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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) is a professional certification exam designed to validate the skills and knowledge of data architects and engineers who specialize in designing and implementing complex Snowflake cloud data platforms. SnowPro Advanced Architect Certification certification exam is recognized globally by companies that use Snowflake for their data management, warehousing, and analytics needs.

Snowflake SnowPro Advanced Architect Certification Sample Questions (Q147-Q152):

NEW QUESTION # 147
Company A would like to share data in Snowflake with Company B.
Company B is not on the same cloud platform as Company A.
What is required to allow data sharing between these two companies?

A. Setup data replication to the region and cloud platform where the consumer resides.
B. Create a pipeline to write shared data to a cloud storage location in the target cloud provider.
C. Ensure that all views are persisted, as views cannot be shared across cloud platforms.
D. Company A and Company B must agree to use a single cloud platform: Data sharing is only possible if the companies share the same cloud provider.

Answer: A

NEW QUESTION # 148
An Architect has designed a data pipeline that Is receiving small CSV files from multiple sources. All of the files are landing in one location. Specific files are filtered for loading into Snowflake tables using the copy command. The loading performance is poor.
What changes can be made to Improve the data loading performance?

A. Create a specific storage landing bucket to avoid file scanning.
B. Create a multi-cluster warehouse and merge smaller files to create bigger files.
C. Increase the size of the virtual warehouse.
D. Change the file format from CSV to JSON.

Answer: B

Explanation:
According to the Snowflake documentation, the data loading performance can be improved by following some best practices and guidelines for preparing and staging the data files. One of the recommendations is to aim for data files that are roughly 100-250 MB (or larger) in size compressed, as this will optimize the number of parallel operations for a load. Smaller files should be aggregated and larger files should be split to achieve this size range. Another recommendation is to use a multi-cluster warehouse for loading, as this will allow for scaling up or out the compute resources depending on the load demand. A single-cluster warehouse may not be able to handle the load concurrency and throughput efficiently. Therefore, by creating a multi-cluster warehouse and merging smaller files to create bigger files, the data loading performance can be improved. References:
* Data Loading Considerations
* Preparing Your Data Files
* Planning a Data Load

NEW QUESTION # 149
A Snowflake Architect is designing an application and tenancy strategy for an organization where strong legal isolation rules as well as multi-tenancy are requirements.
Which approach will meet these requirements if Role-Based Access Policies (RBAC) is a viable option for isolating tenants?

A. Create accounts for each tenant in the Snowflake organization.
B. Create a multi-tenant table strategy if row level security is not viable for isolating tenants.
C. Create an object for each tenant strategy if row level security is not viable for isolating tenants.
D. Create an object for each tenant strategy if row level security is viable for isolating tenants.

Answer: A

NEW QUESTION # 150
A Snowflake Architect created a new data share and would like to verify that only specific records in secure views are visible within the data share by the consumers.
What is the recommended way to validate data accessibility by the consumers?

A. Alter the share settings as shown below, in order to impersonate a specific consumer account.
alter share sales share set accounts = 'Consumerl' share restrictions = true
B. Create a row access policy as shown below and assign it to the data share.
create or replace row access policy rap_acct as (acct_id varchar) returns boolean -> case when
'acctl_role' = current_role() then true else false end;
C. Create reader accounts as shown below and impersonate the consumers by logging in with their credentials.
create managed account reader_acctl admin_name = userl , adroin_password 'Sdfed43da!44T , type = reader;
D. Set the session parameter called SIMULATED_DATA_SHARING_C0NSUMER as shown below in order to impersonate the consumer accounts.
alter session set simulated_data_sharing_consumer - 'Consumer Acctl*

Answer: D

Explanation:
The SIMULATED_DATA_SHARING_CONSUMER session parameter allows a data provider to simulate the data access of a consumer account without creating a reader account or logging in with the consumer credentials. This parameter can be used to validate the data accessibility by the consumers in a data share, especially when using secure views or secure UDFs that filter data based on the current account or role. By setting this parameter to the name of a consumer account, the data provider can see the same data as the consumer would see when querying the shared database. This is a convenient and efficient way to test the data sharing functionality and ensure that only the intended data is visible to the consumers.
References:
* Using the SIMULATED_DATA_SHARING_CONSUMER Session Parameter
* SnowPro Advanced: Architect Exam Study Guide

NEW QUESTION # 151
The Data Engineering team at a large manufacturing company needs to engineer data coming from many sources to support a wide variety of use cases and data consumer requirements which include:
1) Finance and Vendor Management team members who require reporting and visualization
2) Data Science team members who require access to raw data for ML model development
3) Sales team members who require engineered and protected data for data monetization What Snowflake data modeling approaches will meet these requirements? (Choose two.)

A. Create a single star schema in a single database to support all consumers' requirements.
B. Consolidate data in the company's data lake and use EXTERNAL TABLES.
C. Create a Data Vault as the sole data pipeline endpoint and have all consumers directly access the Vault.
D. Create a raw database for landing and persisting raw data entering the data pipelines.
E. Create a set of profile-specific databases that aligns data with usage patterns.

Answer: A,C

NEW QUESTION # 152
......

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