ARA-C01 TEST CERTIFICATION COST 100% PASS-RATE QUESTIONS POOL ONLY AT PREPAWAYTEST

ARA-C01 Test Certification Cost 100% Pass-Rate Questions Pool Only at PrepAwayTest

ARA-C01 Test Certification Cost 100% Pass-Rate Questions Pool Only at PrepAwayTest

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Snowflake ARA-C01: SnowPro Advanced Architect Certification is a highly recognized and sought-after certification for individuals who want to demonstrate their advanced knowledge and expertise in Snowflake's cloud data platform. SnowPro Advanced Architect Certification certification is designed for architects and advanced-level professionals who have experience in designing and implementing complex data solutions with Snowflake.

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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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is a cloud-based certification exam that is designed to validate the advanced skills and knowledge of Snowflake architects. SnowPro Advanced Architect Certification certification exam is intended for those professionals who possess a deep understanding of Snowflake data warehouses and their architecture, and who can design and implement complex Snowflake solutions using best practices. The SnowPro Advanced Architect Certification Exam is a vendor-neutral certification, which means that it is not affiliated with any particular vendor or technology.

Snowflake SnowPro Advanced Architect Certification Sample Questions (Q39-Q44):

NEW QUESTION # 39
When using the Snowflake Connector for Kafka, what data formats are supported for the messages? (Choose two.)

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

Answer: C,E

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. References: Snowflake Connector for Kafka | Snowflake Documentation, Loading Protobuf Data using the Snowflake Connector for Kafka | Snowflake Documentation


NEW QUESTION # 40
A company has several sites in different regions from which the company wants to ingest data.
Which of the following will enable this type of data ingestion?

  • A. The company must have a Snowflake account in each cloud region to be able to ingest data to that account.
  • B. The company should provision a reader account to each site and ingest the data through the reader accounts.
  • C. The company must replicate data between Snowflake accounts.
  • D. The company should use a storage integration for the external stage.

Answer: D

Explanation:
This is the correct answer because it allows the company to ingest data from different regions using a storage integration for the external stage. A storage integration is a feature that enables secure and easy access to files in external cloud storage from Snowflake. A storage integration can be used to create an external stage, which is a named location that references the files in the external storage. An external stage can be used to load data into Snowflake tables using the COPY INTO command, or to unload data from Snowflake tables using the COPY INTO LOCATION command. A storage integration can support multiple regions and cloud platforms, as long as the external storage service is compatible with Snowflake12.
References:
* Snowflake Documentation: Storage Integrations
* Snowflake Documentation: External Stages


NEW QUESTION # 41
How can the Snowflake context functions be used to help determine whether a user is authorized to see data that has column-level security enforced? (Select TWO).

  • A. Assign the accountadmin role to the user who is executing the object.
  • B. Set masking policy conditions using current_role targeting the role in use for the current session.
  • C. Set masking policy conditions using invoker_role targeting the executing role in a SQL statement.
  • D. Determine if there are ownership privileges on the masking policy that would allow the use of any function.
  • E. Set masking policy conditions using is_role_in_session targeting the role in use for the current account.

Answer: B,C

Explanation:
Snowflake context functions are functions that return information about the current session, user, role, warehouse, database, schema, or object. They can be used to help determine whether a user is authorized to see data that has column-level security enforced by setting masking policy conditions based on the context functions. The following context functions are relevant for column-level security:
* current_role: This function returns the name of the role in use for the current session. It can be used to set masking policy conditions that target the current session and are not affected by the execution context of the SQL statement. For example, a masking policy condition using current_role can allow or deny access to a column based on the role that the user activated in the session.
* invoker_role: This function returns the name of the executing role in a SQL statement. It can be used to set masking policy conditions that target the executing role and are affected by the execution context of the SQL statement. For example, a masking policy condition using invoker_role can allow or deny access to a column based on the role that the user specified in the SQL statement, such as using the AS ROLE clause or a stored procedure.
* is_role_in_session: This function returns TRUE if the user's current role in the session (i.e. the role returned by current_role) inherits the privileges of the specified role. It can be used to set masking policy conditions that involve role hierarchy and privilege inheritance. For example, a masking policy condition using is_role_in_session can allow or deny access to a column based on whether the user's current role is a lower privilege role in the specified role hierarchy.
The other options are not valid ways to use the Snowflake context functions for column-level security:
* Set masking policy conditions using is_role_in_session targeting the role in use for the current account.
This option is incorrect because is_role_in_session does not target the role in use for the current account,
* but rather the role in use for the current session. Also, the current account is not a role, but rather a logical entity that contains users, roles, warehouses, databases, and other objects.
* Determine if there are ownership privileges on the masking policy that would allow the use of any function. This option is incorrect because ownership privileges on the masking policy do not affect the use of any function, but rather the ability to create, alter, or drop the masking policy. Also, this is not a way to use the Snowflake context functions, but rather a way to check the privileges on the masking policy object.
* Assign the accountadmin role to the user who is executing the object. This option is incorrect because assigning the accountadmin role to the user who is executing the object does not involve using the Snowflake context functions, but rather granting the highest-level role to the user. Also, this is not a recommended practice for column-level security, as it would give the user full access to all objects and data in the account, which could compromise data security and governance.
References:
* Context Functions
* Advanced Column-level Security topics
* Snowflake Data Governance: Column Level Security Overview
* Data Security Snowflake Part 2 - Column Level Security


NEW QUESTION # 42
An Architect is troubleshooting a query with poor performance using the QUERY_HIST0RY function. The Architect observes that the COMPILATIONJHME is greater than the EXECUTIONJTIME.
What is the reason for this?

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

Answer: C

Explanation:
Compilation time is the time it takes for the optimizer to create an optimal query plan for the efficient execution of the query. It also involves some pruning of partition files, making the query execution efficient2 If the compilation time is greater than the execution time, it means that the optimizer spent more time analyzing the query than actually running it. This could indicate that the query has overly complex logic, such as multiple joins, subqueries, aggregations, or expressions. The complexity of the query could also affect the size and quality of the query plan, which could impact the performance of the query3 To reduce the compilation time, the Architect can try to simplify the query logic, use views or common table expressions (CTEs) to break down the query into smaller parts, or use hints to guide the optimizer. The Architect can also use the EXPLAIN command to examine the query plan and identify potential bottlenecks or inefficiencies4 References:
* 1: SnowPro Advanced: Architect | Study Guide 5
* 2: Snowflake Documentation | Query Profile Overview 6
* 3: Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time 7
* 4: Snowflake Documentation | Optimizing Query Performance 8
* : SnowPro Advanced: Architect | Study Guide
* : Query Profile Overview
* : Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time
* : Optimizing Query Performance


NEW QUESTION # 43
Which of the following are characteristics of Snowflake's parameter hierarchy?

  • A. Schema parameters override account parameters.
  • B. Table parameters override virtual warehouse parameters.
  • C. Session parameters override virtual warehouse parameters.
  • D. Virtual warehouse parameters override user parameters.

Answer: C


NEW QUESTION # 44
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