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[General] ARA-C01 Certification Practice, ARA-C01 Latest Test Preparation

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【General】 ARA-C01 Certification Practice, ARA-C01 Latest Test Preparation

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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is a certification program designed for IT professionals who want to demonstrate their expertise in architecting data solutions using Snowflake. ARA-C01 exam is intended for individuals who have already obtained the SnowPro Core or SnowPro Advanced certification and have experience working with Snowflake as a data warehousing platform.
Snowflake ARA-C01: SnowPro Advanced Architect Certification Exam is a comprehensive assessment designed to measure an individual's knowledge and expertise in advanced Snowflake architecture. It is the highest-level certification offered by Snowflake and is an essential step for professionals looking to advance their careers in the field of data warehousing and cloud computing.
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Snowflake SnowPro Advanced Architect Certification Sample Questions (Q77-Q82):NEW QUESTION # 77
A Snowflake Architect created a new data share and would like to verify that only specific records in secure views are visible within the data share by the consumers.
What is the recommended way to validate data accessibility by the consumers?
  • A. Create reader accounts as shown below and impersonate the consumers by logging in with their credentials.
    create managed account reader_acctl admin_name = userl , adroin_password 'Sdfed43da!44T , type = reader;
  • B. Set the session parameter called SIMULATED_DATA_SHARING_C0NSUMER as shown below in order to impersonate the consumer accounts.
    alter session set simulated_data_sharing_consumer - 'Consumer Acctl*
  • C. Create a row access policy as shown below and assign it to the data share.
    create or replace row access policy rap_acct as (acct_id varchar) returns boolean -> case when
    'acctl_role' = current_role() then true else false end;
  • D. Alter the share settings as shown below, in order to impersonate a specific consumer account.
    alter share sales share set accounts = 'Consumerl' share restrictions = true
Answer: B
Explanation:
The SIMULATED_DATA_SHARING_CONSUMER session parameter allows a data provider to simulate the data access of a consumer account without creating a reader account or logging in with the consumer credentials. This parameter can be used to validate the data accessibility by the consumers in a data share, especially when using secure views or secure UDFs that filter data based on the current account or role. By setting this parameter to the name of a consumer account, the data provider can see the same data as the consumer would see when querying the shared database. This is a convenient and efficient way to test the data sharing functionality and ensure that only the intended data is visible to the consumers.
References:
* Using the SIMULATED_DATA_SHARING_CONSUMER Session Parameter
* SnowPro Advanced: Architect Exam Study Guide

NEW QUESTION # 78
A table for IOT devices that measures water usage is created. The table quickly becomes large and contains more than 2 billion rows.

The general query patterns for the table are:
1. DeviceId, lOT_timestamp and Customerld are frequently used in the filter predicate for the select statement
2. The columns City and DeviceManuf acturer are often retrieved
3. There is often a count on Uniqueld
Which field(s) should be used for the clustering key?
  • A. Uniqueld
  • B. City and DeviceManuf acturer
  • C. lOT_timestamp
  • D. Deviceld and Customerld
Answer: D
Explanation:
A clustering key is a subset of columns or expressions that are used to co-locate the data in the same micro-partitions, which are the units of storage in Snowflake. Clustering can improve the performance of queries that filter on the clustering key columns, as it reduces the amount of data that needs to be scanned. The best choice for a clustering key depends on the query patterns and the data distribution in the table. In this case, the columns DeviceId, IOT_timestamp, and CustomerId are frequently used in the filter predicate for the select statement, which means they are good candidates for the clustering key. The columns City and DeviceManufacturer are often retrieved, but not filtered on, so they are not as important for the clustering key.
The column UniqueId is used for counting, but it is not a good choice for the clustering key, as it is likely to have a high cardinality and a uniform distribution, which means it will not help to co-locate the data.
Therefore, the best option is to use DeviceId and CustomerId as the clustering key, as they can help to prune the micro-partitions and speed up the queries. References: Clustering Keys & Clustered Tables, Micro-partitions & Data Clustering, A Complete Guide to Snowflake Clustering

NEW QUESTION # 79
What Snowflake features should be leveraged when modeling using Data Vault?
  • A. Snowflake's ability to hash keys so that hash key joins can run faster than integer joins
  • B. Scaling up the virtual warehouses will support parallel processing of new source loads
  • C. Snowflake's support of multi-table inserts into the data model's Data Vault tables
  • D. Data needs to be pre-partitioned to obtain a superior data access performance
Answer: C
Explanation:
These two features are relevant for modeling using Data Vault on Snowflake. Data Vault is a data modeling approach that organizes data into hubs, links, and satellites. Data Vault is designed to enable high scalability, flexibility, and performance for data integration and analytics. Snowflake is a cloud data platform that supports various data modeling techniques, including Data Vault. Snowflake provides some features that can enhance the Data Vault modeling, such as:
* Snowflake's support of multi-table inserts into the data model's Data Vault tables. Multi-table inserts (MTI) are a feature that allows inserting data from a single query into multiple tables in a single DML statement. MTI can improve the performance and efficiency of loading data into Data Vault tables, especially for real-time or near-real-time data integration. MTI can also reduce the complexity and maintenance of the loading code, as well as the data duplication and latency12.
* Scaling up the virtual warehouses will support parallel processing of new source loads. Virtual warehouses are a feature that allows provisioning compute resources on demand for data processing.
Virtual warehouses can be scaled up or down by changing the size of the warehouse, which determines the number of servers in the warehouse. Scaling up the virtual warehouses can improve the performance and concurrency of processing new source loads into Data Vault tables, especially for large or complex data sets. Scaling up the virtual warehouses can also leverage the parallelism and distribution of Snowflake's architecture, which can optimize the data loading and querying34.
Snowflake Documentation: Multi-table Inserts
Snowflake Blog: Tips for Optimizing the Data Vault Architecture on Snowflake Snowflake Documentation: Virtual Warehouses Snowflake Blog: Building a Real-Time Data Vault in Snowflake

NEW QUESTION # 80
An Architect Is designing a data lake with Snowflake. The company has structured, semi-structured, and unstructured dat a. The company wants to save the data inside the data lake within the Snowflake system. The company is planning on sharing data among Its corporate branches using Snowflake data sharing.
What should be considered when sharing the unstructured data within Snowflake?
  • A. A file URL should be used to save the unstructured data into Snowflake in order to share data over secure views, with the "expiration_time" argument defined for the URL time limit.
  • B. A scoped URL should be used to save the unstructured data into Snowflake in order to share data over secure views, with a 24-hour time limit for the URL.
  • C. A pre-signed URL should be used to save the unstructured data into Snowflake in order to share data over secure views, with no time limit for the URL.
  • D. A file URL should be used to save the unstructured data into Snowflake in order to share data over secure views, with a 7-day time limit for the URL.
Answer: A
Explanation:
According to the Snowflake documentation, unstructured data files can be shared by using a secure view and Secure Data Sharing. A secure view allows the result of a query to be accessed like a table, and a secure view is specifically designated for data privacy. A scoped URL is an encoded URL that permits temporary access to a staged file without granting privileges to the stage. The URL expires when the persisted query result period ends, which is currently 24 hours. A scoped URL is recommended for file administrators to give scoped access to data files to specific roles in the same account. Snowflake records information in the query history about who uses a scoped URL to access a file, and when. Therefore, a scoped URL is the best option to share unstructured data within Snowflake, as it provides security, accountability, and control over the data access. Reference:
Sharing unstructured Data with a secure view
Introduction to Loading Unstructured Data

NEW QUESTION # 81
Please select the correct hierarchy from below

  • A. Option C
  • B. Option B
  • C. Option A
  • D. Option D
Answer: A

NEW QUESTION # 82
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