手に入れよう！は2025年最新の有効な実践問題であなたのARA-C01試験を合格させる（本日更新された164問) [Q14-Q35]

手に入れよう！は2025年最新の有効な実践問題であなたのARA-C01試験を合格させる（本日更新された164問)

SnowPro Advanced Certification ARA-C01試験実践テスト問題集解答豪華セットを使おう！

Snowflake ARA-C01（Snowpro Advanced Architect認定）試験は、クラウドベースのデータウェアハウジングおよび分析プラットフォームであるSnowflakeを使用する専門家向けに設計された認定プログラムです。この認定は、スノーフレークの建築、ベストプラクティス、および機能性を深く理解している上級レベルのアーキテクト向けに設計されています。この試験では、さまざまなシナリオで複雑なスノーフレークソリューションを設計および実装する候補者の能力を測定します。

質問 # 14
A table contains five columns and it has millions of records. The cardinality distribution of the columns is shown below:

Column C4 and C5 are mostly used by SELECT queries in the GROUP BY and ORDER BY clauses.
Whereas columns C1, C2 and C3 are heavily used in filter and join conditions of SELECT queries.
The Architect must design a clustering key for this table to improve the query performance.
Based on Snowflake recommendations, how should the clustering key columns be ordered while defining the multi-column clustering key?

A. C5, C4, C2
B. C1, C3, C2
C. C3, C4, C5
D. C2, C1, C3

正解：D

解説：
According to the Snowflake documentation, the following are some considerations for choosing clustering for a table1:
* Clustering is optimal when either:
* You require the fastest possible response times, regardless of cost.
* Your improved query performance offsets the credits required to cluster and maintain the table.
* Clustering is most effective when the clustering key is used in the following types of query predicates:
* Filter predicates (e.g. WHERE clauses)
* Join predicates (e.g. ON clauses)
* Grouping predicates (e.g. GROUP BY clauses)
* Sorting predicates (e.g. ORDER BY clauses)
* Clustering is less effective when the clustering key is not used in any of the above query predicates, or when the clustering key is used in a predicate that requires a function or expression to be applied to the key (e.g. DATE_TRUNC, TO_CHAR, etc.).
* For most tables, Snowflake recommends a maximum of 3 or 4 columns (or expressions) per key.
Adding more than 3-4 columns tends to increase costs more than benefits.
Based on these considerations, the best option for the clustering key columns is C. C1, C3, C2, because:
* These columns are heavily used in filter and join conditions of SELECT queries, which are the most effective types of predicates for clustering.
* These columns have high cardinality, which means they have many distinct values and can help reduce the clustering skew and improve the compression ratio.
* These columns are likely to be correlated with each other, which means they can help co-locate similar rows in the same micro-partitions and improve the scan efficiency.
* These columns do not require any functions or expressions to be applied to them, which means they can be directly used in the predicates without affecting the clustering.
1: Considerations for Choosing Clustering for a Table | Snowflake Documentation

質問 # 15
A company is storing large numbers of small JSON files (ranging from 1-4 bytes) that are received from IoT devices and sent to a cloud provider. In any given hour, 100,000 files are added to the cloud provider.
What is the MOST cost-effective way to bring this data into a Snowflake table?

A. An external table
B. A stream
C. A pipe
D. A copy command at regular intervals

正解：C

解説：
A pipe is a Snowflake object that continuously loads data from files in a stage (internal or external) into a table. A pipe can be configured to use auto-ingest, which means that Snowflake automatically detects new or modified files in the stage and loads them into the table without any manual intervention1.
A pipe is the most cost-effective way to bring large numbers of small JSON files into a Snowflake table, because it minimizes the number of COPY commands executed and the number of micro-partitions created. A pipe can use file aggregation, which means that it can combine multiple small files into a single larger file before loading them into the table. This reduces the load time and the storage cost of the data2.
An external table is a Snowflake object that references data files stored in an external location, such as Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage. An external table does not store the data in Snowflake, but only provides a view of the data for querying. An external table is not a cost-effective way to bring data into a Snowflake table, because it does not support file aggregation, and it requires additional network bandwidth and compute resources to query the external data3.
A stream is a Snowflake object that records the history of changes (inserts, updates, and deletes) made to a table. A stream can be used to consume the changes from a table and apply them to another table or a task. A stream is not a way to bring data into a Snowflake table, but a way to process the data after it is loaded into a table4.
A copy command is a Snowflake command that loads data from files in a stage into a table. A copy command can be executed manually or scheduled using a task. A copy command is not a cost-effective way to bring large numbers of small JSON files into a Snowflake table, because it does not support file aggregation, and it may create many micro-partitions that increase the storage cost of the data5.

質問 # 16
A Snowflake Architect is designing a multi-tenant application strategy for an organization in the Snowflake Data Cloud and is considering using an Account Per Tenant strategy.
Which requirements will be addressed with this approach? (Choose two.)

A. Security and Role-Based Access Control (RBAC) policies must be simple to configure.
B. Storage costs must be optimized.
C. There needs to be fewer objects per tenant.
D. Tenant data shape may be unique per tenant.
E. Compute costs must be optimized.

正解：A、D

解説：
The Account Per Tenant strategy involves creating separate Snowflake accounts for each tenant within the multi-tenant application. This approach offers a number of advantages.
Option B: With separate accounts, each tenant's environment is isolated, making security and RBAC policies simpler to configure and maintain. This is because each account can have its own set of roles and privileges without the risk of cross-tenant access or the complexity of maintaining a highly granular permission model within a shared environment.
Option D: This approach also allows for each tenant to have a unique data shape, meaning that the database schema can be tailored to the specific needs of each tenant without affecting others. This can be essential when tenants have different data models, usage patterns, or application customizations.

質問 # 17
What is the best practice to follow when calling the SNOWPIPE REST API loadHistoryScan

A. Reading the last 10 minutes of history every 8 minutes
B. Read the last 7 days of history every hour
C. Read the last 24 hours of history every minute

正解：A

質問 # 18
Assuming all Snowflake accounts are using an Enterprise edition or higher, in which development and testing scenarios would be copying of data be required, and zero-copy cloning not be suitable? (Select TWO).

A. The release process requires pre-production testing of changes with data of production scale and complexity. For security reasons, pre-production also runs in the production account.
B. Data is in a production Snowflake account that needs to be provided to Developers in a separate development/testing Snowflake account in the same cloud region.
C. Developers create their own copies of a standard test database previously created for them in the development account, for their initial development and unit testing.
D. Developers create their own datasets to work against transformed versions of the live data.
E. Production and development run in different databases in the same account, and Developers need to see production-like data but with specific columns masked.

正解：B、D

解説：
Zero-copy cloning is a feature that allows creating a clone of a table, schema, or database without physically copying the data. Zero-copy cloning is suitable for scenarios where the cloned object needs to have the same data and metadata as the original object, and where the cloned object does not need to be modified or updated frequently. Zero-copy cloning is also suitable for scenarios where the cloned object needs to be shared within the same Snowflake account or across different accounts in the same cloud region2 However, zero-copy cloning is not suitable for scenarios where the cloned object needs to have different data or metadata than the original object, or where the cloned object needs to be modified or updated frequently. Zero-copy cloning is also not suitable for scenarios where the cloned object needs to be shared across different accounts in different cloud regions. In these scenarios, copying of data would be required, either by using the COPY INTO command or by using data sharing with secure views3 The following are examples of development and testing scenarios where copying of data would be required, and zero-copy cloning would not be suitable:
Developers create their own datasets to work against transformed versions of the live data. This scenario requires copying of data because the developers need to modify the data or metadata of the cloned object to perform transformations, such as adding, deleting, or updating columns, rows, or values. Zero-copy cloning would not be suitable because it would create a read-only clone that shares the same data and metadata as the original object, and any changes made to the clone would affect the original object as well4 Data is in a production Snowflake account that needs to be provided to Developers in a separate development/testing Snowflake account in the same cloud region. This scenario requires copying of data because the data needs to be shared across different accounts in the same cloud region. Zero-copy cloning would not be suitable because it would create a clone within the same account as the original object, and it would not allow sharing the clone with another account. To share data across different accounts in the same cloud region, data sharing with secure views or COPY INTO command can be used5 The following are examples of development and testing scenarios where zero-copy cloning would be suitable, and copying of data would not be required:
Production and development run in different databases in the same account, and Developers need to see production-like data but with specific columns masked. This scenario can use zero-copy cloning because the data needs to be shared within the same account, and the cloned object does not need to have different data or metadata than the original object. Zero-copy cloning can create a clone of the production database in the development database, and the clone can have the same data and metadata as the original database. To mask specific columns, secure views can be created on top of the clone, and the developers can access the secure views instead of the clone directly6 Developers create their own copies of a standard test database previously created for them in the development account, for their initial development and unit testing. This scenario can use zero-copy cloning because the data needs to be shared within the same account, and the cloned object does not need to have different data or metadata than the original object. Zero-copy cloning can create a clone of the standard test database for each developer, and the clone can have the same data and metadata as the original database. The developers can use the clone for their initial development and unit testing, and any changes made to the clone would not affect the original database or other clones7 The release process requires pre-production testing of changes with data of production scale and complexity. For security reasons, pre-production also runs in the production account. This scenario can use zero-copy cloning because the data needs to be shared within the same account, and the cloned object does not need to have different data or metadata than the original object. Zero-copy cloning can create a clone of the production database in the pre-production database, and the clone can have the same data and metadata as the original database. The pre-production testing can use the clone to test the changes with data of production scale and complexity, and any changes made to the clone would not affect the original database or the production environment8 Reference:
1: SnowPro Advanced: Architect | Study Guide 9
2: Snowflake Documentation | Cloning Overview
3: Snowflake Documentation | Loading Data Using COPY into a Table
4: Snowflake Documentation | Transforming Data During a Load
5: Snowflake Documentation | Data Sharing Overview
6: Snowflake Documentation | Secure Views
7: Snowflake Documentation | Cloning Databases, Schemas, and Tables
8: Snowflake Documentation | Cloning for Testing and Development
: SnowPro Advanced: Architect | Study Guide
: Cloning Overview
: Loading Data Using COPY into a Table
: Transforming Data During a Load
: Data Sharing Overview
: Secure Views
: Cloning Databases, Schemas, and Tables
: Cloning for Testing and Development

質問 # 19
What integration object should be used to place restrictions on where data may be exported?

A. API integration
B. Stage integration
C. Security integration
D. Storage integration

正解：D

解説：
In Snowflake, a storage integration is used to define and configure external cloud storage that Snowflake will interact with. This includes specifying security policies for access control. One of the main features of storage integrations is the ability to set restrictions on where data may be exported. This is done by binding the storage integration to specific cloud storage locations, thereby ensuring that Snowflake can only access those locations. It helps to maintain control over the data and complies with data governance and security policies by preventing unauthorized data exports to unspecified locations.

質問 # 20
A company is designing its serving layer for data that is in cloud storage. Multiple terabytes of the data will be used for reporting. Some data does not have a clear use case but could be useful for experimental analysis. This experimentation data changes frequently and is sometimes wiped out and replaced completely in a few days.
The company wants to centralize access control, provide a single point of connection for the end-users, and maintain data governance.
What solution meets these requirements while MINIMIZING costs, administrative effort, and development overhead?

A. Import all the data in cloud storage to be used for reporting into a Snowflake schema with native tables. Then create two different roles with grants to the different datasets to match the different user personas, and grant these roles to the corresponding users.
B. Import the data used for reporting into a Snowflake schema with native tables. Then create external tables pointing to the cloud storage folders used for the experimentation data. Then create two different roles with grants to the different datasets to match the different user personas, and grant these roles to the corresponding users.
C. Import all the data in cloud storage to be used for reporting into a Snowflake schema with native tables. Then create a role that has access to this schema and manage access to the data through that role.
D. Import the data used for reporting into a Snowflake schema with native tables. Then create views that have SELECT commands pointing to the cloud storage files for the experimentation data. Then create two different roles to match the different user personas, and grant these roles to the corresponding users.

正解：B

解説：
The most cost-effective and administratively efficient solution is to use a combination of native and external tables. Native tables for reporting data ensure performance and governance, while external tables allow for flexibility with frequently changing experimental data. Creating roles with specific grants to datasets aligns with the principle of least privilege, centralizing access control and simplifying user management12.
Reference
* Snowflake Documentation on Optimizing Cost1.
* Snowflake Documentation on Controlling Cost2.

質問 # 21
Which technique will efficiently ingest and consume semi-structured data for Snowflake data lake workloads?

A. Schema-on-read
B. Information schema
C. Schema-on-write
D. IDEF1X

正解：A

質問 # 22
Dynamic data masking is supported in which editions of snowflake

A. Business Critical
B. Enterprise
C. VPS
D. Standard

正解：A、B、C

質問 # 23
It is recommended to assign ACCOUNTADMIN role to atleast two user

A. TRUE
B. FALSE

正解：A

質問 # 24
When using the copy into <table> command with the CSV file format, how does the match_by_column_name parameter behave?

A. The command will return a warning stating that the file has unmatched columns.
B. It expects a header to be present in the CSV file, which is matched to a case-sensitive table column name.
C. The parameter will be ignored.
D. The command will return an error.

正解：C

解説：
Option B is the best design to meet the requirements because it uses Snowpipe to ingest the data continuously and efficiently as new records arrive in the object storage, leveraging event notifications. Snowpipe is a service that automates the loading of data from external sources into Snowflake tables1. It also uses streams and tasks to orchestrate transformations on the ingested data. Streams are objects that store the change history of a table, and tasks are objects that execute SQL statements on a schedule or when triggered by another task2.
Option B also uses an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. An external function is a user-defined function that calls an external API, such as Amazon Comprehend, to perform computations that are not natively supported by Snowflake3. Finally, option B uses the Snowflake Marketplace to make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions. The Snowflake Marketplace is a platform that enables data providers to list and share their data sets with data consumers, regardless of the cloud platform or region they use4.
Option A is not the best design because it uses copy into to ingest the data, which is not as efficient and continuous as Snowpipe. Copy into is a SQL command that loads data from files into a table in a single transaction. It also exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.
Option C is not the best design because it uses Amazon EMR and PySpark to ingest and transform the data, which also increases the operational complexity and maintenance of the infrastructure. Amazon EMR is a cloud service that provides a managed Hadoop framework to process and analyze large-scale data sets.
PySpark is a Python API for Spark, a distributed computing framework that can run on Hadoop. Option C also develops a python program to do model inference by leveraging the Amazon Comprehend text analysis API, which increases the development effort.
Option D is not the best design because it is identical to option A, except for the ingestion method. It still exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.
References: 1: Snowpipe Overview 2: Using Streams and Tasks to Automate Data Pipelines 3: External Functions Overview 4: Snowflake Data Marketplace Overview : [Loading Data Using COPY INTO] : [What is Amazon EMR?] : [PySpark Overview]
* The copy into <table> command is used to load data from staged files into an existing table in Snowflake. The command supports various file formats, such as CSV, JSON, AVRO, ORC, PARQUET, and XML1.
* The match_by_column_name parameter is a copy option that enables loading semi-structured data into separate columns in the target table that match corresponding columns represented in the source data. The parameter can have one of the following values2:
* CASE_SENSITIVE: The column names in the source data must match the column names in the target table exactly, including the case. This is the default value.
* CASE_INSENSITIVE: The column names in the source data must match the column names in
* the target table, but the case is ignored.
* NONE: The column names in the source data are ignored, and the data is loaded based on the order of the columns in the target table.
* The match_by_column_name parameter only applies to semi-structured data, such as JSON, AVRO, ORC, PARQUET, and XML. It does not apply to CSV data, which is considered structured data2.
* When using the copy into <table> command with the CSV file format, the match_by_column_name parameter behaves as follows2:
* It expects a header to be present in the CSV file, which is matched to a case-sensitive table column name. This means that the first row of the CSV file must contain the column names, and they must match the column names in the target table exactly, including the case. If the header is missing or does not match, the command will return an error.
* The parameter will not be ignored, even if it is set to NONE. The command will still try to match the column names in the CSV file with the column names in the target table, and will return an error if they do not match.
* The command will not return a warning stating that the file has unmatched columns. It will either load the data successfully if the column names match, or return an error if they do not match.
References:
* 1: COPY INTO <table> | Snowflake Documentation
* 2: MATCH_BY_COLUMN_NAME | Snowflake Documentation

質問 # 25
Based on the architecture in the image, how can the data from DB1 be copied into TBL2? (Select TWO).

正解：B、E

解説：
The architecture in the image shows a Snowflake data platform with two databases, DB1 and DB2, and two schemas, SH1 and SH2. DB1 contains a table TBL1 and a stage STAGE1. DB2 contains a table TBL2. The image also shows a snippet of code written in SQL language that copies data from STAGE1 to TBL2 using a file format FF PIPE 1.
To copy data from DB1 to TBL2, there are two possible options among the choices given:
Option B: Use a named external stage that references STAGE1. This option requires creating an external stage object in DB2.SH2 that points to the same location as STAGE1 in DB1.SH1. The external stage can be created using the CREATE STAGE command with the URL parameter specifying the location of STAGE11. For example:
SQLAI-generated code. Review and use carefully. More info on FAQ.
use database DB2;
use schema SH2;
create stage EXT_STAGE1
url = @DB1.SH1.STAGE1;
Then, the data can be copied from the external stage to TBL2 using the COPY INTO command with the FROM parameter specifying the external stage name and the FILE FORMAT parameter specifying the file format name2. For example:
SQLAI-generated code. Review and use carefully. More info on FAQ.
copy into TBL2
from @EXT_STAGE1
file format = (format name = DB1.SH1.FF PIPE 1);
Option E: Use a cross-database query to select data from TBL1 and insert into TBL2. This option requires using the INSERT INTO command with the SELECT clause to query data from TBL1 in DB1.SH1 and insert it into TBL2 in DB2.SH2. The query must use the fully-qualified names of the tables, including the database and schema names3. For example:
SQLAI-generated code. Review and use carefully. More info on FAQ.
use database DB2;
use schema SH2;
insert into TBL2
select * from DB1.SH1.TBL1;
The other options are not valid because:
Option A: It uses an invalid syntax for the COPY INTO command. The FROM parameter cannot specify a table name, only a stage name or a file location2.
Option C: It uses an invalid syntax for the COPY INTO command. The FILE FORMAT parameter cannot specify a stage name, only a file format name or options2.
Option D: It uses an invalid syntax for the CREATE STAGE command. The URL parameter cannot specify a table name, only a file location1.
Reference:
1: CREATE STAGE | Snowflake Documentation
2: COPY INTO table | Snowflake Documentation
3: Cross-database Queries | Snowflake Documentation

質問 # 26
What are some of the characteristics of result set caches? (Choose three.)

A. Snowflake persists the data results for 24 hours.
B. The retention period can be reset for a maximum of 31 days.
C. The result set cache is not shared between warehouses.
D. Each time persisted results for a query are used, a 24-hour retention period is reset.
E. Time Travel queries can be executed against the result set cache.
F. The data stored in the result cache will contribute to storage costs.

正解：A、C、D

解説：
In Snowflake, the characteristics of result set caches include persistence of data results for 24 hours (B), each use of persisted results resets the 24-hour retention period (C), and result set caches are not shared between different warehouses (F). The result set cache is specifically designed to avoid repeated execution of the same query within this timeframe, reducing computational overhead and speeding up query responses. These caches do not contribute to storage costs, and their retention period cannot be extended beyond the default duration nor up to 31 days, as might be misconstrued.
References:Snowflake Documentation on Result Set Caching.

質問 # 27
You have a need to make external file data available to your users with the lowest latency. The files are on an external stage in AWS.
What feature of Snowflake is the most appropriate to use

A. Secure View
B. SnowPipe
C. Materialized View

正解：B

質問 # 28
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 support of multi-table inserts into the data model's Data Vault tables
C. Data needs to be pre-partitioned to obtain a superior data access performance
D. Snowflake's ability to hash keys so that hash key joins can run faster than integer joins

正解：A、B

解説：
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.
Reference:
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

質問 # 29
How do Snowflake databases that are created from shares differ from standard databases that are not created from shares? (Choose three.)

A. Shared databases must be refreshed in order for new data to be visible.
B. Shared databases can also be created as transient databases.
C. Shared databases are not supported by Time Travel.
D. Shared databases will have the PUBLIC or INFORMATION_SCHEMA schemas without explicitly granting these schemas to the share.
E. Shared databases are read-only.
F. Shared databases cannot be cloned.

正解：C、E、F

解説：
According to the SnowPro Advanced: Architect documents and learning resources, the ways that Snowflake databases that are created from shares differ from standard databases that are not created from shares are:
* Shared databases are read-only. This means that the data consumers who access the shared databases cannot modify or delete the data or the objects in the databases. The data providers who share the databases have full control over the data and the objects, and can grant or revoke privileges on them1.
* Shared databases cannot be cloned. This means that the data consumers who access the shared databases cannot create a copy of the databases or the objects in the databases. The data providers who share the databases can clone the databases or the objects, but the clones are not automatically shared2.
* Shared databases are not supported by Time Travel. This means that the data consumers who access the shared databases cannot use the AS OF clause to query historical data or restore deleted data. The data providers who share the databases can use Time Travel on the databases or the objects, but the historical
* data is not visible to the data consumers3.
The other options are incorrect because they are not ways that Snowflake databases that are created from shares differ from standard databases that are not created from shares. Option B is incorrect because shared databases do not need to be refreshed in order for new data to be visible. The data consumers who access the shared databases can see the latest data as soon as the data providers update the data1. Option E is incorrect because shared databases will not have the PUBLIC or INFORMATION_SCHEMA schemas without explicitly granting these schemas to the share. The data consumers who access the shared databases can only see the objects that the data providers grant to the share, and the PUBLIC and INFORMATION_SCHEMA schemas are not granted by default4. Option F is incorrect because shared databases cannot be created as transient databases. Transient databases are databases that do not support Time Travel or Fail-safe, and can be dropped without affecting the retention period of the data. Shared databases are always created as permanent databases, regardless of the type of the source database5. References: Introduction to Secure Data Sharing | Snowflake Documentation, Cloning Objects | Snowflake Documentation, Time Travel | Snowflake Documentation, Working with Shares | Snowflake Documentation, CREATE DATABASE | Snowflake Documentation

質問 # 30
A healthcare company is deploying a Snowflake account that may include Personal Health Information (PHI). The company must ensure compliance with all relevant privacy standards.
Which best practice recommendations will meet data protection and compliance requirements? (Choose three.)

A. Use, at minimum, the Business Critical edition of Snowflake.
B. Create Dynamic Data Masking policies and apply them to columns that contain PHI.
C. Use the Internal Tokenization feature to obfuscate sensitive data.
D. Avoid sharing data with partner organizations.
E. Use the External Tokenization feature to obfuscate sensitive data.
F. Rewrite SQL queries to eliminate projections of PHI data based on current_role().

正解：A、B、E

質問 # 31
A user named USER_01 needs access to create a materialized view on a schema EDW. STG_SCHEMA. How can this access be provided?

A. GRANT ROLE NEW_ROLE TO USER_01;GRANT CREATE MATERIALIZED VIEW ON EDW.STG_SCHEMA TO NEW_ROLE;
B. GRANT ROLE NEW_ROLE TO USER USER_01;GRANT CREATE MATERIALIZED VIEW ON SCHEMA ECW.STG_SCHEKA TO NEW_ROLE;
C. GRANT CREATE MATERIALIZED VIEW ON SCHEMA EDW.STG_SCHEMA TO USER
USER_01;
D. GRANT CREATE MATERIALIZED VIEW ON DATABASE EDW TO USER USERJD1;

正解：C

解説：
* The correct answer is A because it grants the specific privilege to create a materialized view on the schema EDW.STG_SCHEMA to the user USER_01 directly.
* Option B is incorrect because it grants the privilege to create a materialized view on the entire database EDW, which is too broad and unnecessary. Also, there is a typo in the user name (USERJD1 instead of USER_01).
* Option C is incorrect because it grants the privilege to create a materialized view on a different schema (ECW.STG_SCHEKA instead of EDW.STG_SCHEMA). Also, there is no need to create a new role for this purpose.
* Option D is incorrect because it grants the privilege to create a materialized view on an invalid object (EDW.STG_SCHEMA is not a valid schema name, it should be EDW.STG_SCHEMA). Also, there is no need to create a new role for this purpose. References:
* Snowflake Documentation: CREATE MATERIALIZED VIEW
* Snowflake Documentation: Working with Materialized Views
* [Snowflake Documentation: GRANT Privileges on a Schema]

質問 # 32
Which statement is not true about shared database?

A. Shared databases are read only
B. Shared databases can be re-shared with other accounts
C. Shared databases cannot be cloned
D. Time travel is not supported on a shared database

正解：B

質問 # 33
Who can provide permission to EXECUTE TASK?

A. THE TASK OWNER
B. SYSADMIN
C. ACCOUNTADMIN

正解：C

質問 # 34
A company is using a Snowflake account in Azure. The account has SAML SSO set up using ADFS as a SCIM identity provider. To validate Private Link connectivity, an Architect performed the following steps:
* Confirmed Private Link URLs are working by logging in with a username/password account
* Verified DNS resolution by running nslookups against Private Link URLs
* Validated connectivity using SnowCD
* Disabled public access using a network policy set to use the company's IP address range However, the following error message is received when using SSO to log into the company account:
IP XX.XXX.XX.XX is not allowed to access snowflake. Contact your local security administrator.
What steps should the Architect take to resolve this error and ensure that the account is accessed using only Private Link? (Choose two.)

A. Generate a new SCIM access token using system$generate_scim_access_token and save it to Azure AD.
B. Alter the Azure security integration to use the Private Link URLs.
C. Add the IP address in the error message to the allowed list in the network policy.
D. Update the configuration of the Azure AD SSO to use the Private Link URLs.
E. Open a case with Snowflake Support to authorize the Private Link URLs' access to the account.

正解：A、C

質問 # 35
......

完全版最新の問題集PDFで最新ARA-C01試験問題と解答：https://www.goshiken.com/Snowflake/ARA-C01-mondaishu.html

本日更新された最新のARA-C01のPDFはARA-C01無料お試し可能です：https://drive.google.com/open?id=1A6C8HjPPNLzhFX6R-i-8Kp4MpIpGa9wl

関するブログ

もっと

ARA-C01無料問題集