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?

• 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.

Two queries are run on the customer_address table:
create or replace TABLE CUSTOMER_ADDRESS ( CA_ADDRESS_SK NUMBER(38,0),
CA_ADDRESS_ID VARCHAR(16), CA_STREET_NUMBER VARCHAR(IO) CA_STREET_NAME
VARCHAR(60), CA_STREET_TYPE VARCHAR(15), CA_SUITE_NUMBER VARCHAR(10), CA_CITY VARCHAR(60), CA_COUNTY VARCHAR(30), CA_STATE VARCHAR(2), CA_ZIP VARCHAR(10), CA_COUNTRY VARCHAR(20), CA_GMT_OFFSET NUMBER(5,2), CA_LOCATION_TYPE VARCHAR(20) ); ALTER TABLE DEMO_DB.DEMO_SCH.CUSTOMER_ADDRESS ADD SEARCH OPTIMIZATION ON SUBSTRING(CA_ADDRESS_ID); Which queries will benefit from the use of the search optimization service? (Select TWO).

• A. select * from DEMO_DB.DEMO_SCH.CUSTOMER_ADDRESS Where substring
  (CA_ADDRESS_ID,1,8)= substring('AAAAAAAAPHPPLBAAASKDJHASLKDJHASKJD',1,8);
• B. select*fromDEMO_DB.DEMO_SCH.CUSTOMER_ADDRESSWhereCA_ADDRESS_IDLIKE '% BAAASKD%';
• C. select*fromDEMO_DB.DEMO_SCH.CUSTOMER_ADDRESSWhereCA_ADDRESS_IDLIKE '% PHPP%';
• D. select*fromDEMO_DB.DEMO_SCH.CUSTOMER_ADDRESSWhereCA_ADDRESS_IDNOT LIKE
  '%AAAAAAAAPHPPL%';
• E. select * from DEMO_DB.DEMO_SCH.CUSTOMER_ADDRESS Where CA_ADDRESS_ID= substring('AAAAAAAAPHPPLBAAASKDJHASLKDJHASKJD',1,16);

A group of marketing and advertising companies want to share data with one another for joint analysis while maintaining strict privacy controls.
How should an Architect design the data sharing solution?

• A. Use Snowflake Marketplace listings.
• B. Use Secure Data Sharing with standard shares.
• C. Use a Data Exchange as a shared data hub.
• D. Use Data Clean Rooms for privacy-preserving collaboration.

Which SQL ALTER command will MAXIMIZE memory and compute resources for a Snowpark stored procedure when executed on the snowpark_opt_wh warehouse?

• A. ALTER WAREHOUSE snowpark_opt_wh SET MAX_CONCURRENCY_LEVEL = 2;
• B. ALTER WAREHOUSE snowpark_opt_wh SET MAX_CONCURRENCY_LEVEL = 8;
• C. ALTER WAREHOUSE snowpark_opt_wh SET MAX_CONCURRENCY_LEVEL = 1;
• D. ALTER WAREHOUSE snowpark_opt_wh SET MAX_CONCURRENCY_LEVEL = 16;