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?

1. A. Ingest the data using copy into and use streams and tasks to orchestrate transformation
2. B. Export the data into Amazon S3 to do model inference with Amazon Comprehend and ingest the data back into a Snowflake tabl
3. C. Then create a listing in the Snowflake Marketplace to make the data available to other companies.
4. D. Ingest the data using Snowpipe and use streams and tasks to orchestrate transformation
5. E. Create an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake tabl
6. F. Then create a listing in the Snowflake Marketplace to make the data available to other companies.
7. G. Ingest the data into Snowflake using Amazon EMR and PySpark using the Snowflake Spark connecto
8. H. Apply transformations using another Spark jo
9. I. Develop a python program to do model inference by leveraging the Amazon Comprehend text analysis AP
10. J. Then write the results to a Snowflake table and create a listing in the Snowflake Marketplace to make the data available to other companies.
11. K. Ingest the data using Snowpipe and use streams and tasks to orchestrate transformation
12. L. Export the data into Amazon S3 to do model inference with Amazon Comprehend and ingest the data back into a Snowflake tabl
13. M. Then create a listing in the Snowflake Marketplace to make the data available to other companies.

Correct Answer: B
Option B is the best design to meet the requirements because it uses Snowpipe to ingest the data continuously and efficiently as new records arrive in the object storage, leveraging event notifications. Snowpipe is a service that automates the loading of data from external sources into Snowflake tables1. It also uses streams and tasks to orchestrate transformations on the ingested data. Streams are objects that store the change history of a table, and tasks are objects that execute SQL statements on a
schedule or when triggered by another task2. Option B also uses an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. An external function is a user-defined function that calls an external API, such as Amazon Comprehend, to perform computations that are not natively supported by Snowflake3. Finally, option B uses the Snowflake Marketplace to make the de-identified final data set available publicly for advertising companies who use different cloud providers
in different regions. The Snowflake Marketplace is a platform that enables data providers to list and share their data sets with data consumers, regardless of the cloud platform or region they use4.
Option A is not the best design because it uses copy into to ingest the data, which is not as efficient and continuous as Snowpipe. Copy into is a SQL command that loads data from files into a table in a single transaction. It also exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.
Option C is not the best design because it uses Amazon EMR and PySpark to ingest and transform the data, which also increases the operational complexity and maintenance of the infrastructure. Amazon EMR is a cloud service that provides a managed Hadoop framework to process and analyze large-scale data sets. PySpark is a Python API for Spark, a distributed computing framework that can run on Hadoop. Option C also develops a python program to do model inference by leveraging the Amazon Comprehend text analysis API, which increases the development effort.
Option D is not the best design because it is identical to option A, except for the ingestion method. It still exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.
References: 1: Snowpipe Overview 2: Using Streams and Tasks to Automate Data Pipelines 3: External Functions Overview 4: Snowflake Data Marketplace Overview : [Loading Data Using COPY INTO] : [What is Amazon EMR?] : [PySpark Overview]

An Architect is integrating an application that needs to read and write data to Snowflake without installing any additional software on the application server.
How can this requirement be met?

1. A. Use SnowSQL.
2. B. Use the Snowpipe REST API.
3. C. Use the Snowflake SQL REST API.
4. D. Use the Snowflake ODBC driver.

Correct Answer: C
The Snowflake SQL REST API is a REST API that you can use to access and update data in a Snowflake database. You can use this API to execute standard queries and most DDL and DML statements. This API can be used to develop custom applications and integrations that can read and write data to Snowflake without installing any additional software on the application server. Option A is not correct because SnowSQL is a command-line client that requires installation and configuration on the application server. Option B is not correct because the Snowpipe REST API is used to load data from cloud storage into Snowflake tables, not to read or write data to Snowflake. Option D is not correct because the Snowflake ODBC driver is a software component that enables applications to connect to Snowflake using the ODBC protocol, which also requires installation and configuration on the application server. References: The answer can be verified from Snowflake??s official documentation on the Snowflake SQL REST API available on their website. Here are some relevant links:
✑ Snowflake SQL REST API | Snowflake Documentation
✑ Introduction to the SQL API | Snowflake Documentation
✑ Submitting a Request to Execute SQL Statements | Snowflake Documentation

An Architect is using SnowCD to investigate a connectivity issue.
Which system function will provide a list of endpoints that the network must be able to access to use a specific Snowflake account, leveraging private connectivity?

1. A. SYSTEMSALLOWLIST ()
2. B. SYSTEMSGET_PRIVATELINK
3. C. SYSTEMSAUTHORIZE_PRIVATELINK
4. D. SYSTEMSALLOWLIST_PRIVATELINK ()

Correct Answer: B
The SYSTEM$GET_PRIVATELINK function is used to retrieve the list of Snowflake service endpoints that need to be accessible when configuring private connectivity (such as AWS PrivateLink or Azure Private Link) for a Snowflake account. The function returns information necessary for setting up the networking infrastructure that allows secure and private access to Snowflake without using the public internet. SnowCD can then be used to verify connectivity to these endpoints.

An Architect needs to automate the daily Import of two files from an external stage into Snowflake. One file has Parquet-formatted data, the other has CSV-formatted data.
How should the data be joined and aggregated to produce a final result set?

1. A. Use Snowpipe to ingest the two files, then create a materialized view to produce the final result set.
2. B. Create a task using Snowflake scripting that will import the files, and then call a User- Defined Function (UDF) to produce the final result set.
3. C. Create a JavaScript stored procedure to rea
4. D. join, and aggregate the data directly from the external stage, and then store the results in a table.
5. E. Create a materialized view to read, Join, and aggregate the data directly from the external stage, and use the view to produce the final result set

Correct Answer: B
According to the Snowflake documentation, tasks are objects that enable scheduling and execution of SQL statements or JavaScript user-defined functions (UDFs) in Snowflake. Tasks can be used to automate data loading, transformation, and maintenance operations. Snowflake scripting is a feature that allows writing procedural logic using SQL statements and JavaScript UDFs. Snowflake scripting can be used to create complex workflows and orchestrate tasks. Therefore, the best option to automate the daily import of two files from an external stage into Snowflake, join and aggregate the data, and produce a final result set is to create a task using Snowflake scripting that will import the files using the COPY INTO command, and then call a UDF to perform the join and aggregation logic. The UDF can return a table or a variant value as the final result set. References:
✑ Tasks
✑ Snowflake Scripting
✑ User-Defined Functions

Which feature provides the capability to define an alternate cluster key for a table with an existing cluster key?

1. A. External table
2. B. Materialized view
3. C. Search optimization
4. D. Result cache

Correct Answer: B
A materialized view is a feature that provides the capability to define an alternate cluster key for a table with an existing cluster key. A materialized view is a pre- computed result set that is stored in Snowflake and can be queried like a regular table. A materialized view can have a different cluster key than the base table, which can improve the performance and efficiency of queries on the materialized view. A materialized view can also support aggregations, joins, and filters on the base table data. A materialized view is automatically refreshed when the underlying data in the base table changes, as long as the AUTO_REFRESH parameter is set to true1.
References:
✑ Materialized Views | Snowflake Documentation