100% Pass Quiz 2026 ARA-C01 - SnowPro Advanced Architect Certification Dump File

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Snowflake ARA-C01 certification exam is designed to test a candidate's knowledge and skills related to Snowflake's advanced architectural concepts. It is a rigorous exam that requires candidates to have a strong understanding of Snowflake's architecture, data modeling, performance tuning, security, and data integration. ARA-C01 Exam is divided into multiple sections, each of which covers a specific topic related to Snowflake's architecture. Candidates must demonstrate their proficiency in each section to earn their certification.

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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.

Snowflake ARA-C01 Certification Exam is a globally recognized certification that demonstrates a candidate's advanced proficiency in Snowflake architecture, data modeling, and performance optimization. SnowPro Advanced Architect Certification certification is highly valued in the industry and provides a competitive advantage to individuals who hold it. The Snowflake ARA-C01 certification exam is an excellent way for Snowflake architects to demonstrate their expertise and advance their careers in the field of data warehousing.

Snowflake SnowPro Advanced Architect Certification Sample Questions (Q37-Q42):

NEW QUESTION # 37
Role A has the following permissions:
. USAGE on db1
. USAGE and CREATE VIEW on schemal in db1
. SELECT on tablel in schemal
Role B has the following permissions:
. USAGE on db2
. USAGE and CREATE VIEW on schema2 in db2
. SELECT on table2 in schema2
A user has Role A set as the primary role and Role B as a secondary role.
What command will fail for this user?

A. use database db2;
use schema schema2;
create view v2 as select * from dbl.schemal. tablel;
B. use database db2;
use schema schema2;
select * from db1.schemal.tablel union select * from table2;
C. use database db1;
use schema schemal;
select * from db2.schema2.table2;
D. use database db1;
use schema schemal;
create view v1 as select * from db2.schema2.table2;

Answer: A

Explanation:
This command will fail because while the user has USAGE permission on db2 and schema2 through Role B, and can create a view in schema2, they do not have SELECT permission on db1.schemal.table1 with Role B. Since Role A, which has SELECT permission on db1.schemal.table1, is not the currently active role when the view v2 is being created in db2.schema2, the user does not have the necessary permissions to read from db1.schemal.table1 to create the view. Snowflake's security model requires that the active role have all necessary permissions to execute the command.

NEW QUESTION # 38
A global company needs to securely share its sales and Inventory data with a vendor using a Snowflake account.
The company has its Snowflake account In the AWS eu-west 2 Europe (London) region. The vendor's Snowflake account Is on the Azure platform in the West Europe region. How should the company's Architect configure the data share?

A. 1. Create a new role called db_share.
2. Grant the db_share role privileges to read data from the company database and schema.
3. Create a user for the vendor.
4. Grant the ds_share role to the vendor's users.
B. 1. Promote an existing database in the company's local account to primary.
2. Replicate the database to Snowflake on Azure in the West-Europe region.
3. Create a share and add objects to the share.
4. Add a consumer account to the share for the vendor to access.
C. 1. Create a share.
2. Create a reader account for the vendor to use.
3. Add the reader account to the share.
D. 1. Create a share.
2. Add objects to the share.
3. Add a consumer account to the share for the vendor to access.

Answer: D

Explanation:
The correct way to securely share data with a vendor using a Snowflake account on a different cloud platform and region is to create a share, add objects to the share, and add a consumer account to the share for the vendor to access. This way, the company can control what data is shared, who can access it, and how long the share is valid. The vendor can then query the shared data without copying or moving it to their own account. The other options are either incorrect or inefficient, as they involve creating unnecessary reader accounts, users, roles, or database replication.
https://learn.snowflake.com/en/certifications/snowpro-advanced-architect/

NEW QUESTION # 39
Which technique will efficiently ingest and consume semi-structured data for Snowflake data lake workloads?

A. IDEF1X
B. Information schema
C. Schema-on-write
D. Schema-on-read

Answer: D

Explanation:
Option C is the correct answer because schema-on-read is a technique that allows Snowflake to ingest and consume semi-structured data without requiring a predefined schema. Snowflake supports various semi- structured data formats such as JSON, Avro, ORC, Parquet, and XML, and provides native data types (ARRAY, OBJECT, and VARIANT) for storing them. Snowflake also provides native support for querying semi-structured data using SQL and dot notation. Schema-on-read enables Snowflake to query semi- structured data at the same speed as performing relational queries while preserving the flexibility of schema- on-read. Snowflake's near-instant elasticity rightsizes compute resources, and consumption-based pricing ensures you only pay for what you use.
Option A is incorrect because IDEF1X is a data modeling technique that defines the structure and constraints of relational data using diagrams and notations. IDEF1X is not suitable for ingesting and consuming semi- structured data, which does not have a fixed schema or structure.
Option B is incorrect because schema-on-write is a technique that requires defining a schema before loading and processing data. Schema-on-write is not efficient for ingesting and consuming semi-structured data, which may have varying or complex structures that are difficult to fit into a predefined schema. Schema-on- write also introduces additional overhead and complexity for data transformation and validation.
Option D is incorrect because information schema is a set of metadata views that provide information about the objects and privileges in a Snowflake database. Information schema is not a technique for ingesting and consuming semi-structured data, but rather a way of accessing metadata about the data.
References:
Semi-structured Data
Snowflake for Data Lake

NEW QUESTION # 40
How is the change of local time due to daylight savings time handled in Snowflake tasks? (Choose two.)

A. A task will move to a suspended state during the daylight savings time change.
B. A task schedule will follow only the specified time and will fail to handle lost or duplicated hours.
C. Task schedules can be designed to follow specified or local time zones to accommodate the time changes.
D. A frequent task execution schedule like minutes may not cause a problem, but will affect the task history.
E. A task scheduled in a UTC-based schedule will have no issues with the time changes.

Answer: C,E

Explanation:
Explanation
According to the Snowflake documentation1 and the web search results2, these two statements are true about how the change of local time due to daylight savings time is handled in Snowflake tasks. A task is a feature that allows scheduling and executing SQL statements or stored procedures in Snowflake. A task can be scheduled using a cron expression that specifies the frequency and time zone of the task execution.
* A task scheduled in a UTC-based schedule will have no issues with the time changes. UTC is a universal time standard that does not observe daylight savings time. Therefore, a task that uses UTC as the time zone will run at the same time throughout the year, regardless of the local time changes1.
* Task schedules can be designed to follow specified or local time zones to accommodate the time changes. Snowflake supports using any valid IANA time zone identifier in the cron expression for a task. This allows the task to run according to the local time of the specified time zone, which may include daylight savings time adjustments. For example, a task that uses Europe/London as the time zone will run one hour earlier or later when the local time switches between GMT and BST12.
References:
* Snowflake Documentation: Scheduling Tasks
* Snowflake Community: Do the timezones used in scheduling tasks in Snowflake adhere to daylight savings?

NEW QUESTION # 41
Which of the below commands will use warehouse credits?

A. SELECT COUNT(*) FROM SNOWFLAKE;
B. SHOW TABLES LIKE 'SNOWFL%';
C. SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID;
D. SELECT MAX(FLAKE_ID) FROM SNOWFLAKE;

Answer: A,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.
Understanding Compute Cost : MAX Function : COUNT Function : GROUP BY Clause : SHOW TABLES

NEW QUESTION # 42
......

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100% Pass Quiz 2026 ARA-C01 - SnowPro Advanced Architect Certification Dump File

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Snowflake ARA-C01 Sample Test Online, Test ARA-C01 Online

Snowflake SnowPro Advanced Architect Certification Sample Questions (Q37-Q42):

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