ARA-C01認證，ARA-C01熱門題庫

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Snowflake ARA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Performance Optimization	20%	- Virtual warehouse configuration and sizing - Query performance tuning - Clustering and partitioning strategies - Cost optimization and resource management
Topic 2: Data Engineering	25%	- Data pipeline and integration design - Transformation and processing frameworks - Data loading and unloading solutions
Topic 3: Snowflake Architecture	30%	- Development lifecycle and workload support - Data modeling and use cases - Data sharing architecture design - Data protection and recovery strategies - Object hierarchy and architectural impact
Topic 4: Accounts and Security	25%	- Security principles and implementation scenarios - Security, compliance and governance architecture - Account and database strategy design

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Snowflake ARA-C01熱門題庫 - 最新ARA-C01考證

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最新的 SnowPro Advanced Certification ARA-C01 免費考試真題 (Q78-Q83):

問題 #78
Consider the following COPY command which is loading data with CSV format into a Snowflake table from an internal stage through a data transformation query.

This command results in the following error:
SQL compilation error: invalid parameter 'validation_mode'
Assuming the syntax is correct, what is the cause of this error?

A. The VALIDATION_MODE parameter does not support COPY statements that transform data during a load.
B. The VALIDATION_MODE parameter supports COPY statements that load data from external stages only.
C. The VALIDATION_MODE parameter does not support COPY statements with CSV file formats.
D. The value return_all_errors of the option VALIDATION_MODE is causing a compilation error.

答案：A

解題說明：
* The VALIDATION_MODE parameter is used to specify the behavior of the COPY statement when loading data into a table. It is used to specify whether the COPY statement should return an error if any of the rows in the file are invalid or if it should continue loading the valid rows. The VALIDATION_MODE parameter is only supported for COPY statements that load data from external stages1.
* The query in the question uses a data transformation query to load data from an internal stage. A data transformation query is a query that transforms the data during the load process, such as parsing JSON or XML data, applying functions, or joining with other tables2.
* According to the documentation, VALIDATION_MODE does not support COPY statements that transform data during a load. If the parameter is specified, the COPY statement returns an error1.
Therefore, option C is the correct answer.
References: : COPY INTO <table> : Transforming Data During a Load

問題 #79
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

答案：D

解題說明：
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

問題 #80
The IT Security team has identified that there is an ongoing credential stuffing attack on many of their organization's system.
What is the BEST way to find recent and ongoing login attempts to Snowflake?

A. View the Users section in the Account tab in the Snowflake UI and review the last login column.
B. View the History tab in the Snowflake UI and set up a filter for SQL text that contains the text
"LOGIN".
C. Query the LOGIN_HISTORY view in the ACCOUNT_USAGE schema in the SNOWFLAKE database.
D. Call the LOGIN_HISTORY Information Schema table function.

答案：C

解題說明：
This view can be used to query login attempts by Snowflake users within the last 365 days (1 year). It provides information such as the event timestamp, the user name, the client IP, the authentication method, the success or failure status, and the error code or message if the login attempt was unsuccessful. By querying this view, the IT Security team can identify any suspicious or malicious login attempts to Snowflake and take appropriate actions to prevent credential stuffing attacks1. The other options are not the best ways to find recent and ongoing login attempts to Snowflake. Option A is incorrect because the LOGIN_HISTORY Information Schema table function only returns login events within the last 7 days, which may not be sufficient to detect credential stuffing attacks that span a longer period of time2. Option C is incorrect because the History tab in the Snowflake UI only shows the queries executed by the current user or role, not the login events of other users or roles3. Option D is incorrect because the Users section in the Account tab in the Snowflake UI only shows the last login time for each user, not the details of the login attempts or the failures.

問題 #81
An Architect has chosen to separate their Snowflake Production and QA environments using two separate Snowflake accounts.
The QA account is intended to run and test changes on data and database objects before pushing those changes to the Production account. It is a requirement that all database objects and data in the QA account need to be an exact copy of the database objects, including privileges and data in the Production account on at least a nightly basis.
Which is the LEAST complex approach to use to populate the QA account with the Production account's data and database objects on a nightly basis?

A. 1) In the Production account, create an external function that connects into the QA account and returns all the data for one specific table
2) Run the external function as part of a stored procedure that loops through each table in the Production account and populates each table in the QA account
B. 1) Create a stage in the Production account
2) Create a stage in the QA account that points to the same external object-storage location
3) Create a task that runs nightly to unload each table in the Production account into the stage
4) Use Snowpipe to populate the QA account
C. 1) Create a share in the Production account for each database
2) Share access to the QA account as a Consumer
3) The QA account creates a database directly from each share
4) Create clones of those databases on a nightly basis
5) Run tests directly on those cloned databases
D. 1) Enable replication for each database in the Production account
2) Create replica databases in the QA account
3) Create clones of the replica databases on a nightly basis
4) Run tests directly on those cloned databases

答案：C

問題 #82
Role A has the following permissions:
. USAGE on db1
. USAGE and CREATE VIEW on schemal in db1
. SELECT on tablel in schemal
Role B has the following permissions:
. USAGE on db2
. USAGE and CREATE VIEW on schema2 in db2
. SELECT on table2 in schema2
A user has Role A set as the primary role and Role B as a secondary role.
What command will fail for this user?

A. use database db2;
use schema schema2;
select * from db1.schemal.tablel union select * from table2;
B. use database db1;
use schema schemal;
create view v1 as select * from db2.schema2.table2;
C. use database db1;
use schema schemal;
select * from db2.schema2.table2;
D. use database db2;
use schema schema2;
create view v2 as select * from dbl.schemal. tablel;

答案：D

解題說明：
This command will fail because while the user has USAGE permission on db2 and schema2 through Role B, and can create a view in schema2, they do not have SELECT permission on db1.schemal.table1 with Role B. Since Role A, which has SELECT permission on db1.schemal.table1, is not the currently active role when the view v2 is being created in db2.schema2, the user does not have the necessary permissions to read from db1.schemal.table1 to create the view. Snowflake's security model requires that the active role have all necessary permissions to execute the command.

問題 #83
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