Snowflake ARA-C01 Exam Questions - Navigate Your Path to Success

The Snowflake SnowPro Advanced: Architect Certification Exam (ARA-C01) exam is a good choice and if the candidate manages to pass Snowflake SnowPro Advanced: Architect Certification Exam, he/she will earn Snowflake SnowPro Certification. Below are some essential facts for Snowflake ARA-C01 exam candidates:

In actual Snowflake SnowPro Advanced: Architect Certification Exam (ARA-C01) exam, a candidate can expect 65 Questions and the officially allowed time is expected to be around 115 Minutes.
TrendyCerts offers 162 Questions that are based on actual Snowflake ARA-C01 syllabus.
Our Snowflake ARA-C01 Exam Practice Questions were last updated on: Mar 07, 2025

Sample Questions for Snowflake ARA-C01 Exam Preparation

Question 1

An Architect has a design where files arrive every 10 minutes and are loaded into a primary database table using Snowpipe. A secondary database is refreshed every hour with the latest data from the primary database.

Based on this scenario, what Time Travel query options are available on the secondary database?

AA query using Time Travel in the secondary database is available for every hourly table version within the retention window.

BA query using Time Travel in the secondary database is available for every hourly table version within and outside the retention window.

CUsing Time Travel, secondary database users can query every iterative version within each hour (the individual Snowpipe loads) in the retention window.

DUsing Time Travel, secondary database users can query every iterative version within each hour (the individual Snowpipe loads) and outside the retention window.

Correct : A

Snowflake's Time Travel feature allows users to query historical data within a defined retention period. In the given scenario, since the secondary database is refreshed every hour, Time Travel can be used to query each hourly version of the table as long as it falls within the retention window. This does not include individual Snowpipe loads within each hour unless they coincide with the hourly refresh.

Options Selected by Other Users:

Question 2

An Architect for a multi-national transportation company has a system that is used to check the weather conditions along vehicle routes. The data is provided to drivers.

The weather information is delivered regularly by a third-party company and this information is generated as JSON structure. Then the data is loaded into Snowflake in a column with a VARIANT data type. This

table is directly queried to deliver the statistics to the drivers with minimum time lapse.

A single entry includes (but is not limited to):

- Weather condition; cloudy, sunny, rainy, etc.

- Degree

- Longitude and latitude

- Timeframe

- Location address

- Wind

The table holds more than 10 years' worth of data in order to deliver the statistics from different years and locations. The amount of data on the table increases every day.

The drivers report that they are not receiving the weather statistics for their locations in time.

What can the Architect do to deliver the statistics to the drivers faster?

ACreate an additional table in the schema for longitude and latitude. Determine a regular task to fill this information by extracting it from the JSON dataset.

BAdd search optimization service on the variant column for longitude and latitude in order to query the information by using specific metadata.

CDivide the table into several tables for each year by using the timeframe information from the JSON dataset in order to process the queries in parallel.

DDivide the table into several tables for each location by using the location address information from the JSON dataset in order to process the queries in parallel.

Correct : B

To improve the performance of queries on semi-structured data, such as JSON stored in a VARIANT column, Snowflake's search optimization service can be utilized. By adding search optimization specifically for the longitude and latitude fields within the VARIANT column, the system can perform point lookups and substring queries more efficiently. This will allow for faster retrieval of weather statistics, which is critical for the drivers to receive timely updates.