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 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.
• B. 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.
• C. 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.
• D. 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.

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

• A. TEMPORARY
• B. TRANSIENT
• C. PERMANENT

Which statements describe characteristics of the use of materialized views in Snowflake? (Choose two.)

• A. They can support MIN and MAX aggregates.
• B. They cannot include nested subqueries.
• C. They can include ORDER BY clauses.
• D. They can include context functions, such as CURRENT_TIME().
• E. They can support inner joins, but not outer joins.

When using the COPY INTO
command with the CSV file format, how does the MATCH_BY_COLUMN_NAME parameter behave?

• A. The parameter will be ignored.
• B. The command will return a warning stating that the file has unmatched columns.
• C. It expects a header to be present in the CSV file, which is matched to a case-sensitive table column name.
• D. The command will return an error.