A. The Snowflake SQL REST API does support the use of a GET command, which can be used to retrieve the status of a previously submitted query or to fetch the results of a query once it has been executed. D. The use of a CALL command to a stored procedure is supported, which can return a result set, including a table. This allows the invocation of stored procedures within Snowflake through the SQL REST API.

Warehouse credits are used to pay for the processing time used by each virtual warehouse in Snowflake. A virtual warehouse is a cluster of compute resources that enables executing queries, loading data, and performing other DML operations.  Warehouse credits are charged based on the number of virtual warehouses you use, how long they run, and their size 1 .

Among the commands listed in the question, the following ones will use warehouse credits:

SELECT MAX(FLAKE_ID) FROM SNOWFLAKE: This command will use warehouse credits because it is a query that requires a virtual warehouse to execute.  The query will scan the SNOWFLAKE table and return the maximum value of the FLAKE_ID column 2 . Therefore, option B is correct.

SELECT COUNT(*) FROM SNOWFLAKE: This command will also use warehouse credits because it is a query that requires a virtual warehouse to execute.  The query will scan the SNOWFLAKE table and return the number of rows in the table 3 . Therefore, option C is correct.

SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID: This command will also use warehouse credits because it is a query that requires a virtual warehouse to execute.  The query will scan the SNOWFLAKE table and return the number of rows for each distinct value of the FLAKE_ID column 4 . Therefore, option D is correct.

The command that will not use warehouse credits is:

SHOW TABLES LIKE ‘SNOWFL%’: This command will not use warehouse credits because it is a metadata operation that does not require a virtual warehouse to execute.  The command will return the names of the tables that match the pattern ‘SNOWFL%’ in the current database and schema 5 . Therefore, option A is incorrect.

References : :  Understanding Compute Cost  :  MAX Function  :  COUNT Function  :  GROUP BY Clause  :  SHOW TABLES

The ON_ERROR clause is an optional parameter for the COPY INTO command that specifies the behavior of the command when it encounters errors in the files.  The ON_ERROR clause can have one of the following values 1 :

CONTINUE: This value instructs the command to continue loading the file and return an error message for a maximum of one error encountered per data file. The difference between the ROWS_PARSED and ROWS_LOADED column values represents the number of rows that include detected errors.  To view all errors in the data files, use the VALIDATION_MODE parameter or query the VALIDATE function 1 .

SKIP_FILE: This value instructs the command to skip the file when it encounters a data error on any of the records in the file. The command moves on to the next file in the stage and continues loading.  The skipped file is not loaded and no error message is returned for the file 1 .

ABORT_STATEMENT: This value instructs the command to stop loading data when the first error is encountered. The command returns an error message for the file and aborts the load operation.  This is the default value for the ON_ERROR clause 1 .

Therefore, options A, B, and C are correct.

References : :  COPY INTO < table >

The correct answer is B and E because they use the correct syntax and values for the identifier function and the session variables.

The identifier function allows you to use a variable or expression as an identifier (such as a table name or column name) in a SQL statement. It takes a single argument and returns it as an identifier. For example, identifier($tbl_ref) returns EMP_TBL as an identifier.

The session variables are set using the SET command and can be referenced using the $ sign. For example, $var1 returns Name1 as a value.

Option A is incorrect because it uses Stbl_ref and Scol_ref, which are not valid session variables or identifiers. They should be $tbl_ref and $col_ref instead.

Option C is incorrect because it uses identifier < Stbl_ref > , which is not a valid syntax for the identifier function. It should be identifier($tbl_ref) instead.

Option D is incorrect because it uses Cvarl, var2, and var3, which are not valid session variables or values. They should be $var1, $var2, and $var3 instead.  References:

Snowflake Documentation: Identifier Function

Snowflake Documentation: Session Variables

Snowflake Learning: SnowPro Advanced: Architect Exam Study Guide

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 tables 1 . 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 task 2 . 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 Snowflake 3 . 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 use 4 .

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]

 Compilation time is the time it takes for the optimizer to create an optimal query plan for the efficient execution of the query.  It also involves some pruning of partition files, making the query execution efficient 2

If the compilation time is greater than the execution time, it means that the optimizer spent more time analyzing the query than actually running it. This could indicate that the query has overly complex logic, such as multiple joins, subqueries, aggregations, or expressions.  The complexity of the query could also affect the size and quality of the query plan, which could impact the performance of the query 3

To reduce the compilation time, the Architect can try to simplify the query logic, use views or common table expressions (CTEs) to break down the query into smaller parts, or use hints to guide the optimizer.  The Architect can also use the EXPLAIN command to examine the query plan and identify potential bottlenecks or inefficiencies 4   References :

1 : SnowPro Advanced: Architect | Study Guide  5

2 : Snowflake Documentation | Query Profile Overview  6

3 : Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time  7

4 : Snowflake Documentation | Optimizing Query Performance  8

:  SnowPro Advanced: Architect | Study Guide

:  Query Profile Overview

:  Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time

:  Optimizing Query Performance

The most cost-effective way to increase the availability and performance of the reports during peak usage times, while keeping costs under control, is to use a multi-cluster warehouse in auto-scale mode. Option D suggests using a multi-cluster warehouse with 1 size Medium cluster and allowing it to auto-scale between 1 and 4 clusters based on demand. This setup ensures that additional computing resources are available when needed (e.g., during Monday morning peaks) and are scaled down to minimize costs when the demand decreases. This approach optimizes resource utilization and cost by adjusting the compute capacity dynamically, rather than maintaining a larger fixed size or multiple clusters continuously. References: Snowflake ' s official documentation on managing warehouses and using auto-scaling features.

The correct answer is option B. This query is designed to assess the need for a multi-cluster warehouse by examining the queuing time ( AVG_QUEUED_LOAD ) on different days and virtual warehouses. When the AVG_QUEUED_LOAD is greater than zero, it suggests that queries are waiting for resources, which can be an indicator that performance might be improved by using a multi-cluster warehouse to handle the workload more efficiently. By grouping by date and warehouse name and filtering on the sum of the average queued load being greater than zero, the query identifies specific days and warehouses where the workload exceeded the available compute resources. This information is valuable when considering scaling out warehouses to multi-cluster configurations for improved performance.

A transient table is a type of table in Snowflake that does not have a Fail-safe period and can have a Time Travel retention period of either 0 or 1 day.  Transient tables are suitable for temporary or intermediate data that can be easily reproduced or replicated 1 .

A temporary table is a type of table in Snowflake that is automatically dropped when the session ends or the current user logs out.  Temporary tables do not incur any storage costs, but they are not visible to other users or sessions 2 .

A permanent table is a type of table in Snowflake that has a Fail-safe period and a Time Travel retention period of up to 90 days.  Permanent tables are suitable for persistent and durable data that needs to be protected from accidental or malicious deletion 3 .

In this case, the use case requires loading some data that can be visualized through Tableau. The data is updated every day and the old data is no longer required. Therefore, the best type of table to use in this case to optimize cost is a transient table, because it does not incur any Fail-safe costs and it can have a short Time Travel retention period of 0 or 1 day. This way, the data can be loaded and queried by Tableau, and then deleted or overwritten without incurring any unnecessary storage costs.

References : :  Transient Tables  :  Temporary Tables  :  Understanding & Using Time Travel

The get command in SnowSQL is a convenient way to download files from an internal stage to a local directory. The get command can be used in interactive mode or in a script, and it supports wildcards and parallel downloads.  The get command also allows specifying the overwrite option, which determines how to handle existing files with the same name 2

The Snowflake Connector for Python, the Snowflake API endpoint, and the get command in Snowsight are not recommended methods for downloading files from an internal stage, because they require more operational overhead than the get command in SnowSQL. The Snowflake Connector for Python and the Snowflake API endpoint require writing and maintaining code to handle the connection, authentication, and file transfer.  The get command in Snowsight requires using the web interface and manually selecting the files to download 3 4   References :

1 : SnowPro Advanced: Architect | Study Guide

2 : Snowflake Documentation | Using the GET Command

3 : Snowflake Documentation | Using the Snowflake Connector for Python

4 : Snowflake Documentation | Using the Snowflake API

: Snowflake Documentation | Using the GET Command in Snowsight

:  SnowPro Advanced: Architect | Study Guide

:  Using the GET Command

:  Using the Snowflake Connector for Python

:  Using the Snowflake API

: [Using the GET Command in Snowsight]