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2025 Accurate ARA-C01: SnowPro Advanced Architect Certification Exam Sample Online

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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is an essential credential for professionals who want to demonstrate their expertise in Snowflake architecture. SnowPro Advanced Architect Certification certification program is designed to help professionals stay up-to-date with the latest industry trends and technologies, and it can help them advance their careers by demonstrating their skills and knowledge to potential employers.

Snowflake ARA-C01 Certification Exam is not for the faint-hearted. It is a rigorous and challenging exam that requires a deep understanding of Snowflake architecture, data modeling, performance optimization, security, and administration. ARA-C01 exam consists of 60 multiple-choice questions that must be completed within 120 minutes. The passing score for the ARA-C01 exam is 80%, and candidates who pass the exam are awarded the SnowPro Advanced Architect Certification.

Snowflake SnowPro Advanced Architect Certification Sample Questions (Q25-Q30):

NEW QUESTION # 25
An Architect is designing a pipeline to stream event data into Snowflake using the Snowflake Kafka connector. The Architect's highest priority is to configure the connector to stream data in the MOST cost-effective manner.
Which of the following is recommended for optimizing the cost associated with the Snowflake Kafka connector?

A. Utilize a higher Buffer.flush.time in the connector configuration.
B. Utilize a lower Buffer.size.bytes in the connector configuration.
C. Utilize a lower Buffer.count.records in the connector configuration.
D. Utilize a higher Buffer.size.bytes in the connector configuration.

Answer: A

Explanation:
The minimum value supported for the buffer.flush.time property is 1 (in seconds). For higher average data flow rates, we suggest that you decrease the default value for improved latency. If cost is a greater concern than latency, you could increase the buffer flush time. Be careful to flush the Kafka memory buffer before it becomes full to avoid out of memory exceptions. https://docs.snowflake.com/en/user-guide/data-load-snowpipe-streaming-kafka

NEW QUESTION # 26
A company is designing a process for importing a large amount of loT JSON data from cloud storage into Snowflake. New sets of loT data get generated and uploaded approximately every 5 minutes.
Once the loT data is in Snowflake, the company needs up-to-date information from an external vendor to join to the data. This data is then presented to users through a dashboard that shows different levels of aggregation.
The external vendor is a Snowflake customer.
What solution will MINIMIZE complexity and MAXIMIZE performance?

A. 1. Create an external table over the JSON data in cloud storage.2. Create a task that runs every 5 minutes to run a transformation procedure on new data based on a saved timestamp.3. Ask the vendor to create a data share with the required data that can be imported into the company's Snowflake account.
4. Join the vendor's data back to the loT data using a transformation procedure.5. Create views over the larger dataset to perform the aggregations required by the dashboard.6. Give the views access to the dashboard tool.
B. 1. Create a Snowpipe to bring the JSON data into Snowflake.2. Use streams and tasks to trigger a transformation procedure when new JSON data arrives.3. Ask the vendor to create a data share with the required data that is then imported into the Snowflake account.4. Join the vendor's data back to the loT data in a transformation procedure5. Create materialized views over the larger dataset to perform the aggregations required by the dashboard.6. Give the materialized views access to the dashboard tool.
C. 1. Create an external table over the JSON data in cloud storage.2. Create a task that runs every 5 minutes to run a transformation procedure on new data, based on a saved timestamp.3. Ask the vendor to expose an API so an external function can be used to generate a call to join the data back to the loT data in the transformation procedure.4. Give the transformed table access to the dashboard tool.5.
Perform the aggregations on the dashboard tool.
D. 1. Create a Snowpipe to bring the JSON data into Snowflake.2. Use streams and tasks to trigger a transformation procedure when new JSON data arrives.3. Ask the vendor to expose an API so an external function call can be made to join the vendor's data back to the loT data in a transformation procedure.4. Create materialized views over the larger dataset to perform the aggregations required by the dashboard.5. Give the materialized views access to the dashboard tool.

Answer: B

Explanation:
Using Snowpipe for continuous, automated data ingestion minimizes the need for manual intervention and ensures that data is available in Snowflake promptly after it is generated. Leveraging Snowflake's data sharing capabilities allows for efficient and secure access to the vendor's data without the need for complex API integrations. Materialized views provide pre-aggregated data for fast access, which is ideal for dashboards that require high performance1234.
References =
*Snowflake Documentation on Snowpipe4
*Snowflake Documentation on Secure Data Sharing2
*Best Practices for Data Ingestion with Snowflake1

NEW QUESTION # 27
Following objects can be cloned in snowflake

A. External tables
B. Permanent table
C. Temporary table
D. Internal stages
E. Transient table

Answer: A,B,E

Explanation:
Snowflake supports cloning of various objects, such as databases, schemas, tables, stages, file formats, sequences, streams, tasks, and roles. Cloning creates a copy of an existing object in the system without copying the data or metadata. Cloning is also known as zero-copy cloning1.
Among the objects listed in the question, the following ones can be cloned in Snowflake:
Permanent table: A permanent table is a type of table that has a Fail-safe period and a Time Travel retention period of up to 90 days. A permanent table can be cloned using the CREATE TABLE ... CLONE command2. Therefore, option A is correct.
Transient table: A transient table is a type of table that does not have a Fail-safe period and can have a Time Travel retention period of either 0 or 1 day. A transient table can also be cloned using the CREATE TABLE ... CLONE command2. Therefore, option B is correct.
External table: An external table is a type of table that references data files stored in an external location, such as Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage. An external table can be cloned using the CREATE EXTERNAL TABLE ... CLONE command3. Therefore, option D is correct.
The following objects listed in the question cannot be cloned in Snowflake:
Temporary table: A temporary table is a type of table that is automatically dropped when the session ends or the current user logs out. Temporary tables do not support cloning4. Therefore, option C is incorrect.
Internal stage: An internal stage is a type of stage that is managed by Snowflake and stores files in Snowflake's internal cloud storage. Internal stages do not support cloning5. Therefore, option E is incorrect.

NEW QUESTION # 28
What considerations need to be taken when using database cloning as a tool for data lifecycle management in a development environment? (Select TWO).

A. Any pipes in the source are not cloned.
B. The clone inherits all granted privileges of all child objects in the source object, excluding the database.
C. The clone inherits all granted privileges of all child objects in the source object, including the database.
D. Any pipes in the source referring to external stages are not cloned.
E. Any pipes in the source referring to internal stages are not cloned.

Answer: A,D

NEW QUESTION # 29
Which SQL alter command will MAXIMIZE memory and compute resources for a Snowpark stored procedure when executed on the snowpark_opt_wh warehouse?

A. Option C
B. Option A
C. Option B
D. Option D

Answer: B

Explanation:
To maximize memory and compute resources for a Snowpark stored procedure, you need to set the MAX_CONCURRENCY_LEVEL parameter for the warehouse that executes the stored procedure. This parameter determines the maximum number of concurrent queries that can run on a single warehouse. By setting it to 16, you ensure that the warehouse can use all the available CPU cores and memory on a single node, which is the optimal configuration for Snowpark-optimized warehouses. This will improve the performance and efficiency of the stored procedure, as it will not have to share resources with other queries or nodes. The other options are incorrect because they either do not change the MAX_CONCURRENCY_LEVEL parameter, or they set it to a lower value than 16, which will reduce the memory and compute resources for the stored procedure. Reference:
[Snowpark-optimized Warehouses] 1
[Training Machine Learning Models with Snowpark Python] 2
[Snowflake Shorts: Snowpark Optimized Warehouses] 3

NEW QUESTION # 30
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