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Snowflake ARA-C01 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	SnowPro Advanced: Architect Certification Exam
Exam Number:	ARA-C01
Available Languages:	English
Passing Score:	750 / 1000
Real Exam Qty:	65
Exam Format:	Multiple Select, Multiple Choice
Exam Duration:	115 minutes
Certificate Validity Period:	2 years
Exam Price:	USD 375
Related Certifications:	SnowPro Advanced: Data Analyst SnowPro Advanced: Data Engineer SnowPro Core
Recommended Training:	Snowflake Documentation Snowflake Advanced Architect Learning Path
Exam Registration:	Snowflake Certification Portal Pearson VUE Registration
Sample Questions:	Snowflake ARA-C01 Sample Questions
Exam Way:	Online proctored or Onsite testing center
Pre Condition:	Recommended: SnowPro Core certification + 2+ years hands-on experience as Snowflake Architect in production environment
Official Syllabus URL:	https://learn.snowflake.com/en/certifications/snowpro-advanced-architect/

>> Snowflake ARA-C01 Reliable Practice Materials <<

Quiz Snowflake - ARA-C01 - Valid SnowPro Advanced Architect Certification Reliable Practice Materials

We provide Snowflake ARA-C01 web-based self-assessment practice software that will help you to prepare for the Snowflake SnowPro Advanced Architect Certification exam. Snowflake ARA-C01 Web-based software offers computer-based assessment solutions to help you automate the entire SnowPro Advanced Architect Certification exam testing procedure. The stylish and user-friendly interface works with all browsers, including Mozilla Firefox, Google Chrome, Opera, Safari, and Internet Explorer. It will make your Snowflake ARA-C01 Exam Preparation simple, quick, and smart. So, rest certain that you will discover all you need to study for and pass the Snowflake ARA-C01 exam on the first try.

Snowflake ARA-C01 certification exam is a comprehensive test that covers a wide range of topics related to Snowflake. ARA-C01 exam is designed to test the candidate's ability to design, implement, and manage complex data solutions on the Snowflake platform. ARA-C01 Exam is also intended to evaluate the candidate's ability to integrate Snowflake with other technologies and to optimize performance and scalability.

Snowflake SnowPro Advanced Architect Certification Sample Questions (Q140-Q145):

NEW QUESTION # 140
An Architect has a table called leader_follower that contains a single column named JSON. The table has one row with the following structure:
{
"activities": [
{ "activityNumber": 1, "winner": 5 },
{ "activityNumber": 2, "winner": 4 }
],
"follower": {
"name": { "default": "Matt" },
"number": 4
},
"leader": {
"name": { "default": "Adam" },
"number": 5
}
}
Which query will produce the following results?
ACTIVITY_NUMBER
WINNER_NAME
1
Adam
2
Matt

A. value:activityNumber AS activity_number,
IFF(
B. value:activityNumber AS activity_number,
IFF(
C. value:winner = lf.json:leader.number,
lf.json:leader.name.default,
lf.json:follower.name.default
)::VARCHAR AS winner_name
FROM leader_follower lf,
LATERAL FLATTEN(input => json:activities) p;
D. value:winner = lf.json:leader.number,
lf.json:leader,
lf.json:follower
)::VARCHAR AS winner_name
FROM leader_follower lf,
LATERAL FLATTEN(input => json:activities) p;
E. value:winner = lf.json:leader.number,
lf.json:leader.name.default,
lf.json:follower.name.default
)::VARCHAR AS winner_name
FROM leader_follower lf,
LATERAL FLATTEN(input => json:activities, outer => TRUE) p;
F. SELECT
G. SELECT
H. SELECT lf.json:activities.activityNumber AS activity_number,
IFF(
lf.json:activities.activityNumber = lf.json:leader.number,
lf.json:leader.name.default,
lf.json:follower.name.default
)::VARCHAR
FROM leader_follower lf;
I. value:activityNumber AS activity_number,
IFF(
J. SELECT

Answer: J

Explanation:
This question tests several core Snowflake semi-structured data concepts that are explicitly part of the SnowPro Architect exam scope: working with VARIANT data, array handling, and the use of LATERAL FLATTEN. The activities element in the JSON structure is an array, meaning it must be flattened before individual attributes such as activityNumber and winner can be accessed. Option A is invalid because it attempts to directly reference fields inside an array without flattening it.
Option B correctly uses LATERAL FLATTEN on json:activities, which produces one row per activity. The alias p.value represents each array element, allowing access to activityNumber and winner. The IFF expression then compares the activity's winner value with leader.number. When they match, the query returns leader.name.default; otherwise, it returns follower.name.default. Casting the result to VARCHAR ensures a proper scalar output.
Option C is incorrect because it attempts to return full JSON objects (leader or follower) rather than the nested name.default value. Option D uses OUTER => TRUE, which is unnecessary in this case because the activities array is guaranteed to exist; while it would still work, Snowflake exam questions typically expect the most precise and minimal correct solution.

NEW QUESTION # 141
How is the change of local time due to daylight savings time handled in Snowflake tasks? (Choose two.)

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

Answer: C,E

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.
Snowflake Documentation: Scheduling Tasks
Snowflake Community: Do the timezones used in scheduling tasks in Snowflake adhere to daylight savings?

NEW QUESTION # 142
An Architect is designing a file ingestion recovery solution. The project will use an internal named stage for file storage. Currently, in the case of an ingestion failure, the Operations team must manually download the failed file and check for errors.
Which downloading method should the Architect recommend that requires the LEAST amount of operational overhead?

A. Use the get command in SnowSQL to retrieve the file.
B. Use the Snowflake Connector for Python, connect to remote storage and download the file.
C. Use the get command in Snowsight to retrieve the file.
D. Use the Snowflake API endpoint and download the file.

Answer: A

Explanation:
The get command in SnowSQL is a convenient way to download files from an internal stage to a local directory. The get command can be used in interactive mode or in a script, and it supports wildcards and parallel downloads. The get command also allows specifying the overwrite option, which determines how to handle existing files with the same name2 The Snowflake Connector for Python, the Snowflake API endpoint, and the get command in Snowsight are not recommended methods for downloading files from an internal stage, because they require more operational overhead than the get command in SnowSQL. The Snowflake Connector for Python and the Snowflake API endpoint require writing and maintaining code to handle the connection, authentication, and file transfer. The get command in Snowsight requires using the web interface and manually selecting the files to download34 Reference:
1: SnowPro Advanced: Architect | Study Guide
2: Snowflake Documentation | Using the GET Command
3: Snowflake Documentation | Using the Snowflake Connector for Python
4: Snowflake Documentation | Using the Snowflake API
: Snowflake Documentation | Using the GET Command in Snowsight
: SnowPro Advanced: Architect | Study Guide
: Using the GET Command
: Using the Snowflake Connector for Python
: Using the Snowflake API
: [Using the GET Command in Snowsight]

NEW QUESTION # 143
A Snowflake Architect is designing a multi-tenant application strategy for an organization in the Snowflake Data Cloud and is considering using an Account Per Tenant strategy.
Which requirements will be addressed with this approach? (Choose two.)

A. Compute costs must be optimized.
B. Tenant data shape may be unique per tenant.
C. There needs to be fewer objects per tenant.
D. Security and Role-Based Access Control (RBAC) policies must be simple to configure.
E. Storage costs must be optimized.

Answer: B,D

Explanation:
The Account Per Tenant strategy involves creating separate Snowflake accounts for each tenant within the multi-tenant application. This approach offers a number of advantages.
Option B: With separate accounts, each tenant's environment is isolated, making security and RBAC policies simpler to configure and maintain. This is because each account can have its own set of roles and privileges without the risk of cross-tenant access or the complexity of maintaining a highly granular permission model within a shared environment.
Option D: This approach also allows for each tenant to have a unique data shape, meaning that the database schema can be tailored to the specific needs of each tenant without affecting others. This can be essential when tenants have different data models, usage patterns, or application customizations.

NEW QUESTION # 144
When a database gets cloned, what accesses are replicated

A. All objects in the database and their child objects(schemas, tables etc)
B. Only the data base object access
C. Only the child object access
D. No access gets replicated

Answer: A

NEW QUESTION # 145
......

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