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Advanced Architect ARA-C01 Practice Questions

SnowPro Advanced Architect

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Question 1

An Architect is designing a solution for data recovery in case of a data pipeline failure or a violation of business rules in a database. The team needs to be able to recover the correct data in either case.

The solution has these requirements:

1. Database data must be recoverable from the past 48 hours.

2. Data in the ANALYTICS schema must be recoverable from the past 5 days.

How should these requirements be met?

Parameterize a stored procedure to perform a backup and removal of the entire database and the ANALYTICS schema based on defined rules.
Create a Directed Acyclic Graph (DAG) from tasks that will handle the data backups: 1. Tasks for cloning whole database with the parameter REMOVE_CLONE_AFTER = 2 2. A task for cloning the ANALYTICS schema with parameter REMOVE_CLONE_AFTER = 5
Use Time Travel with these parameters: 1. MIN_DATA_RETENTION_TIME_IN_DAYS = 2 2. DATA_RETENTION_TIME_IN_DAYS = 5 (For the ANALYTICS schema)
Use Time Travel with these parameters: 1. DATA_RETENTION_TIME_IN_DAYS = 2 2. MIN_DATA_RENTENTION_TIME_IN_DAYS = 5 (For the ANALYTICS schema)

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Question 2

Which considerations should be made when using Automatic Clustering in a Snowflake account? (Select TWO).

Automatic Clustering blocks DML statements issued against tables while they are being reclustered.
Cloned tables start with Automatic Clustering suspended, even if it is enabled for the source table.
The billing for Automatic Clustering appears in the same warehouse used by the user to create the table.
Users can suspend and resume Automatic Clustering for a clustered table at any time using ALTER TABLE … SUSPEND / RESUME RECLUSTER commands.
The billing for Automatic Clustering is calculated every 24 hours.

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Question 3

An Architect is investigating a Snowflake environment experiencing fluctuating credit consumption and rising storage costs. Critical BI dashboards run on several virtual warehouses provisioned with large sizes and high auto-suspend timeouts, and ETL pipelines create and drop staging tables daily.

Which combination of steps addresses these issues MOST cost effectively? (Select TWO).

Review and resize over provisioned virtual warehouses and configure appropriate auto-suspend intervals on each warehouse to minimize idle credit accumulation between workloads.
Set SCALING_POLICY = STANDARD on multi-cluster virtual warehouses to ensure new clusters start immediately when queued queries are detected.
Use transient tables for the daily ETL staging data so dropped and recreated tables do not accumulate Time Travel and Fail-safe storage.
Raise AUTO_SUSPEND to a high value on the BI virtual warehouses to keep the local cache warm for repeated dashboard queries.
Enable search optimization on all dimension tables used by BI dashboards to reduce scan volume and lower virtual warehouse credit consumption.

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Question 4

An Architect set up a Snowflake environment to support a company's data development lifecycle, using cloning to deliver data from a production environment to development and testing environments.

What needs to be done to the objects in the clones before they can be used in the development and testing environments?

Recreate any database roles.
Reset the cloned object parameters.
Recreate any pipes that reference an internal stage.
Perform an ALTER operation to convert any cloned event tables to standard tables.

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Question 5

PDF files are being ingested into a Snowflake environment. The files will be staged and processed into tables later. The data will be shared directly using software links to the PDF files, with server-side encryption enabled.

How should these PDF files be ingested while creating a catalog of staged files, using server-side encryption?

Create a Python User-Defined Function (UDF) to process the PDF files into tables.
CREATE STAGE invoices_pdf_stage ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE');
CREATE STAGE invoices_pdf_stage ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE') DIRECTORY = ( ENABLE = true );
CREATE STAGE my_stage URL = 's3://bucketname/PDFs/' CREDENTIALS = (AWS_KEY_ID = 'xxyyzz' AWS_SECRET_KEY = 'zzyyxx');

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Question 6

An application is being migrated from Amazon Athena to Snowflake using a custom complex anonymization algorithm in Node JS using AWS Lambda. This Lambda function is also used across other applications which are in other platforms.

How can this logic be imported into Snowflake with the LEAST amount of operational overhead?

Build a Java function in Snowflake that implements the custom anonymization logic.
Build a JavaScript function in Snowflake that implements the custom anonymization logic.
Execute the Lambda function from within Snowflake for anonymization.
Apply the anonymization logic in Athena and then migrate the anonymized data into Snowflake.

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