RELIABLE ARA-C01 BRAINDUMPS BOOK | EXAM ARA-C01 FLASHCARDS

Reliable ARA-C01 Braindumps Book | Exam ARA-C01 Flashcards

Reliable ARA-C01 Braindumps Book | Exam ARA-C01 Flashcards

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Snowflake ARA-C01 exam is a challenging and rigorous certification that is designed to recognize the top architects in the Snowflake community. By earning this certification, professionals can demonstrate their ability to design and manage complex Snowflake environments, and position themselves as leaders in this fast-growing field.

Achieving the Snowflake ARA-C01 Certification demonstrates a high level of expertise in Snowflake's cloud-based data warehousing solutions. It is a valuable credential that can help professionals stand out in the job market and advance their careers in the field of data warehousing and cloud computing.

Snowflake SnowPro Advanced Architect Certification Sample Questions (Q77-Q82):

NEW QUESTION # 77
Data replication in snowflake helps in

  • A. Data sharing
  • B. Account Migration
  • C. Fail safe
  • D. Disaster recovery

Answer: A,B,D


NEW QUESTION # 78
A company has a source system that provides JSON records for various loT operations. The JSON Is loading directly into a persistent table with a variant field. The data Is quickly growing to 100s of millions of records and performance to becoming an issue. There is a generic access pattern that Is used to filter on the create_date key within the variant field.
What can be done to improve performance?

  • A. Validate the size of the warehouse being used. If the record count is approaching 100s of millions, size XL will be the minimum size required to process this amount of data.
  • B. Alter the target table to Include additional fields pulled from the JSON records. This would Include a create_date field with a datatype of time stamp. When this field Is used in the filter, partition pruning will occur.
  • C. Incorporate the use of multiple tables partitioned by date ranges. When a user or process needs to query a particular date range, ensure the appropriate base table Is used.
  • D. Alter the target table to include additional fields pulled from the JSON records. This would include a create_date field with a datatype of varchar. When this field is used in the filter, partition pruning will occur.

Answer: B

Explanation:
The correct answer is A because it improves the performance of queries by reducing the amount of data scanned and processed. By adding a create_date field with a timestamp data type, Snowflake can automatically cluster the table based on this field and prune the micro-partitions that do not match the filter condition. This avoids the need to parse the JSON data and access the variant field for every record.
Option B is incorrect because it does not improve the performance of queries. By adding a create_date field with a varchar data type, Snowflake cannot automatically cluster the table based on this field and prune the micro-partitions that do not match the filter condition. This still requires parsing the JSON data and accessing the variant field for every record.
Option C is incorrect because it does not address the root cause of the performance issue. By validating the size of the warehouse being used, Snowflake can adjust the compute resources to match the data volume and parallelize the query execution. However, this does not reduce the amount of data scanned and processed, which is the main bottleneck for queries on JSON data.
Option D is incorrect because it adds unnecessary complexity and overhead to the data loading and querying process. By incorporating the use of multiple tables partitioned by date ranges, Snowflake can reduce the amount of data scanned and processed for queries that specify a date range. However, this requires creating and maintaining multiple tables, loading data into the appropriate table based on the date, and joining the tables for queries that span multiple date ranges. Reference:
Snowflake Documentation: Loading Data Using Snowpipe: This document explains how to use Snowpipe to continuously load data from external sources into Snowflake tables. It also describes the syntax and usage of the COPY INTO command, which supports various options and parameters to control the loading behavior, such as ON_ERROR, PURGE, and SKIP_FILE.
Snowflake Documentation: Date and Time Data Types and Functions: This document explains the different data types and functions for working with date and time values in Snowflake. It also describes how to set and change the session timezone and the system timezone.
Snowflake Documentation: Querying Metadata: This document explains how to query the metadata of the objects and operations in Snowflake using various functions, views, and tables. It also describes how to access the copy history information using the COPY_HISTORY function or the COPY_HISTORY view.
Snowflake Documentation: Loading JSON Data: This document explains how to load JSON data into Snowflake tables using various methods, such as the COPY INTO command, the INSERT command, or the PUT command. It also describes how to access and query JSON data using the dot notation, the FLATTEN function, or the LATERAL join.
Snowflake Documentation: Optimizing Storage for Performance: This document explains how to optimize the storage of data in Snowflake tables to improve the performance of queries. It also describes the concepts and benefits of automatic clustering, search optimization service, and materialized views.


NEW QUESTION # 79
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 in combination with data shares for the European accounts.
  • B. Migrate the European accounts in the global region and manage shares in a connected graph architecture. Deploy a Data Exchange.
  • C. Deploy to the Snowflake Marketplace making sure that invoker_share() is used in all secure views.
  • D. Deploy a Private Data Exchange and use replication to allow European data shares in the Exchange.

Answer: D

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 # 80
If your role does not own the share, but owns the objects in the share, how can you block access to the objects

  • A. Revoking the USAGE or SELECT privileges on the objects from the share owner
  • B. Revoking the USAGE or SELECT privileges with CASCADE on the objects from the share owner.
  • C. You will need to connect with the share owner

Answer: B


NEW QUESTION # 81
How is the change of local time due to daylight savings time handled in Snowflake tasks? (Choose two.)

  • A. A task will move to a suspended state during the daylight savings time change.
  • B. A frequent task execution schedule like minutes may not cause a problem, but will affect the task history.
  • C. A task schedule will follow only the specified time and will fail to handle lost or duplicated hours.
  • D. Task schedules can be designed to follow specified or local time zones to accommodate the time changes.
  • E. A task scheduled in a UTC-based schedule will have no issues with the time changes.

Answer: B,D


NEW QUESTION # 82
......

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