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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is an industry-recognized certification that validates the advanced skills and knowledge of professionals in designing and implementing Snowflake solutions. It is a comprehensive exam that covers a wide range of topics, including Snowflake architecture, security, performance optimization, data modeling, and integration with other technologies. SnowPro Advanced Architect Certification certification is ideal for professionals who are responsible for managing Snowflake solutions for their organizations and want to demonstrate their expertise in this area.

Snowflake SnowPro Advanced Architect Certification Sample Questions (Q36-Q41):

NEW QUESTION # 36
A user is executing the following command sequentially within a timeframe of 10 minutes from start to finish:

What would be the output of this query?

A. Table T_SALES_CLONE successfully created.
B. Syntax error line 1 at position 58 unexpected 'at'.
C. The offset -> is not a valid clause in the clone operation.
D. Time Travel data is not available for table T_SALES.

Answer: A

Explanation:
The query is executing a clone operation on an existing table t_sales with an offset to account for the retention time. The syntax used is correct for cloning a table in Snowflake, and the use of the at(offset => -60*30) clause is valid. This specifies that the clone should be based on the state of the table 30 minutes prior (60 seconds * 30). Assuming the table t_sales exists and has been modified within the last 30 minutes, and considering the data_retention_time_in_days is set to 1 day (which enables time travel queries for the past
24 hours), the table t_sales_clone would be successfully created based on the state of t_sales 30 minutes before the clone command was issued.

NEW QUESTION # 37
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. Determine if there are ownership privileges on the masking policy that would allow the use of any function.
B. Set masking policy conditions using invoker_role targeting the executing role in a SQL statement.
C. Set masking policy conditions using is_role_in_session targeting the role in use for the current account.
D. Assign the accountadmin role to the user who is executing the object.
E. Set masking policy conditions using current_role targeting the role in use for the current session.

Answer: B,E

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.
References:
* Context Functions
* Advanced Column-level Security topics
* Snowflake Data Governance: Column Level Security Overview
* Data Security Snowflake Part 2 - Column Level Security

NEW QUESTION # 38
Which data models can be used when modeling tables in a Snowflake environment? (Select THREE).

A. Data lake
B. lnmon/3NF
C. Data vault
D. Graph model
E. Dimensional/Kimball
F. Bayesian hierarchical model

Answer: B,C,E

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.
What is Data Modeling? | Snowflake, Snowflake Schema in Data Warehouse Model - GeeksforGeeks, [Data Vault 2.0 Modeling with Snowflake]

NEW QUESTION # 39
select metadata$filename, metadata$file_row_number from @filestage/data1.json.gz;
Please select the correct statements for the above-mentioned query.

A. FILESTAGE is the stage name, METADATA$FILE_ROW_NUMBER will give the path to the data file in the stage
B. FILESTAGE is the file name, METADATA$FILE_ROW_NUMBER will give the path to the data file in the stage
C. FILESTAGE is the stage name, METADATA$FILE_ROW_NUMBER will give the row number for each record in the container staged data file

Answer: C

NEW QUESTION # 40
What will the below query return
SELECT TOP 10 GRADES FROM STUDENT;

A. The 10 lowest grades
B. The top 10 highest grades
C. Non-deterministic list of 10 grades

Answer: C

NEW QUESTION # 41
......

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