privileges on the masking policy do not affect the use of any function, but rather the ability to create, alter, or drop the masking policy. Also, this is not a way to use the Snowflake context functions, but rather a way to check the privileges on the masking policy object.
* Assign the accountadmin role to the user who is executing the object. This option is incorrect because assigning the accountadmin role to the user who is executing the object does not involve using the Snowflake context functions, but rather granting the highest-level role to the user. Also, this is not a recommended practice for column-level security, as it would give the user full access to all objects and data in the account, which could compromise data security and governance.
Context Functions
Advanced Column-level Security topics
Snowflake Data Governance: Column Level Security Overview
Data Security Snowflake Part 2 - Column Level Security

NEW QUESTION # 13
Assuming all Snowflake accounts are using an Enterprise edition or higher, in which development and testing scenarios would be copying of data be required, and zero-copy cloning not be suitable? (Select TWO).
  • A. Developers create their own datasets to work against transformed versions of the live data.
  • B. Developers create their own copies of a standard test database previously created for them in the development account, for their initial development and unit testing.
  • C. Data is in a production Snowflake account that needs to be provided to Developers in a separate development/testing Snowflake account in the same cloud region.
  • D. The release process requires pre-production testing of changes with data of production scale and complexity. For security reasons, pre-production also runs in the production account.
  • E. Production and development run in different databases in the same account, and Developers need to see production-like data but with specific columns masked.
Answer: A,C
Explanation:
Zero-copy cloning is a feature that allows creating a clone of a table, schema, or database without physically copying the data. Zero-copy cloning is suitable for scenarios where the cloned object needs to have the same data and metadata as the original object, and where the cloned object does not need to be modified or updated frequently. Zero-copy cloning is also suitable for scenarios where the cloned object needs to be shared within the same Snowflake account or across different accounts in the same cloud region2 However, zero-copy cloning is not suitable for scenarios where the cloned object needs to have different data or metadata than the original object, or where the cloned object needs to be modified or updated frequently. Zero-copy cloning is also not suitable for scenarios where the cloned object needs to be shared across different accounts in different cloud regions. In these scenarios, copying of data would be required, either by using the COPY INTO command or by using data sharing with secure views3 The following are examples of development and testing scenarios where copying of data would be required, and zero-copy cloning would not be suitable:
Developers create their own datasets to work against transformed versions of the live data. This scenario requires copying of data because the developers need to modify the data or metadata of the cloned object to perform transformations, such as adding, deleting, or updating columns, rows, or values. Zero-copy cloning would not be suitable because it would create a read-only clone that shares the same data and metadata as the original object, and any changes made to the clone would affect the original object as well4 Data is in a production Snowflake account that needs to be provided to Developers in a separate development/testing Snowflake account in the same cloud region. This scenario requires copying of data because the data needs to be shared across different accounts in the same cloud region. Zero-copy cloning would not be suitable because it would create a clone within the same account as the original object, and it would not allow sharing the clone with another account. To share data across different accounts in the same cloud region, data sharing with secure views or COPY INTO command can be used5 The following are examples of development and testing scenarios where zero-copy cloning would be suitable, and copying of data would not be required:
Production and development run in different databases in the same account, and Developers need to see production-like data but with specific columns masked. This scenario can use zero-copy cloning because the data needs to be shared within the same account, and the cloned object does not need to have different data or metadata than the original object. Zero-copy cloning can create a clone of the production database in the development database, and the clone can have the same data and metadata as the original database. To mask specific columns, secure views can be created on top of the clone, and the developers can access the secure views instead of the clone directly6 Developers create their own copies of a standard test database previously created for them in the development account, for their initial development and unit testing. This scenario can use zero-copy cloning because the data needs to be shared within the same account, and the cloned object does not need to have different data or metadata than the original object. Zero-copy cloning can create a clone of the standard test database for each developer, and the clone can have the same data and metadata as the original database. The developers can use the clone for their initial development and unit testing, and any changes made to the clone would not affect the original database or other clones7 The release process requires pre-production testing of changes with data of production scale and complexity. For security reasons, pre-production also runs in the production account. This scenario can use zero-copy cloning because the data needs to be shared within the same account, and the cloned object does not need to have different data or metadata than the original object. Zero-copy cloning can create a clone of the production database in the pre-production database, and the clone can have the same data and metadata as the original database. The pre-production testing can use the clone to test the changes with data of production scale and complexity, and any changes made to the clone would not affect the original database or the production environment8 Reference:
1: SnowPro Advanced: Architect | Study Guide 9
2: Snowflake Documentation | Cloning Overview
3: Snowflake Documentation | Loading Data Using COPY into a Table
4: Snowflake Documentation | Transforming Data During a Load
5: Snowflake Documentation | Data Sharing Overview
6: Snowflake Documentation | Secure Views
7: Snowflake Documentation | Cloning Databases, Schemas, and Tables
8: Snowflake Documentation | Cloning for Testing and Development
9: SnowPro Advanced: Architect | Study Guide
10: Cloning Overview
11: Loading Data Using COPY into a Table
12: Transforming Data During a Load
13: Data Sharing Overview
14: Secure Views
15: Cloning Databases, Schemas, and Tables
16: Cloning for Testing and Development

NEW QUESTION # 14
Which data models can be used when modeling tables in a Snowflake environment? (Select THREE).
  • A. Dimensional/Kimball
  • B. Graph model
  • C. Data vault
  • D. lnmon/3NF
  • E. Data lake
  • F. Bayesian hierarchical model
Answer: A,C,D
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.
References: What is Data Modeling? | Snowflake, Snowflake Schema in Data Warehouse Model - GeeksforGeeks, [Data Vault 2.0 Modeling with Snowflake]

NEW QUESTION # 15
......
Exam ARA-C01 Book: https://www.realexamfree.com/ARA-C01-real-exam-dumps.html
P.S. Free 2026 Snowflake ARA-C01 dumps are available on Google Drive shared by RealExamFree: https://drive.google.com/open?id=1kFG0aMxDTgDFDJ2SrepipLls1JWLN4Lv
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