ARA-C01 BRAIN EXAM - RELIABLE ARA-C01 GUIDE FILES

ARA-C01 Brain Exam - Reliable ARA-C01 Guide Files

ARA-C01 Brain Exam - Reliable ARA-C01 Guide Files

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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.

One of the unique features of the SnowPro Advanced Architect Certification exam is that it is an online, proctored exam. This means that candidates can take the exam from anywhere in the world, at any time, while being monitored by a proctor through a webcam. This format ensures the integrity and security of the exam, while also providing convenience and flexibility to candidates.

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Snowflake SnowPro Advanced Architect Certification Sample Questions (Q161-Q166):

NEW QUESTION # 161
Company A would like to share data in Snowflake with Company B. Company B is not on the same cloud platform as Company A.
What is required to allow data sharing between these two companies?

  • A. Company A and Company B must agree to use a single cloud platform: Data sharing is only possible if the companies share the same cloud provider.
  • B. Create a pipeline to write shared data to a cloud storage location in the target cloud provider.
  • C. Ensure that all views are persisted, as views cannot be shared across cloud platforms.
  • D. Setup data replication to the region and cloud platform where the consumer resides.

Answer: D

Explanation:
Explanation
According to the SnowPro Advanced: Architect documents and learning resources, the requirement to allow data sharing between two companies that are not on the same cloud platform is to set up data replication to the region and cloud platform where the consumer resides. Data replication is a feature of Snowflake that enables copying databases across accounts in different regions and cloud platforms. Data replication allows data providers to securely share data with data consumers across different regions and cloud platforms by creating a replica database in the consumer's account. The replica database is read-only and automatically synchronized with the primary database in the provider's account. Data replication is useful for scenarios where data sharing is not possible or desirable due to latency, compliance, or security reasons1. The other options are incorrect because they are not required or feasible to allow data sharing between two companies that are not on the same cloud platform. Option A is incorrect because creating a pipeline to write shared data to a cloud storage location in the target cloud provider is not a secure or efficient way of sharing data. It would require additional steps to load the data from the cloud storage to the consumer's account, and it would not leverage the benefits of Snowflake's data sharing features. Option B is incorrect because ensuring that all views are persisted is not relevant for data sharing across cloud platforms. Views can be shared across cloud platforms as long as they reference objects in the same database. Persisting views is an option to improve the performance of querying views, but it is not required for data sharing2. Option D is incorrect because Company A and Company B do not need to agree to use a single cloud platform. Data sharing is possible across different cloud platforms using data replication or other methods, such as listings or auto-fulfillment3. References: ReplicatingDatabases Across Multiple Accounts | Snowflake Documentation, Persisting Views | Snowflake Documentation, Sharing Data Across Regions and Cloud Platforms | Snowflake Documentation


NEW QUESTION # 162
Which of the below commands will use warehouse credits?

  • A. SELECT MAX(FLAKE_ID) FROM SNOWFLAKE;
  • B. SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID;
  • C. SELECT COUNT(*) FROM SNOWFLAKE;
  • D. SHOW TABLES LIKE 'SNOWFL%';

Answer: A,B,C

Explanation:
Explanation
* Warehouse credits are used to pay for the processing time used by each virtual warehouse in Snowflake.
A virtual warehouse is a cluster of compute resources that enables executing queries, loading data, and performing other DML operations. Warehouse credits are charged based on the number of virtual warehouses you use, how long they run, and their size1.
* Among the commands listed in the question, the following ones will use warehouse credits:
* SELECT MAX(FLAKE_ID) FROM SNOWFLAKE: This command will use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the maximum value of the FLAKE_ID column2. Therefore, option B is correct.
* SELECT COUNT(*) FROM SNOWFLAKE: This command will also use warehouse credits
* because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the number of rows in the table3. Therefore, option C is correct.
* SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID: This command will also use warehouse credits because it is a query that requires a virtual warehouseto execute. The query will scan the SNOWFLAKE table and return the number of rows for each distinct value of the FLAKE_ID column4. Therefore, option D is correct.
* The command that will not use warehouse credits is:
* SHOW TABLES LIKE 'SNOWFL%': This command will not use warehouse credits because it is a metadata operation that does not require a virtual warehouse to execute. The command will return the names of the tables that match the pattern 'SNOWFL%' in the current database and schema5. Therefore, option A is incorrect.
References: : Understanding Compute Cost : MAX Function : COUNT Function : GROUP BY Clause : SHOW TABLES


NEW QUESTION # 163
A Snowflake Architect is designing an application and tenancy strategy for an organization where strong legal isolation rules as well as multi-tenancy are requirements.
Which approach will meet these requirements if Role-Based Access Policies (RBAC) is a viable option for isolating tenants?

  • A. Create an object for each tenant strategy if row level security is viable for isolating tenants.
  • B. Create a multi-tenant table strategy if row level security is not viable for isolating tenants.
  • C. Create accounts for each tenant in the Snowflake organization.
  • D. Create an object for each tenant strategy if row level security is not viable for isolating tenants.

Answer: C

Explanation:
This approach meets the requirements of strong legal isolation and multi-tenancy. By creating separate accounts for each tenant, the application can ensure that each tenant has its own dedicated storage, compute, and metadata resources, as well as its own encryption keys and security policies. This provides the highest level of isolation and data protection among the tenancy models. Furthermore, by creating the accounts within the same Snowflake organization, the application can leverage the features of Snowflake Organizations, such as centralized billing, account management, and cross-account data sharing.
Reference:
Snowflake Organizations Overview | Snowflake Documentation
Design Patterns for Building Multi-Tenant Applications on Snowflake


NEW QUESTION # 164
What are some of the characteristics of result set caches? (Choose three.)

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

Answer: B,C,E

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. Reference: Using Persisted Query Results | Snowflake Documentation, Time Travel | Snowflake Documentation


NEW QUESTION # 165
An Architect is troubleshooting a query with poor performance using the QUERY function. The Architect observes that the COMPILATION_TIME Is greater than the EXECUTION_TIME.
What is the reason for this?

  • A. The query has overly complex logic.
  • B. The query Is reading from remote storage
  • C. The query is processing a very large dataset.
  • D. The query Is queued for execution.

Answer: A

Explanation:
The correct answer is B because the compilation time is the time it takes for the optimizer to create an optimal query plan for the efficient execution of the query. The compilation time depends on the complexity of the query, such as the number of tables, columns, joins, filters, aggregations, subqueries, etc. The more complex the query, the longer it takes to compile.
Option A is incorrect because the query processing time is not affected by the size of the dataset, but by the size of the virtual warehouse. Snowflake automatically scales the compute resources to match the data volume and parallelizes the query execution. The size of the dataset may affect the execution time, but not the compilation time.
Option C is incorrect because the query queue time is not part of the compilation time or the execution time. It is a separate metric that indicates how long the query waits for a warehouse slot before it starts running. The query queue time depends on the warehouse load, concurrency, and priority settings.
Option D is incorrect because the query remote IO time is not part of the compilation time or the execution time. It is a separate metric that indicates how long the query spends reading data from remote storage, such as S3 or Azure Blob Storage. The query remote IO time depends on the network latency, bandwidth, and caching efficiency. Reference:
Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time: This article explains why the total duration (compilation + execution) time is an essential metric to measure query performance in Snowflake. It discusses the reasons for the long compilation time, including query complexity and the number of tables and columns.
Exploring Execution Times: This document explains how to examine the past performance of queries and tasks using Snowsight or by writing queries against views in the ACCOUNT_USAGE schema. It also describes the different metrics and dimensions that affect query performance, such as duration, compilation, execution, queue, and remote IO time.
What is the "compilation time" and how to optimize it?: This community post provides some tips and best practices on how to reduce the compilation time, such as simplifying the query logic, using views or common table expressions, and avoiding unnecessary columns or joins.


NEW QUESTION # 166
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