Online Access Free ARA-R01 Exam Questions

A DevOps team has a requirement for recovery of staging tables used in a complex set of data pipelines. The staging tables are all located in the same staging schema. One of the requirements is to have online recovery of data on a rolling 7-day basis.
After setting up the DATA_RETENTION_TIME_IN_DAYS at the database level, certain tables remain unrecoverable past 1 day.
What would cause this to occur? (Choose two.)

• A.The tables exceed the 1 TB limit for data recovery.
• B.The DATA_RETENTION_TIME_IN_DAYS for the staging schema has been set to 1 day.
• C.The staging schema has not been setup for MANAGED ACCESS.
• D.The staging tables are of the TRANSIENT type.
• E.The DevOps role should be granted ALLOW_RECOVERY privilege on the staging schema.

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?

• 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.

A large manufacturing company runs a dozen individual Snowflake accounts across its business divisions. The company wants to increase the level of data sharing to support supply chain optimizations and increase its purchasing leverage with multiple vendors.
The company's Snowflake Architects need to design a solution that would allow the business divisions to decide what to share, while minimizing the level of effort spent on configuration and management. Most of the company divisions use Snowflake accounts in the same cloud deployments with a few exceptions for European-based divisions.
According to Snowflake recommended best practice, how should these requirements be met?

• A.Deploy to the Snowflake Marketplace making sure that invoker_share() is used in all secure views.
• B.Deploy a Private Data Exchange and use replication to allow European data shares in the Exchange.
• C.Migrate the European accounts in the global region and manage shares in a connected graph architecture. Deploy a Data Exchange.
• D.Deploy a Private Data Exchange in combination with data shares for the European accounts.

What built-in Snowflake features make use of the change tracking metadata for a table? (Choose two.)

• A.The CHANGES clause
• B.A STREAM object
• C.The MERGE command
• D.The UPSERT command
• E.The CHANGE_DATA_CAPTURE command

An Architect has been asked to clone schema STAGING as it looked one week ago, Tuesday June 1st at 8:00 AM, to recover some objects.
The STAGING schema has 50 days of retention.
The Architect runs the following statement:
CREATE SCHEMA STAGING_CLONE CLONE STAGING at (timestamp => '2021-06-01 08:00:00'); The Architect receives the following error: Time travel data is not available for schema STAGING. The requested time is either beyond the allowed time travel period or before the object creation time.
The Architect then checks the schema history and sees the following:
CREATED_ON|NAME|DROPPED_ON
2021-06-02 23:00:00 | STAGING | NULL
2021-05-01 10:00:00 | STAGING | 2021-06-02 23:00:00
How can cloning the STAGING schema be achieved?

• A.Rename the STAGING schema and perform an UNDROP to retrieve the previous STAGING schema version, then run the CLONE statement.
• B.Cloning cannot be accomplished because the STAGING schema version was not active during the proposed Time Travel time period.
• C.Undrop the STAGING schema and then rerun the CLONE statement.
• D.Modify the statement: CREATE SCHEMA STAGING_CLONE CLONE STAGING at (timestamp =>
  '2021-05-01 10:00:00');