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Snowflake ARA-C01: SnowPro Advanced Architect Certification Exam is a highly respected and sought-after certification in the field of data warehousing and cloud computing. It is designed to test the advanced knowledge and skills of architects who are responsible for designing and implementing complex data warehousing solutions using Snowflake's cloud data platform. Passing ARA-C01 exam demonstrates the candidate's expertise in designing and implementing scalable and high-performance data warehouses, data lakes, and data pipelines using Snowflake's cloud data platform.

Snowflake ARA-C01 Certification Exam is an industry-leading certification that validates the advanced technical skills of Snowflake architects. SnowPro Advanced Architect Certification certification is highly respected by organizations that utilize Snowflake and is a demonstration of the architect's expertise in designing and implementing complex Snowflake solutions. Candidates who successfully pass the exam can expect to have a significant advantage in the job market and be highly sought after by organizations looking for Snowflake experts.

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Snowflake SnowPro Advanced Architect Certification Sample Questions (Q182-Q187):

NEW QUESTION # 182
When would you usually consider to add clustering key to a table

Answer: A,B


NEW QUESTION # 183
An Architect needs to allow a user to create a database from an inbound share.
To meet this requirement, the user's role must have which privileges? (Choose two.)

Answer: D,E


NEW QUESTION # 184
Which SQL alter command will MAXIMIZE memory and compute resources for a Snowpark stored procedure when executed on the snowpark_opt_wh warehouse?

Answer: D

Explanation:
To maximize memory and compute resources for a Snowpark stored procedure, you need to set the MAX_CONCURRENCY_LEVEL parameter for the warehouse that executes the stored procedure. This parameter determines the maximum number of concurrent queries that can run on a single warehouse. By setting it to 16, you ensure that the warehouse can use all the available CPU cores and memory on a single node, which is the optimal configuration for Snowpark-optimized warehouses. This will improve the performance and efficiency of the stored procedure, as it will not have to share resources with other queries or nodes. The other options are incorrect because they either do not change the MAX_CONCURRENCY_LEVEL parameter, or they set it to a lower value than 16, which will reduce the memory and compute resources for the stored procedure. Reference:
[Snowpark-optimized Warehouses] 1
[Training Machine Learning Models with Snowpark Python] 2
[Snowflake Shorts: Snowpark Optimized Warehouses] 3


NEW QUESTION # 185
What Snowflake features should be leveraged when modeling using Data Vault?

Answer: A

Explanation:
These two features are relevant for modeling using Data Vault on Snowflake. Data Vault is a data modeling approach that organizes data into hubs, links, and satellites. Data Vault is designed to enable high scalability, flexibility, and performance for data integration and analytics. Snowflake is a cloud data platform that supports various data modeling techniques, including Data Vault. Snowflake provides some features that can enhance the Data Vault modeling, such as:
Snowflake's support of multi-table inserts into the data model's Data Vault tables. Multi-table inserts (MTI) are a feature that allows inserting data from a single query into multiple tables in a single DML statement. MTI can improve the performance and efficiency of loading data into Data Vault tables, especially for real-time or near-real-time data integration. MTI can also reduce the complexity and maintenance of the loading code, as well as the data duplication and latency12.
Scaling up the virtual warehouses will support parallel processing of new source loads. Virtual warehouses are a feature that allows provisioning compute resources on demand for data processing. Virtual warehouses can be scaled up or down by changing the size of the warehouse, which determines the number of servers in the warehouse. Scaling up the virtual warehouses can improve the performance and concurrency of processing new source loads into Data Vault tables, especially for large or complex data sets. Scaling up the virtual warehouses can also leverage the parallelism and distribution of Snowflake's architecture, which can optimize the data loading and querying34.
Reference:
Snowflake Documentation: Multi-table Inserts
Snowflake Blog: Tips for Optimizing the Data Vault Architecture on Snowflake Snowflake Documentation: Virtual Warehouses Snowflake Blog: Building a Real-Time Data Vault in Snowflake


NEW QUESTION # 186
An Architect needs to allow a user to create a database from an inbound share.
To meet this requirement, the user's role must have which privileges? (Choose two.)

Answer: C,E

Explanation:
According to the Snowflake documentation, to create a database from an inbound share, the user's role must have the following privileges:
* The CREATE DATABASE privilege on the current account. This privilege allows the user to create a new database in the account1.
* The IMPORT DATABASE privilege on the share. This privilege allows the user to import a database from the share into the account2. The other privileges listed are not relevant for this requirement. The IMPORT SHARE privilege is used to import a share into the account, not a database3. The IMPORT PRIVILEGES privilege is used to import the privileges granted on the shared objects, not the objects themselves2. The CREATE SHARE privilege is used to create a share to provide data to other accounts, not to consume data from other accounts4.
References:
* CREATE DATABASE | Snowflake Documentation
* Importing Data from a Share | Snowflake Documentation
* Importing a Share | Snowflake Documentation
* CREATE SHARE | Snowflake Documentation


NEW QUESTION # 187
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