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100% Pass 2025 Snowflake ARA-C01: Fantastic Latest SnowPro Advanced Architect Certification Test Sample

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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is a professional accreditation designed for experienced data architects and engineers who specialize in building data solutions on the Snowflake platform. SnowPro Advanced Architect Certification certification validates an individual's expertise in designing and implementing complex data architectures that can handle the demands of modern businesses. ARA-C01 Exam covers a broad range of topics, including data modeling, data integration, security, scalability, and performance optimization.

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Snowflake SnowPro Advanced Architect Certification Sample Questions (Q141-Q146):

NEW QUESTION # 141
A company has several sites in different regions from which the company wants to ingest data.
Which of the following will enable this type of data ingestion?

A. The company should use a storage integration for the external stage.
B. The company should provision a reader account to each site and ingest the data through the reader accounts.
C. The company must replicate data between Snowflake accounts.
D. The company must have a Snowflake account in each cloud region to be able to ingest data to that account.

Answer: D

NEW QUESTION # 142
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 current_role targeting the role in use for the current session.
B. Set masking policy conditions using is_role_in_session targeting the role in use for the current account.
C. Assign the accountadmin role to the user who is executing the object.
D. Set masking policy conditions using invoker_role targeting the executing role in a SQL statement.
E. Determine if there are ownership privileges on the masking policy that would allow the use of any function.

Answer: A,D

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 # 143
Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported.
What could be causing this?

A. There were JSON nulls in the recent data imports.
B. The recent data imports contained fewer fields than usual.
C. There were variations in string lengths for the JSON values in the recent data imports.
D. The order of the keys in the JSON was changed.

Answer: C,D

Explanation:
Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported. This could be caused by the following factors:
* The order of the keys in the JSON was changed. Snowflake stores semi-structured data internally in a column-like structure for the most common elements, and the remainder in a leftovers-like column. The order of the keys in the JSON affects how Snowflake determines the common elements and how it optimizes the query performance. If the order of the keys in the JSON was changed, Snowflake might have to re-parse the data and re-organize the internal storage, which could result in slower query performance.
* There were variations in string lengths for the JSON values in the recent data imports. Non-native values, such as dates and timestamps, are stored as strings when loaded into a VARIANT column.
Operations on these values could be slower and also consume more space than when stored in a relational column with the corresponding data type. If there were variations in string lengths for the
* JSON values in the recent data imports, Snowflake might have to allocate more space and perform more conversions, which could also result in slower query performance.
The other options are not valid causes for poor query performance:
* There were JSON nulls in the recent data imports. Snowflake supports two types of null values in semi-structured data: SQL NULL and JSON null. SQL NULL means the value is missing or unknown, while JSON null means the value is explicitly set to null. Snowflake can distinguish between these two types of null values and handle them accordingly. Having JSON nulls in the recent data imports should not affect the query performance significantly.
* The recent data imports contained fewer fields than usual. Snowflake can handle semi-structured data with varying schemas and fields. Having fewer fields than usual in the recent data imports should not affect the query performance significantly, as Snowflake can still optimize the data ingestion and query execution based on the existing fields.
References:
* Considerations for Semi-structured Data Stored in VARIANT
* Snowflake Architect Training
* Snowflake query performance on unique element in variant column
* Snowflake variant performance

NEW QUESTION # 144
Which semi structured data function interprets an input string as a JSON document, producing a VARIANT value.

A. STRIP_JSON
B. PARSE_XML
C. PARSE_JSON

Answer: C

NEW QUESTION # 145
The following DDL command was used to create a task based on a stream:

Assuming MY_WH is set to auto_suspend - 60 and used exclusively for this task, which statement is true?

A. The warehouse MY_WH will automatically resize to accommodate the size of the stream.
B. The warehouse MY_WH will never suspend.
C. The warehouse MY_WH will be made active every five minutes to check the stream.
D. The warehouse MY_WH will only be active when there are results in the stream.

Answer: D

Explanation:
The warehouse MY_WH will only be active when there are results in the stream. This is because the task is created based on a stream, which means that the task will only be executed when there are new data in the stream. Additionally, the warehouse is set to auto_suspend - 60, which means that the warehouse will automatically suspend after 60 seconds of inactivity. Therefore, the warehouse will only be active when there are results in the stream. Reference:
[CREATE TASK | Snowflake Documentation]
[Using Streams and Tasks | Snowflake Documentation]
[CREATE WAREHOUSE | Snowflake Documentation]

NEW QUESTION # 146
......

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