ARA-C01 Exam Dumps : SnowPro Advanced: Architect Certification Exam

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 name2

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 download34 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]

The SQL statement is a command for creating a pipe in Snowflake, which is an object that defines the COPY INTO

statement used by Snowpipe to load data from an ingestion queue into tables1. The statement uses a subquery in the FROM clause to transform the data from the staged files before loading it into the table2.

The transformations supported in the subquery are as follows2:

Data can be filtered by an optional WHERE clause, which specifies a condition that must be satisfied by the rows returned by the subquery. For example:

SQLAI-generated code. Review and use carefully. More info on FAQ.

createpipe mypipeas

copyintomytable

from(

select*from@mystage

wherecol1='A'andcol2>10

);

Columns can be reordered, which means changing the order of the columns in the subquery to match the order of the columns in the target table. For example:

SQLAI-generated code. Review and use carefully. More info on FAQ.

createpipe mypipeas

copyintomytable (col1, col2, col3)

from(

selectcol3, col1, col2from@mystage

);

Columns can be omitted, which means excluding some columns from the subquery that are not needed in the target table. For example:

SQLAI-generated code. Review and use carefully. More info on FAQ.

createpipe mypipeas

copyintomytable (col1, col2)

from(

selectcol1, col2from@mystage

);

The other options are not supported in the subquery because2:

Type casts are not supported, which means changing the data type of a column in the subquery. For example, the following statement will cause an error:

SQLAI-generated code. Review and use carefully. More info on FAQ.

createpipe mypipeas

copyintomytable (col1, col2)

from(

selectcol1::date, col2from@mystage

);

Incoming data can not be joined with other tables, which means combining the data from the staged files with the data from another table in the subquery. For example, the following statement will cause an error:

SQLAI-generated code. Review and use carefully. More info on FAQ.

createpipe mypipeas

copyintomytable (col1, col2, col3)

from(

selects.col1, s.col2, t.col3from@mystages

joinothertable tons.col1=t.col1

);

The ON ERROR - ABORT statement command can not be used, which means aborting the entire load operation if any error occurs. This command can only be used in the COPY INTO

statement, not in the subquery. For example, the following statement will cause an error:

SQLAI-generated code. Review and use carefully. More info on FAQ.

createpipe mypipeas

copyintomytable

from(

select*from@mystage

onerror abort

);

1: CREATE PIPE | Snowflake Documentation

2: Transforming Data During a Load | Snowflake Documentation

Question 3
A company is using Snowflake in Azure in the Netherlands. The company analyst team also has data in JSON format that is stored in an Amazon S3 bucket in the AWS Singapore region that the team wants to analyze.
The Architect has been given the following requirements:
1. Provide access to frequently changing data
2. Keep egress costs to a minimum
3. Maintain low latency
How can these requirements be met with the LEAST amount of operational overhead?
Options:
A.
Use a materialized view on top of an external table against the S3 bucket in AWS Singapore.
B.
Use an external table against the S3 bucket in AWS Singapore and copy the data into transient tables.
C.
Copy the data between providers from S3 to Azure Blob storage to collocate, then use Snowpipe for data ingestion.
D.
Use AWS Transfer Family to replicate data between the S3 bucket in AWS Singapore and an Azure Netherlands Blob storage, then use an external table against the Blob storage.
Show Answer
Answer:
A
Explanation:
 Option A is the best design to meet the requirements because it uses a materialized view on top of an external table against the S3 bucket in AWS Singapore. A materialized view is a database object that contains the results of a query and can be refreshed periodically to reflect changes in the underlying data1. An external table is a table that references data files stored in a cloud storage service, such as Amazon S32. By using a materialized view on top of an external table, the company can provide access to frequently changing data, keep egress costs to a minimum, and maintain low latency. This is because the materialized view will cache the query results in Snowflake, reducing the need to access the external data files and incur network charges. The materialized view will also improve the query performance by avoiding scanning the external data files every time. The materialized view can be refreshed on a schedule or on demand to capture the changes in the external data files1.
Option B is not the best design because it uses an external table against the S3 bucket in AWS Singapore and copies the data into transient tables. A transient table is a tablethat is not subject to the Time Travel and Fail-safe features of Snowflake, and is automatically purged after a period of time3. By using an external table and copying the data into transient tables, the company will incur more egress costs and operational overhead than using a materialized view. This is because the external table will access the external data files every time a query is executed, and the copy operation will also transfer data from S3 to Snowflake. The transient tables will also consume more storage space in Snowflake and require manual maintenance to ensure they are up to date.
Option C is not the best design because it copies the data between providers from S3 to Azure Blob storage to collocate, then uses Snowpipe for data ingestion. Snowpipe is a service that automates the loading of data from external sources into Snowflake tables4. By copying the data between providers, the company will incur high egress costs and latency, as well as operational complexity and maintenance of the infrastructure. Snowpipe will also add another layer of processing and storage in Snowflake, which may not be necessary if the external data files are already in a queryable format.
Option D is not the best design because it uses AWS Transfer Family to replicate data between the S3 bucket in AWS Singapore and an Azure Netherlands Blob storage, then uses an external table against the Blob storage. AWS Transfer Family is a service that enables secure and seamless transfer of files over SFTP, FTPS, and FTP to and from Amazon S3 or Amazon EFS5. By using AWS Transfer Family, the company will incur high egress costs and latency, as well as operational complexity and maintenance of the infrastructure. The external table will also access the external data files every time a query is executed, which may affect the query performance.
[References: 1: Materialized Views 2: External Tables 3: Transient Tables 4: Snowpipe Overview 5: AWS Transfer Family, , , ]
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