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Snowflake ARA-C01 Certification Exam is composed of two parts, the first being a multiple-choice exam that tests fundamental knowledge of Snowflake architecture and features. The second part of the exam is a hands-on lab where candidates are given a set of requirements and need to implement a Snowflake solution to meet those requirements. The lab is designed to test the candidate's ability to apply their knowledge in real-world scenarios.

Snowflake ARA-C01 Certification Exam is a computer-based test. It consists of 95 multiple-choice questions that must be completed in 120 minutes. ARA-C01 exam is administered by Pearson VUE, a leading provider of certification exams globally. Candidates can take the exam at any authorized Pearson VUE testing center, making it convenient for professionals worldwide.

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Snowflake ARA-C01 Certification Exam covers a wide range of topics including Snowflake architecture, data modeling, performance optimization, security, and governance. To pass the certification exam, candidates must demonstrate their expertise in designing and implementing Snowflake solutions that meet the business requirements of their organization. Candidates must also demonstrate their ability to optimize the performance of Snowflake solutions and ensure the security and governance of Snowflake data.

Snowflake SnowPro Advanced Architect Certification Sample Questions (Q16-Q21):

NEW QUESTION # 16
When using the Snowflake Connector for Kafka, what data formats are supported for the messages? (Choose two.)

A. JSON
B. Avro
C. CSV
D. Parquet
E. XML

Answer: A,B

Explanation:
The data formats that are supported for the messages when using the Snowflake Connector for Kafka are Avro and JSON. These are the two formats that the connector can parse and convert into Snowflake table rows. The connector supports both schemaless and schematized JSON, as well as Avro with or without a schema registry1. The other options are incorrect because they are not supported data formats for the messages. CSV, XML, and Parquet are not formats that the connector can parse and convert into Snowflake table rows. If the messages are in these formats, the connector will load them as VARIANT data type and store them as raw strings in the table2. Reference: Snowflake Connector for Kafka | Snowflake Documentation, Loading Protobuf Data using the Snowflake Connector for Kafka | Snowflake Documentation

NEW QUESTION # 17
What conditions should be true for a table to consider search optimization

A. The table can be of any size
B. The table size is at least 100 GB
C. The table is not clustered OR The table is frequently queried on columns other than the primary cluster key

Answer: B,C

NEW QUESTION # 18
A large manufacturing company runs a dozen individual Snowflake accounts across its business divisions. The company wants to increase the level of data sharing to support supply chain optimizations and increase its purchasing leverage with multiple vendors.
The company's Snowflake Architects need to design a solution that would allow the business divisions to decide what to share, while minimizing the level of effort spent on configuration and management. Most of the company divisions use Snowflake accounts in the same cloud deployments with a few exceptions for European-based divisions.
According to Snowflake recommended best practice, how should these requirements be met?

A. Deploy a Private Data Exchange and use replication to allow European data shares in the Exchange.
B. Deploy a Private Data Exchange in combination with data shares for the European accounts.
C. Migrate the European accounts in the global region and manage shares in a connected graph architecture. Deploy a Data Exchange.
D. Deploy to the Snowflake Marketplace making sure that invoker_share() is used in all secure views.

Answer: A

Explanation:
According to Snowflake recommended best practice, the requirements of the large manufacturing company should be met by deploying a Private Data Exchange in combination with data shares for the European accounts. A Private Data Exchange is a feature of the Snowflake Data Cloud platform that enables secure and governed sharing of data between organizations. It allows Snowflake customers to create their own data hub and invite other parts of their organization or external partners to access and contribute data sets. A Private Data Exchange provides centralized management, granular access control, and data usage metrics for the data shared in the exchange1. A data share is a secure and direct way of sharing data between Snowflake accounts without having to copy or move the data. A data share allows the data provider to grant privileges on selected objects in their account to one or more data consumers in other accounts2. By using a Private Data Exchange in combination with data shares, the company can achieve the following benefits:
* The business divisions can decide what data to share and publish it to the Private Data Exchange, where it can be discovered and accessed by other members of the exchange. This reduces the effort and complexity of managing multiple data sharing relationships and configurations.
* The company can leverage the existing Snowflake accounts in the same cloud deployments to create the Private Data Exchange and invite the members to join. This minimizes the migration and setup costs and leverages the existing Snowflake features and security.
* The company can use data shares to share data with the European accounts that are in different regions or cloud platforms. This allows the company to comply with the regional and regulatory requirements for data sovereignty and privacy, while still enabling data collaboration across the organization.
* The company can use the Snowflake Data Cloud platform to perform data analysis and transformation
* on the shared data, as well as integrate with other data sources and applications. This enables the company to optimize its supply chain and increase its purchasing leverage with multiple vendors.

NEW QUESTION # 19
An Architect needs to design a Snowflake account and database strategy to store and analyze large amounts of structured and semi-structured data. There are many business units and departments within the company. The requirements are scalability, security, and cost efficiency.
What design should be used?

A. Use a centralized Snowflake database for core business data, and use separate databases for departmental or project-specific data.
B. Create a single Snowflake account and database for all data storage and analysis needs, regardless of data volume or complexity.
C. Set up separate Snowflake accounts and databases for each department or business unit, to ensure data isolation and security.
D. Use Snowflake's data lake functionality to store and analyze all data in a central location, without the need for structured schemas or indexes

Answer: A

Explanation:
The best design to store and analyze large amounts of structured and semi-structured data for different business units and departments is to use a centralized Snowflake database for core business data, and use separate databases for departmental or project-specific data. This design allows for scalability, security, and cost efficiency by leveraging Snowflake's features such as:
* Database cloning: Cloning a database creates a zero-copy clone that shares the same data files as the original database, but can be modified independently. This reduces storage costs and enables fast and consistent data replication for different purposes.
* Database sharing: Sharing a database allows granting secure and governed access to a subset of data in a database to other Snowflake accounts or consumers. This enables data collaboration and monetization across different business units or external partners.
* Warehouse scaling: Scaling a warehouse allows adjusting the size and concurrency of a warehouse to match the performance and cost requirements of different workloads. This enables optimal resource utilization and flexibility for different data analysis needs. References: Snowflake Documentation:
Database Cloning, Snowflake Documentation: Database Sharing, [Snowflake Documentation:
Warehouse Scaling]

NEW QUESTION # 20
What are some of the characteristics of result set caches? (Choose three.)

A. Snowflake persists the data results for 24 hours.
B. Each time persisted results for a query are used, a 24-hour retention period is reset.
C. The result set cache is not shared between warehouses.
D. Time Travel queries can be executed against the result set cache.
E. The data stored in the result cache will contribute to storage costs.
F. The retention period can be reset for a maximum of 31 days.

Answer: A,B,F

Explanation:
Explanation
Comprehensive and Detailed Explanation: According to the SnowPro Advanced: Architect documents and learning resources, some of the characteristics of result set caches are:
* Snowflake persists the data results for 24 hours. This means that the result set cache holds the results of
* every query executed in the past 24 hours, and can be reused if the same query is submitted again and the underlying data has not changed1.
* Each time persisted results for a query are used, a 24-hour retention period is reset. This means that the result set cache extends the lifetime of the results every time they are reused, up to a maximum of 31 days from the date and time that the query was first executed1.
* The retention period can be reset for a maximum of 31 days. This means that the result set cache will purge the results after 31 days, regardless of whether they are reused or not. After 31 days, the next time the query is submitted, a new result is generated and persisted1.
The other options are incorrect because they are not characteristics of result set caches. Option A is incorrect because Time Travel queries cannot be executed against the result set cache. Time Travel queries use the AS OF clause to access historical data that is stored in the storage layer, not the result set cache2. Option D is incorrect because the data stored in the result set cache does not contribute to storage costs. The result set cache is maintained by the service layer, and does not incur any additional charges1. Option F is incorrect because the result set cache is shared between warehouses. The result set cache is available across virtual warehouses, so query results returned to one user are available to any other user on the system who executes the same query, provided the underlying data has not changed1. References: Using Persisted Query Results | Snowflake Documentation, Time Travel | Snowflake Documentation

NEW QUESTION # 21
......

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