Snowflake Exam ARA-R01 Topic 3 Question 22 Discussion

Actual exam question for Snowflake's ARA-R01 exam

Question #: 22
Topic #: 3

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.

Show Suggested Answer

Suggested Answer: 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.

by Tamala at Sep 15, 2024, 04:56 AM

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Nan

1 months ago

I'd go with option C. Parallelizing the queries is the way to go, and using the timeframe info to split the table is a smart move.

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Herminia

11 days ago

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

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Louis

16 days ago

A) Create 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.

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Dorothy

18 days ago

C) Divide 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.

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Cherelle

1 months ago

Wait, they've been storing 10 years' worth of data? Somebody call the weather forecast police, that's a serious data hoarding issue!

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Ariel

2 months ago

Dividing the table by location address might work, but then you'd have to manage a lot of smaller tables. Sounds like a lot of extra work to me.

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Delisa

24 days ago

A: Dividing the table by location address might work, but then you'd have to manage a lot of smaller tables. Sounds like a lot of extra work to me.

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Louisa

27 days ago

C: Divide 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.

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Laquanda

1 months ago

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

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Paola

1 months ago

A: Create 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.

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Marsha

2 months ago

I think adding a search optimization service on the variant column is a good idea. It will make the queries more efficient, especially with the massive amount of data.

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Destiny

2 months ago

The most efficient solution would be to divide the table by year and process the queries in parallel. This way, the drivers can get the weather statistics faster.

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Cristal

29 days ago

A: Exactly, by dividing the data by year, the queries can be processed more efficiently and quickly.

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Lemuel

1 months ago

B: That sounds like a good idea. It would definitely help speed up the delivery of weather statistics to the drivers.

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Telma

1 months ago

A: Divide 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.

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