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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 needs to share its product catalog data with one of its partners. The product catalog data is stored in two database tables: product_category, and product_details. Both tables can be joined by the product_id column. Data access should be governed, and only the partner should have access to the records.

The partner is not a Snowflake customer. The partner uses Amazon S3 for cloud storage.

Which design will be the MOST cost-effective and secure, while using the required Snowflake features?

Options:

A.

Use Secure Data Sharing with an S3 bucket as a destination.

B.

Publish product_category and product_details data sets on the Snowflake Marketplace.

C.

Create a database user for the partner and give them access to the required data sets.

D.

Create a reader account for the partner and share the data sets as secure views.

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Question 2

Company A has recently acquired company B. The Snowflake deployment for company B is located in the Azure West Europe region.

As part of the integration process, an Architect has been asked to consolidate company B's sales data into company A's Snowflake account which is located in the AWS us-east-1 region.

How can this requirement be met?

Options:

A.

Replicate the sales data from company B's Snowflake account into company A's Snowflake account using cross-region data replication within Snowflake. Configure a direct share from company B's account to company A's account.

B.

Export the sales data from company B's Snowflake account as CSV files, and transfer the files to company A's Snowflake account. Import the data using Snowflake's data loading capabilities.

C.

Migrate company B's Snowflake deployment to the same region as company A's Snowflake deployment, ensuring data locality. Then perform a direct database-to-database merge of the sales data.

D.

Build a custom data pipeline using Azure Data Factory or a similar tool to extract the sales data from company B's Snowflake account. Transform the data, then load it into company A's Snowflake account.

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.