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ARA-R01 Exam Dumps : SnowPro Advanced: Architect Recertification Exam

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SnowPro Advanced: Architect Recertification Exam Questions and Answers

Question 1

A company has several sites in different regions from which the company wants to ingest data.

Which of the following will enable this type of data ingestion?

Options:

A.

The company must have a Snowflake account in each cloud region to be able to ingest data to that account.

B.

The company must replicate data between Snowflake accounts.

C.

The company should provision a reader account to each site and ingest the data through the reader accounts.

D.

The company should use a storage integration for the external stage.

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Question 2

A table contains five columns and it has millions of records. The cardinality distribution of the columns is shown below:

Column C4 and C5 are mostly used by SELECT queries in the GROUP BY and ORDER BY clauses. Whereas columns C1, C2 and C3 are heavily used in filter and join conditions of SELECT queries.

The Architect must design a clustering key for this table to improve the query performance.

Based on Snowflake recommendations, how should the clustering key columns be ordered while defining the multi-column clustering key?

Options:

A.

C5, C4, C2

B.

C3, C4, C5

C.

C1, C3, C2

D.

C2, C1, C3

Question 3

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 and 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.