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Snowflake Updated ARA-C01 Exam Questions and Answers by aniyah

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Snowflake ARA-C01 Exam Overview :

Exam Name: SnowPro Advanced: Architect Certification Exam
Exam Code: ARA-C01 Dumps
Vendor: Snowflake Certification: SnowPro Advanced: Architect
Questions: 162 Q&A's Shared By: aniyah
Question 36

A table for IOT devices that measures water usage is created. The table quickly becomes large and contains more than 2 billion rows.

Questions 36

The general query patterns for the table are:

1. DeviceId, lOT_timestamp and Customerld are frequently used in the filter predicate for the select statement

2. The columns City and DeviceManuf acturer are often retrieved

3. There is often a count on Uniqueld

Which field(s) should be used for the clustering key?

Options:

A.

lOT_timestamp

B.

City and DeviceManuf acturer

C.

Deviceld and Customerld

D.

Uniqueld

Discussion
Question 37

You are a snowflake architect in an organization. The business team came to to deploy an use case which requires you to load some data which they can visualize through tableau. Everyday new data comes in and the old data is no longer required.

What type of table you will use in this case to optimize cost

Options:

A.

TRANSIENT

B.

TEMPORARY

C.

PERMANENT

Discussion
Question 38

Which query will identify the specific days and virtual warehouses that would benefit from a multi-cluster warehouse to improve the performance of a particular workload?

A)

Questions 38

B)

Questions 38

C)

Questions 38

D)

Questions 38

Options:

A.

Option A

B.

Option B

C.

Option C

D.

Option D

Discussion
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Question 39

A media company needs a data pipeline that will ingest customer review data into a Snowflake table, and apply some transformations. The company also needs to use Amazon Comprehend to do sentiment analysis and make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions.

The data pipeline needs to run continuously ang efficiently as new records arrive in the object storage leveraging event notifications. Also, the operational complexity, maintenance of the infrastructure, including platform upgrades and security, and the development effort should be minimal.

Which design will meet these requirements?

Options:

A.

Ingest the data using COPY INTO and use streams and tasks to orchestrate transformations. Export the data into Amazon S3 to do model inference with Amazon Comprehend and ingest the data back into a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.

B.

Ingest the data using Snowpipe and use streams and tasks to orchestrate transformations. Create an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.

C.

Ingest the data into Snowflake using Amazon EMR and PySpark using the Snowflake Spark connector. Apply transformations using another Spark job. Develop a python program to do model inference by leveraging the Amazon Comprehend text analysis API. Then write the results to a Snowflake table and create a listing in the Snowflake Marketplace to make the data available to other companies.

D.

Ingest the data using Snowpipe and use streams and tasks to orchestrate transformations. Export the data into Amazon S3 to do model inference with Amazon Comprehend and ingest the data back into a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.

Discussion
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