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[General] Hot Valid Test ARA-C01 Fee | Valid ARA-C01: SnowPro Advanced Architect Certifica

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【General】 Hot Valid Test ARA-C01 Fee | Valid ARA-C01: SnowPro Advanced Architect Certifica

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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is a highly specialized certification program designed for professionals who want to demonstrate their advanced technical knowledge and skills in designing, deploying, and managing Snowflake solutions. SnowPro Advanced Architect Certification certification exam is designed to assess the knowledge and skills required to build, design, and manage a Snowflake architecture at an expert level.
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To prepare for the SnowPro Advanced Architect Certification Exam, candidates must have a strong foundation in Snowflake architecture and design principles. They must also have practical experience in implementing Snowflake solutions in real-world scenarios. ARA-C01 Exam consists of multiple-choice questions and performance-based tasks that require candidates to apply their knowledge of Snowflake architecture to solve complex problems. Successful candidates receive a SnowPro Advanced Architect Certification, which is valid for two years and can be renewed by passing a recertification exam.
Snowflake ARA-C01: SnowPro Advanced Architect Certification Exam is a highly regarded certification exam in the field of data warehousing and cloud computing. It is designed to test the advanced knowledge and skills of architects who are responsible for designing and implementing complex data warehousing solutions using Snowflake's cloud data platform.
Snowflake SnowPro Advanced Architect Certification Sample Questions (Q39-Q44):NEW QUESTION # 39
What Snowflake features should be leveraged when modeling using Data Vault?
  • A. Scaling up the virtual warehouses will support parallel processing of new source loads
  • B. Snowflake's ability to hash keys so that hash key joins can run faster than integer joins
  • C. Data needs to be pre-partitioned to obtain a superior data access performance
  • D. Snowflake's support of multi-table inserts into the data model's Data Vault tables
Answer: D
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.
References:
* 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 # 40
A Snowflake Architect Is working with Data Modelers and Table Designers to draft an ELT framework specifically for data loading using Snowpipe. The Table Designers will add a timestamp column that Inserts the current tlmestamp as the default value as records are loaded into a table. The Intent is to capture the time when each record gets loaded into the table; however, when tested the timestamps are earlier than the loae_take column values returned by the copy_history function or the Copy_HISTORY view (Account Usage).
Why Is this occurring?
  • A. The CURRENT_TIMEis evaluated when the load operation is compiled in cloud services rather than when the record is inserted into the table.
  • B. The timestamps are different because there are parameter setup mismatches. The parameters need to be realigned
  • C. The Table Designer team has not used the localtimestamp or systimestamp functions in the Snowflake copy statement.
  • D. The Snowflake timezone parameter Is different from the cloud provider's parameters causing the mismatch.
Answer: A
Explanation:
The correct answer is D because the CURRENT_TIME function returns the current timestamp at the start of the statement execution, not at the time of the record insertion. Therefore, if the load operation takes some time to complete, the CURRENT_TIME value may be earlier than the actual load time.
Option A is incorrect because the parameter setup mismatches do not affect the timestamp values. The parameters are used to control the behavior and performance of the load operation, such as the file format, the error handling, the purge option, etc.
Option B is incorrect because the Snowflake timezone parameter and the cloud provider's parameters are independent of each other. The Snowflake timezone parameter determines the session timezone for displaying and converting timestamp values, while the cloud provider's parameters determine the physical location and configuration of the storage and compute resources.
Option C is incorrect because the localtimestamp and systimestamp functions are not relevant for the Snowpipe load operation. The localtimestamp function returns the current timestamp in the session timezone, while the systimestamp function returns the current timestamp in the system timezone. Neither of them reflect the actual load time of the records. 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.
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.

NEW QUESTION # 41
What are characteristics of Dynamic Data Masking? (Select TWO).
  • A. The role that creates the masking policy will always see unmasked data In query results
  • B. A masking policy can be applied to the value column of an external table.
  • C. A masking policy can be applied to a column with the GEOGRAPHY data type.
  • D. A masking policy that Is currently set on a table can be dropped.
  • E. A single masking policy can be applied to columns in different tables.
Answer: D,E
Explanation:
Dynamic Data Masking is a feature that allows masking sensitive data in query results based on the role of the user who executes the query. A masking policy is a user-defined function that specifies the masking logic and can be applied to one or more columns in one or more tables. A masking policy that is currently set on a table can be dropped using the ALTER TABLE command. A single masking policy can be applied to columns in different tables using the ALTER TABLE command with the SET MASKING POLICY clause. The other options are either incorrect or not supported by Snowflake. A masking policy cannot be applied to the value column of an external table, as external tables do not support column-level security. The role that creates the masking policy will not always see unmasked data in query results, as the masking policy can be applied to the owner role as well. A masking policy cannot be applied to a column with the GEOGRAPHY data type, as Snowflake only supports masking policies for scalar data types. References: Snowflake Documentation:
Dynamic Data Masking, Snowflake Documentation: ALTER TABLE

NEW QUESTION # 42
An Architect has a VPN_ACCESS_LOGS table in the SECURITY_LOGS schema containing timestamps of the connection and disconnection, username of the user, and summary statistics.
What should the Architect do to enable the Snowflake search optimization service on this table?
  • A. Assume role with OWNERSHIP on future tables and ADD SEARCH OPTIMIZATION on the SECURITY_LOGS schema.
  • B. Assume role with ALL PRIVILEGES on VPN_ACCESS_LOGS and ADD SEARCHOPTIMIZATION in the SECURITY_LOGS schema.
  • C. Assume role with OWNERSHIP on VPN_ACCESS_LOGS and ADD SEARCH OPTIMIZATION in the SECURITY_LOGS schema.
  • D. Assume role with ALL PRIVILEGES including ADD SEARCH OPTIMIZATION in the SECURITY LOGS schema.
Answer: C
Explanation:
According to the SnowPro Advanced: Architect Exam Study Guide, to enable the search optimization service on a table, the user must have the ADD SEARCH OPTIMIZATION privilege on the table and the schema.
The privilege can be granted explicitly or inherited from a higher-level object, such as a database or a role.
The OWNERSHIP privilege on a table implies the ADD SEARCH OPTIMIZATION privilege, so the user who owns the table can enable the search optimization service on it. Therefore, the correct answer is to assume a role with OWNERSHIP on VPN_ACCESS_LOGS and ADD SEARCH OPTIMIZATION in the SECURITY_LOGS schema. This will allow the user to enable the search optimization service on the VPN_ACCESS_LOGS table and any future tables created in the SECURITY_LOGS schema. The other options are incorrect because they either grant excessive privileges or do not grant the required privileges on the table or the schema. References:
* SnowPro Advanced: Architect Exam Study Guide, page 11, section 2.3.1
* Snowflake Documentation: Enabling the Search Optimization Service

NEW QUESTION # 43
What transformations are supported in the below SQL statement? (Select THREE).
CREATE PIPE ... AS COPY ... FROM (...)
  • A. Columns can be reordered.
  • B. The ON ERROR - ABORT statement command can be used.
  • C. Data can be filtered by an optional where clause.
  • D. Columns can be omitted.
  • E. Type casts are supported.
  • F. Incoming data can be joined with other tables.
Answer: A,C,D
Explanation:
* The SQL statement is a command for creating a pipe in Snowflake, which is an object that defines the COPY INTO <table> statement used by Snowpipe to load data from an ingestion queue into tables1. The statement uses a subquery in the FROM clause to transform the data from the staged files before loading it into the table2.
* The transformations supported in the subquery are as follows2:
* Data can be filtered by an optional WHERE clause, which specifies a condition that must be satisfied by the rows returned by the subquery. For example:
SQLAI-generated code. Review and use carefully. More info on FAQ.
createpipe mypipeas
copyintomytable
from(
select*from@mystage
wherecol1='A'andcol2>10
);
* Columns can be reordered, which means changing the order of the columns in the subquery to match the order of the columns in the target table. For example:
SQLAI-generated code. Review and use carefully. More info on FAQ.
createpipe mypipeas
copyintomytable (col1, col2, col3)
from(
selectcol3, col1, col2from@mystage
);
* Columns can be omitted, which means excluding some columns from the subquery that are not needed in the target table. For example:
SQLAI-generated code. Review and use carefully. More info on FAQ.
createpipe mypipeas
copyintomytable (col1, col2)
from(
selectcol1, col2from@mystage
);
* The other options are not supported in the subquery because2:
* Type casts are not supported, which means changing the data type of a column in the subquery.
For example, the following statement will cause an error:
SQLAI-generated code. Review and use carefully. More info on FAQ.
createpipe mypipeas
copyintomytable (col1, col2)
from(
selectcol1::date, col2from@mystage
);
* Incoming data can not be joined with other tables, which means combining the data from the staged files with the data from another table in the subquery. For example, the following statement will cause an error:
SQLAI-generated code. Review and use carefully. More info on FAQ.
createpipe mypipeas
copyintomytable (col1, col2, col3)
from(
selects.col1, s.col2, t.col3from@mystages
joinothertable tons.col1=t.col1
);
* The ON ERROR - ABORT statement command can not be used, which means aborting the entire load operation if any error occurs. This command can only be used in the COPY INTO <table> statement, not in the subquery. For example, the following statement will cause an error:
SQLAI-generated code. Review and use carefully. More info on FAQ.
createpipe mypipeas
copyintomytable
from(
select*from@mystage
onerror abort
);
1: CREATE PIPE | Snowflake Documentation
2: Transforming Data During a Load | Snowflake Documentation

NEW QUESTION # 44
......
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