更新された2025年04月テストエンジンに練習ARA-C01テスト問題 [Q27-Q45]

更新された2025年04月テストエンジンに練習ARA-C01テスト問題

ARA-C01リアル試験問題テストエンジン問題集トレーニングには162問あります

Snowflake ARA-C01（Snowpro Advanced Architect認定）認定試験は、スノーフレークソリューションの設計と実装に関する個人の専門知識を検証するグローバルに認められた認定プログラムです。この認定は、Snowproコア認証をすでに達成しており、Snowflakeのデータウェアハウジングプラットフォームを深く理解している経験豊富な建築家向けに設計されています。 Snowflake ARA-C01試験では、スノーフレークアーキテクチャ、クエリ最適化、データモデリング、セキュリティ、パフォーマンスチューニングなど、幅広い高度なトピックをカバーしています。

Snowflake ARA-C01試験は、データ分析業界で最も求められている認定の1つです。専門家に、Snowflakeの高度な概念とテクニックに関する専門知識を実証する機会を提供します。認定プログラムは、スノーフレーク環境におけるデータウェアハウジング、データモデリング、ETL、セキュリティ、パフォーマンス最適化のベストプラクティスに関する候補者の知識を検証するように設計されています。

質問 # 27
Select the true statement

A. Privileges are granted to users. Users are granted to roles
B. Privileges are granted to both roles and users
C. Privileges are granted to roles. Roles are granted to users

正解：C

質問 # 28
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 accounts for each tenant in the Snowflake organization.
B. Create an object for each tenant strategy if row level security is not viable for isolating tenants.
C. Create an object for each tenant strategy if row level security is viable for isolating tenants.
D. Create a multi-tenant table strategy if row level security is not viable for isolating tenants.

正解：C

質問 # 29
What does a Snowflake Architect need to consider when implementing a Snowflake Connector for Kafka?

A. The Kafka connector supports key pair authentication, OAUTH. and basic authentication (for example, username and password).
B. Every Kafka message is in JSON or Avro format.
C. The default retention time for Kafka topics is 14 days.
D. The Kafka connector will create one table and one pipe to ingest data for each topic. If the connector cannot create the table or the pipe it will result in an exception.

正解：D

解説：
The Snowflake Connector for Kafka is a Kafka Connect sink connector that reads data from one or more Apache Kafka topics and loads the data into a Snowflake table. The connector supports different authentication methods to connect to Snowflake, such as key pair authentication, OAUTH, and basic authentication (for example, username and password). The connector also supports different encryption methods, such as HTTPS and SSL1. The connector does not require that every Kafka message is in JSON or Avro format, as it can handle other formats such as CSV, XML, and Parquet2. The default retention time for Kafka topics is not relevant for the connector, as it only consumes the messages that are available in the topics and does not store them in Kafka. The connector will create one table and one pipe to ingest data for each topic by default, but this behavior can be customized by using the snowflake.topic2table.map configuration property3. If the connector cannot create the table or the pipe, it will log an error and retry the operation until it succeeds or the connector is stopped4. References:
* Installing and Configuring the Kafka Connector
* Overview of the Kafka Connector
* Managing the Kafka Connector
* Troubleshooting the Kafka Connector

質問 # 30
What are some of the characteristics of result set caches? (Choose three.)

A. The result set cache is not shared between warehouses.
B. The data stored in the result cache will contribute to storage costs.
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 retention period can be reset for a maximum of 31 days.

正解：C、E、F

解説：
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

質問 # 31
Consider the following scenario where a masking policy is applied on the CREDICARDND column of the CREDITCARDINFO table. The masking policy definition Is as follows:

Sample data for the CREDITCARDINFO table is as follows:
NAME EXPIRYDATE CREDITCARDNO
JOHN DOE 2022-07-23 4321 5678 9012 1234
if the Snowflake system rotes have not been granted any additional roles, what will be the result?

A. Anyone with the Pl_ANALYTICS role will see the last 4 characters of the CREDICARDND column data in dear text.
B. The sysadmin can see the CREDICARDND column data in clear text.
C. The owner of the table will see the CREDICARDND column data in clear text.
D. Anyone with the Pl_ANALYTICS role will see the CREDICARDND column as*** 'MASKED* **'.

正解：D

解説：
* The masking policy defined in the image indicates that if a user has the PI_ANALYTICS role, they will be able to see the last 4 characters of the CREDITCARDNO column data in clear text. Otherwise, they will see 'MASKED'. Since Snowflake system roles have not been granted any additional roles, they won't have the PI_ANALYTICS role and therefore cannot view the last 4 characters of credit card numbers.
* To apply a masking policy on a column in Snowflake, you need to use the ALTER TABLE ... ALTER COLUMN command or the ALTER VIEW command and specify the policy name. For example, to apply the creditcardno_mask policy on the CREDITCARDNO column of the CREDITCARDINFO table, you can use the following command:
ALTER TABLE CREDITCARDINFO ALTER COLUMN CREDITCARDNO SET MASKING POLICY creditcardno_mask;
* For more information on how to create and use masking policies in Snowflake, you can refer to the following resources:
CREATE MASKING POLICY: This document explains the syntax and usage of the CREATE MASKING POLICY command, which allows you to create a new masking policy or replace an existing one.
Using Dynamic Data Masking: This guide provides instructions on how to configure and use dynamic data masking in Snowflake, which is a feature that allows you to mask sensitive data based on the execution context of the user.
ALTER MASKING POLICY: This document explains the syntax and usage of the ALTER MASKING POLICY command, which allows you to modify the properties of an existing masking policy.
References: 1: https://docs.snowflake.com/en/sql-reference/sql/create-masking-policy 2:
https://docs.snowflake.com/en/user-guide/security-column-ddm-use 3:
https://docs.snowflake.com/en/sql-reference/sql/alter-masking-policy

質問 # 32
Which command below will load data from result_scan to a table?

A. CREATE OR REPLACE TABLE STORE_FROM_RESULT_SCAN AS select * from result_scan(last_query_id());
B. CREATE OR REPLACE TABLE STORE_FROM_RESULT_SCAN AS select * from table(result_scan(last_query_id()));
C. INSERT INTO STORE_FROM_RESULT_SCAN select * from result_scan(last_query_id());

正解：B

質問 # 33
A company is designing a process for importing a large amount of loT JSON data from cloud storage into Snowflake. New sets of loT data get generated and uploaded approximately every 5 minutes.
Once the loT data is in Snowflake, the company needs up-to-date information from an external vendor to join to the data. This data is then presented to users through a dashboard that shows different levels of aggregation.
The external vendor is a Snowflake customer.
What solution will MINIMIZE complexity and MAXIMIZE performance?

A. 1. Create an external table over the JSON data in cloud storage.
2. Create a task that runs every 5 minutes to run a transformation procedure on new data based on a saved timestamp.
3. Ask the vendor to create a data share with the required data that can be imported into the company's Snowflake account.
4. Join the vendor's data back to the loT data using a transformation procedure.
5. Create views over the larger dataset to perform the aggregations required by the dashboard.
6. Give the views access to the dashboard tool.
B. 1. Create an external table over the JSON data in cloud storage.
2. Create a task that runs every 5 minutes to run a transformation procedure on new data, based on a saved timestamp.
3. Ask the vendor to expose an API so an external function can be used to generate a call to join the data back to the loT data in the transformation procedure.
4. Give the transformed table access to the dashboard tool.
5. Perform the aggregations on the dashboard tool.
C. 1. Create a Snowpipe to bring the JSON data into Snowflake.
2. Use streams and tasks to trigger a transformation procedure when new JSON data arrives.
3. Ask the vendor to expose an API so an external function call can be made to join the vendor's data back to the loT data in a transformation procedure.
4. Create materialized views over the larger dataset to perform the aggregations required by the dashboard.
5. Give the materialized views access to the dashboard tool.
D. 1. Create a Snowpipe to bring the JSON data into Snowflake.
2. Use streams and tasks to trigger a transformation procedure when new JSON data arrives.
3. Ask the vendor to create a data share with the required data that is then imported into the Snowflake account.
4. Join the vendor's data back to the loT data in a transformation procedure
5. Create materialized views over the larger dataset to perform the aggregations required by the dashboard.
6. Give the materialized views access to the dashboard tool.

正解：D

解説：
Using Snowpipe for continuous, automated data ingestion minimizes the need for manual intervention and ensures that data is available in Snowflake promptly after it is generated. Leveraging Snowflake's data sharing capabilities allows for efficient and secure access to the vendor's data without the need for complex API integrations. Materialized views provide pre-aggregated data for fast access, which is ideal for dashboards that require high performance1234.
References =
*Snowflake Documentation on Snowpipe4
*Snowflake Documentation on Secure Data Sharing2
*Best Practices for Data Ingestion with Snowflake1

質問 # 34
An Architect is implementing a CI/CD process. When attempting to clone a table from a production to a development environment, the cloning operation fails.
What could be causing this to happen?

A. Tables cannot be cloned from a higher environment to a lower environment.
B. The retention time for the table is set to zero.
C. The table is transient.
D. The table has a masking policy.

正解：D

解説：
Cloning a table with a masking policy can cause the cloning operation to fail because the masking policy is not automatically cloned with the table. This is due to the fact that the masking policy is considered a separate object with its own set of privileges1.
References
Snowflake Documentation on Cloning Considerations1.

質問 # 35
Create a task and a stream following the below steps. So, when the
system$stream_has_data('rawstream1') condition returns false, what will happen to the task ?
-- Create a landing table to store raw JSON data.
-- Snowpipe could load data into this table. create or replace table raw (var variant);
-- Create a stream to capture inserts to the landing table.
-- A task will consume a set of columns from this stream. create or replace stream rawstream1 on table raw;
-- Create a second stream to capture inserts to the landing table.
-- A second task will consume another set of columns from this stream. create or replace stream rawstream2 on table raw;
-- Create a table that stores the names of office visitors identified in the raw data. create or replace table names (id int, first_name string, last_name string);
-- Create a table that stores the visitation dates of office visitors identified in the raw data.
create or replace table visits (id int, dt date);
-- Create a task that inserts new name records from the rawstream1 stream into the names table
-- every minute when the stream contains records.
-- Replace the 'etl_wh' warehouse with a warehouse that your role has USAGE privilege on. create or replace task raw_to_names
warehouse = etl_wh schedule = '1 minute' when
system$stream_has_data('rawstream1') as
merge into names n
using (select var:id id, var:fname fname, var:lname lname from rawstream1) r1 on n.id = to_number(r1.id)
when matched then update set n.first_name = r1.fname, n.last_name = r1.lname
when not matched then insert (id, first_name, last_name) values (r1.id, r1.fname, r1.lname)
;
-- Create another task that merges visitation records from the rawstream1 stream into the visits table
-- every minute when the stream contains records.
-- Records with new IDs are inserted into the visits table;
-- Records with IDs that exist in the visits table update the DT column in the table.
-- Replace the 'etl_wh' warehouse with a warehouse that your role has USAGE privilege on. create or replace task raw_to_visits
warehouse = etl_wh schedule = '1 minute' when
system$stream_has_data('rawstream2') as
merge into visits v
using (select var:id id, var:visit_dt visit_dt from rawstream2) r2 on v.id = to_number(r2.id) when matched then update set v.dt = r2.visit_dt
when not matched then insert (id, dt) values (r2.id, r2.visit_dt)
;
-- Resume both tasks.
alter task raw_to_names resume;
alter task raw_to_visits resume;
-- Insert a set of records into the landing table. insert into raw
select parse_json(column1) from values
('{"id": "123","fname": "Jane","lname": "Smith","visit_dt": "2019-09-17"}'),
('{"id": "456","fname": "Peter","lname": "Williams","visit_dt": "2019-09-17"}');
-- Query the change data capture record in the table streams select * from rawstream1;
select * from rawstream2;

A. Task will be executed but no rows will be merged
B. Task will return an warning message
C. Task will be skipped

正解：C

質問 # 36
Which of the following are characteristics of Snowflake's parameter hierarchy?

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

正解：C

質問 # 37
What are characteristics of Dynamic Data Masking? (Select TWO).

A. A masking policy can be applied to a column with the GEOGRAPHY data type.
B. The role that creates the masking policy will always see unmasked data In query results
C. A masking policy that Is currently set on a table can be dropped.
D. A masking policy can be applied to the value column of an external table.
E. A single masking policy can be applied to columns in different tables.

正解：C、E

解説：
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. Reference: Snowflake Documentation: Dynamic Data Masking, Snowflake Documentation: ALTER TABLE

質問 # 38
With default settings for multi cluster warehouse, how does snowflake determines when to start a new cluster?

A. Immediately when either a query is queued or the system detects that there's one more query than the currently-running clusters can execute
B. Only if the system estimates there's enough query load to keep the cluster busy for at least 6 minutes.
C. Only if the system estimates there's enough query load to keep the cluster busy for at least 4 minutes.

正解：A

質問 # 39
The insertReport endpoint can be thought of like the UNIX command tail

A. FALSE
B. TRUE

正解：B

質問 # 40
You have create a task as below
CREATE TASK mytask1
WAREHOUSE = mywh
SCHEDULE = '5 minute'
WHEN
SYSTEM$STREAM_HAS_DATA('MYSTREAM')
AS
INSERT INTO mytable1(id,name) SELECT id, name FROM mystream WHERE METADATA$ACTION = 'INSERT';
Which statement is true below?

A. If SYSTEM$STREAM_HAS_DATA returns false, the task will go to suspended mode
B. If SYSTEM$STREAM_HAS_DATA returns false, the task will still run
C. If SYSTEM$STREAM_HAS_DATA returns false, the task will be skipped

正解：C

質問 # 41
Where can you define the file format settings?

A. While creating named file formats
B. Directly in the COPY INTO TABLE statement when loading data
C. In the table definition
D. In the named stage definition

正解：A、B、C、D

質問 # 42
An Architect needs to design a Snowflake account and database strategy to store and analyze large amounts of structured and semi-structured data. There are many business units and departments within the company. The requirements are scalability, security, and cost efficiency.
What design should be used?

A. Use Snowflake's data lake functionality to store and analyze all data in a central location, without the need for structured schemas or indexes
B. Create a single Snowflake account and database for all data storage and analysis needs, regardless of data volume or complexity.
C. Set up separate Snowflake accounts and databases for each department or business unit, to ensure data isolation and security.
D. Use a centralized Snowflake database for core business data, and use separate databases for departmental or project-specific data.

正解：D

解説：
The best design to store and analyze large amounts of structured and semi-structured data for different business units and departments is to use a centralized Snowflake database for core business data, and use separate databases for departmental or project-specific data. This design allows for scalability, security, and cost efficiency by leveraging Snowflake's features such as:
* Database cloning: Cloning a database creates a zero-copy clone that shares the same data files as the original database, but can be modified independently. This reduces storage costs and enables fast and consistent data replication for different purposes.
* Database sharing: Sharing a database allows granting secure and governed access to a subset of data in a database to other Snowflake accounts or consumers. This enables data collaboration and monetization across different business units or external partners.
* Warehouse scaling: Scaling a warehouse allows adjusting the size and concurrency of a warehouse to match the performance and cost requirements of different workloads. This enables optimal resource utilization and flexibility for different data analysis needs. References: Snowflake Documentation:
Database Cloning, Snowflake Documentation: Database Sharing, [Snowflake Documentation:
Warehouse Scaling]

質問 # 43
To increase performance, materialized views can be created on external table without any additional cost