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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) certification exam is an essential certification for professionals who work with Snowflake. It validates the expertise of an individual in designing, deploying, and managing complex Snowflake environments. SnowPro Advanced Architect Certification certification is recognized by industry leaders and is a valuable asset for professionals who want to advance their careers in cloud computing. Passing the exam requires a thorough understanding of Snowflake's advanced features and best practices, and candidates who achieve this certification demonstrate their commitment to staying up-to-date with the latest industry trends and technologies.
Snowflake ARA-C01 Exam is a performance-based exam that tests the candidate’s ability to solve real-world problems using Snowflake’s cloud data platform. ARA-C01 exam consists of a series of scenarios that require the candidate to design, implement, and optimize Snowflake solutions based on specific business requirements. ARA-C01 exam is conducted online and can be taken from anywhere in the world.
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To take the Snowflake ARA-C01 Exam, candidates must have previously passed the SnowPro Core Certification and have significant experience working with Snowflake's cloud data warehouse platform. ARA-C01 exam consists of 60 multiple-choice questions and must be completed within 90 minutes. A passing score of 80% or higher is required to earn the SnowPro Advanced Architect Certification, which is recognized by organizations worldwide as a validation of advanced skill and knowledge in Snowflake data warehousing.
NEW QUESTION # 91
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?
Answer: C
Explanation:
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 table that 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.
NEW QUESTION # 92
While creating a clustering key, what is the recommendation for maximum number of columns that you can include as part of the key?
Answer: D
NEW QUESTION # 93
Which Snowflake data modeling approach is designed for BI queries?
Answer: D
NEW QUESTION # 94
How can an Architect enable optimal clustering to enhance performance for different access paths on a given table?
Answer: D
Explanation:
Snowflake allows only one clustering key per table, which limits its effectiveness when multiple access paths exist. Creating a composite clustering key that includes many columns often leads to poor clustering depth and limited pruning.
Materialized views provide an effective alternative. Each materialized view can be clustered independently, allowing architects to tailor physical data organization to specific query patterns (Answer B). Queries targeting different access paths can then leverage the appropriate materialized view, achieving better pruning and performance.
Super projections are not a Snowflake feature. Creating multiple clustering keys on a single table is not supported. This question reinforces SnowPro Architect knowledge of advanced performance design techniques using materialized views.
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NEW QUESTION # 95
Following objects can be cloned in snowflake
Answer: A,C,E
NEW QUESTION # 96
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