Certification ARA-C01 Sample Questions | ARA-C01 Valid Test Voucher
Certification ARA-C01 Sample Questions | ARA-C01 Valid Test Voucher
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In today's Snowflake world getting the SnowPro Advanced Architect Certification (ARA-C01) certification exam is very crucial. With the growing popularity of credentials, the demand for ARA-C01 certification exam holders has increased. Success in the ARA-C01 Exam has become the need of time. People who fail the Snowflake ARA-C01 certification exam face loss of time and money.
Snowflake ARA-C01 exam is a challenging test that requires the candidate to have a deep understanding of Snowflake's cloud data platform and its various components. ARA-C01 exam tests the candidate's ability to design and implement solutions that are scalable, high-performance, and cost-effective.
Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is a professional accreditation designed for experienced data architects and engineers who specialize in building data solutions on the Snowflake platform. SnowPro Advanced Architect Certification certification validates an individual's expertise in designing and implementing complex data architectures that can handle the demands of modern businesses. ARA-C01 Exam covers a broad range of topics, including data modeling, data integration, security, scalability, and performance optimization.
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Excellent Snowflake Certification ARA-C01 Sample Questions - ARA-C01 Free Download
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The SnowPro Advanced Architect Certification exam is designed to test the candidate's ability to design, implement, and manage secure, scalable, and reliable Snowflake solutions. ARA-C01 Exam covers a broad range of topics, including Snowflake architecture, data modeling, security, performance optimization, scalability, and migration. Successful candidates will demonstrate their ability to design and implement Snowflake solutions that meet the complex data management needs of organizations.
Snowflake SnowPro Advanced Architect Certification Sample Questions (Q58-Q63):
NEW QUESTION # 58
Which feature provides the capability to define an alternate cluster key for a table with an existing cluster key?
- A. Materialized view
- B. External table
- C. Result cache
- D. Search optimization
Answer: A
Explanation:
A materialized view is a feature that provides the capability to define an alternate cluster key for a table with an existing cluster key. A materialized view is a pre-computed result set that is stored in Snowflake and can be queried like a regular table. A materialized view can have a different cluster key than the base table, which can improve the performance and efficiency of queries on the materialized view. A materialized view can also support aggregations, joins, and filters on the base table data. A materialized view is automatically refreshed when the underlying data in the base table changes, as long as the AUTO_REFRESH parameter is set to true1.
References:
* Materialized Views | Snowflake Documentation
NEW QUESTION # 59
A company is following the Data Mesh principles, including domain separation, and chose one Snowflake account for its data platform.
An Architect created two data domains to produce two data products. The Architect needs a third data domain that will use both of the data products to create an aggregate data product. The read access to the data products will be granted through a separate role.
Based on the Data Mesh principles, how should the third domain be configured to create the aggregate product if it has been granted the two read roles?
- A. Request a technical ETL user with the sysadmin role.
- B. Request that the two data domains share data using the Data Exchange.
- C. Create a hierarchy between the two read roles.
- D. Use secondary roles for all users.
Answer: B
Explanation:
In the scenario described, where a third data domain needs access to two existing data products in a Snowflake account structured according to Data Mesh principles, the best approach is to utilize Snowflake's Data Exchange functionality. Option D is correct as it facilitates the sharing and governance of data across different domains efficiently and securely. Data Exchange allows domains to publish and subscribe to live data products, enabling real-time data collaboration and access management in a governed manner. This approach is in line with Data Mesh principles, which advocate for decentralized data ownership and architecture, enhancing agility and scalability across the organization.
Reference:
Snowflake Documentation on Data Exchange
Articles on Data Mesh Principles in Data Management
NEW QUESTION # 60
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.)
- A. CREATE DATABASE;
- B. IMPORT DATABASE;
- C. IMPORT SHARE;
- D. IMPORT PRIVILEGES;
- E. CREATE SHARE;
Answer: A,D
NEW QUESTION # 61
Which statement is not true about shared database?
- A. Time travel is not supported on a shared database
- B. Shared databases are read only
- C. Shared databases can be re-shared with other accounts
- D. Shared databases cannot be cloned
Answer: C
NEW QUESTION # 62
Which of the following ingestion methods can be used to load near real-time data by using the messaging services provided by a cloud provider?
- A. Snowpipe
- B. Snowflake Connector for Kafka
- C. Snowflake streams
- D. Spark
Answer: B
Explanation:
Snowflake Connector for Kafka and Snowpipe are two ingestion methods that can be used to load near real-time data by using the messaging services provided by a cloud provider. Snowflake Connector for Kafka enables you to stream structured and semi-structured data from Apache Kafka topics into Snowflake tables.
Snowpipe enables you to load data from files that are continuously added to a cloud storage location, such as Amazon S3 or Azure Blob Storage. Both methods leverage Snowflake's micro-partitioning and columnar storage to optimize data ingestion and query performance. Snowflake streams and Spark are not ingestion methods, but rather components of the Snowflake architecture. Snowflake streams provide change data capture (CDC) functionality by tracking data changes in a table. Spark is a distributed computing framework that can be used to process large-scale data and write it to Snowflake using the Snowflake Spark Connector. References:
* Snowflake Connector for Kafka
* Snowpipe
* Snowflake Streams
* Snowflake Spark Connector
NEW QUESTION # 63
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