PDF問題（2025年最新）実際のSnowflake ARA-C01試験問題
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質問 # 88
By default, the maximum file size that can be unloaded to a single file in snowflake is
- A. 10 MB
- B. 5 GB for Azure, AWS and GCP
- C. 16 MB
正解：C
質問 # 89
How can the Snowflake context functions be used to help determine whether a user is authorized to see data that has column-level security enforced? (Select TWO).
- A. Assign the accountadmin role to the user who is executing the object.
- B. Set masking policy conditions using current_role targeting the role in use for the current session.
- C. Determine if there are ownership privileges on the masking policy that would allow the use of any function.
- D. Set masking policy conditions using is_role_in_session targeting the role in use for the current account.
- E. Set masking policy conditions using invoker_role targeting the executing role in a SQL statement.
正解：B、E
解説：
Snowflake context functions are functions that return information about the current session, user, role, warehouse, database, schema, or object. They can be used to help determine whether a user is authorized to see data that has column-level security enforced by setting masking policy conditions based on the context functions. The following context functions are relevant for column-level security:
* current_role: This function returns the name of the role in use for the current session. It can be used to set masking policy conditions that target the current session and are not affected by the execution context of the SQL statement. For example, a masking policy condition using current_role can allow or deny access to a column based on the role that the user activated in the session.
* invoker_role: This function returns the name of the executing role in a SQL statement. It can be used to set masking policy conditions that target the executing role and are affected by the execution context of the SQL statement. For example, a masking policy condition using invoker_role can allow or deny access to a column based on the role that the user specified in the SQL statement, such as using the AS ROLE clause or a stored procedure.
* is_role_in_session: This function returns TRUE if the user's current role in the session (i.e. the role returned by current_role) inherits the privileges of the specified role. It can be used to set masking policy conditions that involve role hierarchy and privilege inheritance. For example, a masking policy condition using is_role_in_session can allow or deny access to a column based on whether the user's current role is a lower privilege role in the specified role hierarchy.
The other options are not valid ways to use the Snowflake context functions for column-level security:
* Set masking policy conditions using is_role_in_session targeting the role in use for the current account.
This option is incorrect because is_role_in_session does not target the role in use for the current account,
* but rather the role in use for the current session. Also, the current account is not a role, but rather a logical entity that contains users, roles, warehouses, databases, and other objects.
* Determine if there are ownership privileges on the masking policy that would allow the use of any function. This option is incorrect because ownership privileges on the masking policy do not affect the use of any function, but rather the ability to create, alter, or drop the masking policy. Also, this is not a way to use the Snowflake context functions, but rather a way to check the privileges on the masking policy object.
* Assign the accountadmin role to the user who is executing the object. This option is incorrect because assigning the accountadmin role to the user who is executing the object does not involve using the Snowflake context functions, but rather granting the highest-level role to the user. Also, this is not a recommended practice for column-level security, as it would give the user full access to all objects and data in the account, which could compromise data security and governance.
References:
* Context Functions
* Advanced Column-level Security topics
* Snowflake Data Governance: Column Level Security Overview
* Data Security Snowflake Part 2 - Column Level Security
質問 # 90
You have written a procedure and a function in snowflake.
How do you find your procedures and functions in snowflake?
- A. SHOW FUNCTIONS;
- B. SHOW ALL PROCEDURES AND FUNCTIONS
- C. SHOW PROCEDURES;
- D. SHOW PROCEDURES, FUNCTIONS
正解：A
質問 # 91
External functions must be scalar functions
- A. TRUE
- B. FALSE
正解：A
質問 # 92
Out of the three query optimization techniques(search optimization, clustering and materialized view) which one does not have storage cost
- A. Search Optimization Service
- B. Materialized View
- C. Clustering the Table
正解：C
質問 # 93
What step will im the performance of queries executed against an external table?
- A. Shorten the names of the source files.
- B. Use an internal stage instead of an external stage to store the source files.
- C. Partition the external table.
- D. Convert the source files' character encoding to UTF-8.
正解：C
解説：
Partitioning an external table is a technique that improves the performance of queries executed against the table by reducing the amount of data scanned. Partitioning an external table involves creating one or more partition columns that define how the table is logically divided into subsets of data based on the values in those columns. The partition columns can be derived from the file metadata (such as file name, path, size, or modification time) or from the file content (such as a column value or a JSON attribute). Partitioning an external table allows the query optimizer to prune the files that do not match the query predicates, thus avoiding unnecessary data scanning and processing2 The other options are not effective steps for improving the performance of queries executed against an external table:
Shorten the names of the source files. This option does not have any impact on the query performance, as the file names are not used for query processing. The file names are only used for creating the external table and displaying the query results3 Convert the source files' character encoding to UTF-8. This option does not affect the query performance, as Snowflake supports various character encodings for external table files, such as UTF-8, UTF-16, UTF-32, ISO-8859-1, and Windows-1252. Snowflake automatically detects the character encoding of the files and converts them to UTF-8 internally for query processing4 Use an internal stage instead of an external stage to store the source files. This option is not applicable, as external tables can only reference files stored in external stages, such as Amazon S3, Google Cloud Storage, or Azure Blob Storage. Internal stages are used for loading data into internal tables, not external tables5 Reference:
1: SnowPro Advanced: Architect | Study Guide
2: Snowflake Documentation | Partitioning External Tables
3: Snowflake Documentation | Creating External Tables
4: Snowflake Documentation | Supported File Formats and Compression for Staged Data Files
5: Snowflake Documentation | Overview of Stages
6: SnowPro Advanced: Architect | Study Guide
7: Partitioning External Tables
8: Creating External Tables
9: Supported File Formats and Compression for Staged Data Files
10: Overview of Stages
質問 # 94
Cloud services can help in pruning even if the columns are variant columns.
- A. TRUE
- B. FALSE
正解：A
質問 # 95
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;
create view v2 as select * from dbl.schemal. tablel; - B. use database db1;
use schema schemal;
select * from db2.schema2.table2; - C. use database db2;
use schema schema2;
select * from db1.schemal.tablel union select * from table2; - D. use database db1;
use schema schemal;
create view v1 as select * from db2.schema2.table2;
正解：A
質問 # 96
Why might a Snowflake Architect use a star schema model rather than a 3NF model when designing a data architecture to run in Snowflake? (Select TWO).
- A. The Architect is designing a landing zone to receive raw data into Snowflake.
- B. The Bl tool needs a data model that allows users to summarize facts across different dimensions, or to drill down from the summaries.
- C. Snowflake cannot handle the joins implied in a 3NF data model.
- D. The Architect wants to remove data duplication from the data stored in Snowflake.
- E. The Architect wants to present a simple flattened single view of the data to a particular group of end users.
正解：B、E
解説：
A star schema model is a type of dimensional data model that consists of a single fact table and multiple dimension tables. A 3NF model is a type of relational data model that follows the third normal form, which eliminates data redundancy and ensures referential integrity. A Snowflake Architect might use a star schema model rather than a 3NF model when designing a data architecture to run in Snowflake for the following reasons:
A star schema model is more suitable for analytical queries that require aggregating and slicing data across different dimensions, such as those performed by a BI tool. A 3NF model is more suitable for transactional queries that require inserting, updating, and deleting individual records.
A star schema model is simpler and faster to query than a 3NF model, as it involves fewer joins and less complex SQL statements. A 3NF model is more complex and slower to query, as it involves more joins and more complex SQL statements.
A star schema model can provide a simple flattened single view of the data to a particular group of end users, such as business analysts or data scientists, who need to explore and visualize the data. A 3NF model can provide a more detailed and normalized view of the data to a different group of end users, such as application developers or data engineers, who need to maintain and update the data.
The other options are not valid reasons for choosing a star schema model over a 3NF model in Snowflake:
Snowflake can handle the joins implied in a 3NF data model, as it supports ANSI SQL and has a powerful query engine that can optimize and execute complex queries efficiently.
The Architect can use both star schema and 3NF models to remove data duplication from the data stored in Snowflake, as both models can enforce data integrity and avoid data anomalies. However, the trade-off is that a star schema model may have more data redundancy than a 3NF model, as it denormalizes the data for faster query performance, while a 3NF model may have less data redundancy than a star schema model, as it normalizes the data for easier data maintenance.
The Architect can use both star schema and 3NF models to design a landing zone to receive raw data into Snowflake, as both models can accommodate different types of data sources and formats. However, the choice of the model may depend on the purpose and scope of the landing zone, such as whether it is a temporary or permanent storage, whether it is a staging area or a data lake, and whether it is a single source or a multi-source integration.
Reference:
Snowflake Architect Training
Data Modeling: Understanding the Star and Snowflake Schemas
Data Vault vs Star Schema vs Third Normal Form: Which Data Model to Use?
Star Schema vs Snowflake Schema: 5 Key Differences
Dimensional Data Modeling - Snowflake schema
Star schema vs Snowflake Schema
質問 # 97
Which feature provides the capability to define an alternate cluster key for a table with an existing cluster key?
- A. Result cache
- B. External table
- C. Search optimization
- D. Materialized view
正解：D
解説：
Explanation
A materialized view is a feature that provides the capability to define an alternate cluster key for a table with an existing cluster key. A materialized view is a pre-computed result set that is stored in Snowflake and can be queried like a regular table. A materialized view can have a different cluster key than the base table, which can improve the performance and efficiency of queries on the materialized view. A materialized view can also support aggregations, joins, and filters on the base table data. A materialized view is automatically refreshed when the underlying data in the base table changes, as long as the AUTO_REFRESH parameter is set to true1.
References:
* Materialized Views | Snowflake Documentation
質問 # 98
An Architect is designing a file ingestion recovery solution. The project will use an internal named stage for file storage. Currently, in the case of an ingestion failure, the Operations team must manually download the failed file and check for errors.
Which downloading method should the Architect recommend that requires the LEAST amount of operational overhead?
- A. Use the get command in Snowsight to retrieve the file.
- B. Use the get command in SnowSQL to retrieve the file.
- C. Use the Snowflake Connector for Python, connect to remote storage and download the file.
- D. Use the Snowflake API endpoint and download the file.
正解：D
質問 # 99
An Architect is designing a file ingestion recovery solution. The project will use an internal named stage for file storage. Currently, in the case of an ingestion failure, the Operations team must manually download the failed file and check for errors.
Which downloading method should the Architect recommend that requires the LEAST amount of operational overhead?
- A. Use the get command in Snowsight to retrieve the file.
- B. Use the Snowflake API endpoint and download the file.
- C. Use the Snowflake Connector for Python, connect to remote storage and download the file.
- D. Use the get command in SnowSQL to retrieve the file.
正解：D
解説：
The get command in SnowSQL is a convenient way to download files from an internal stage to a local directory. The get command can be used in interactive mode or in a script, and it supports wildcards and parallel downloads. The get command also allows specifying the overwrite option, which determines how to handle existing files with the same name2 The Snowflake Connector for Python, the Snowflake API endpoint, and the get command in Snowsight are not recommended methods for downloading files from an internal stage, because they require more operational overhead than the get command in SnowSQL. The Snowflake Connector for Python and the Snowflake API endpoint require writing and maintaining code to handle the connection, authentication, and file transfer. The get command in Snowsight requires using the web interface and manually selecting the files to download34 Reference:
1: SnowPro Advanced: Architect | Study Guide
2: Snowflake Documentation | Using the GET Command
3: Snowflake Documentation | Using the Snowflake Connector for Python
4: Snowflake Documentation | Using the Snowflake API
5: Snowflake Documentation | Using the GET Command in Snowsight
6: SnowPro Advanced: Architect | Study Guide
7: Using the GET Command
8: Using the Snowflake Connector for Python
9: Using the Snowflake API
10: [Using the GET Command in Snowsight]
質問 # 100
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. Developers create their own datasets to work against transformed versions of the live data.
- B. Production and development run in different databases in the same account, and Developers need to see production-like data but with specific columns masked.
- C. 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.
- D. 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.
- E. 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.
正解：C、E
質問 # 101
A retail company has over 3000 stores all using the same Point of Sale (POS) system. The company wants to deliver near real-time sales results to category managers. The stores operate in a variety of time zones and exhibit a dynamic range of transactions each minute, with some stores having higher sales volumes than others.
Sales results are provided in a uniform fashion using data engineered fields that will be calculated in a complex data pipeline. Calculations include exceptions, aggregations, and scoring using external functions interfaced to scoring algorithms. The source data for aggregations has over 100M rows.
Every minute, the POS sends all sales transactions files to a cloud storage location with a naming convention that includes store numbers and timestamps to identify the set of transactions contained in the files. The files are typically less than 10MB in size.
How can the near real-time results be provided to the category managers? (Select TWO).
- A. A stream should be created to accumulate the near real-time data and a task should be created that runs at a frequency that matches the real-time analytics needs.
- B. An external scheduler should examine the contents of the cloud storage location and issue SnowSQL commands to process the data at a frequency that matches the real-time analytics needs.
- C. The copy into command with a task scheduled to run every second should be used to achieve the near-real time requirement.
- D. A Snowpipe should be created and configured with AUTO_INGEST = true. A stream should be created to process INSERTS into a single target table using the stream metadata to inform the store number and timestamps.
- E. All files should be concatenated before ingestion into Snowflake to avoid micro-ingestion.
正解：C、D
質問 # 102
What integration object should be used to place restrictions on where data may be exported?
- A. API integration
- B. Storage integration
- C. Stage integration
- D. Security integration
正解：D
解説：
According to the SnowPro Advanced: Architect documents and learning resources, the integration object that should be used to place restrictions on where data may be exported is the security integration. A security integration is a Snowflake object that provides an interface between Snowflake and third-party security services, such as Okta, Duo, or Google Authenticator. A security integration can be used to enforce policies on data export, such as requiring multi-factor authentication (MFA) or restricting the export destination to a specific network or domain. A security integration can also be used to enable single sign-on (SSO) or federated authentication for Snowflake users1.
The other options are incorrect because they are not integration objects that can be used to place restrictions on where data may be exported. Option A is incorrect because a stage integration is not a valid type of integration object in Snowflake. A stage is a Snowflake object that references a location where data files are stored, such as an internal stage, an external stage, or a named stage. A stage is not an integration object that provides an interface between Snowflake and third-party services2. Option C is incorrect because a storage integration is a Snowflake object that provides an interface between Snowflake and external cloud storage, such as Amazon S3, Azure Blob Storage, or Google Cloud Storage. A storage integration can be used to securely access data files from external cloud storage without exposing the credentials, but it cannot be used to place restrictions on where data may be exported3. Option D is incorrect because an API integration is a Snowflake object that provides an interface between Snowflake and third-party services that use REST APIs, such as Salesforce, Slack, or Twilio. An API integration can be used to securely call external REST APIs from Snowflake using the CALL_EXTERNAL_API function, but it cannot be used to place restrictions on where data may be exported4. References: CREATE SECURITY INTEGRATION | Snowflake Documentation, CREATE STAGE | Snowflake Documentation, CREATE STORAGE INTEGRATION | Snowflake Documentation, CREATE API INTEGRATION | Snowflake Documentation
質問 # 103
A Snowflake Architect is setting up database replication to support a disaster recovery plan. The primary database has external tables.
How should the database be replicated?
- A. Move the external tables to a database that is not replicated, then replicate the primary database.
- B. Share the primary database with an account in the same region that the database will be replicated to.
- C. Replicate the database ensuring the replicated database is in the same region as the external tables.
- D. Create a clone of the primary database then replicate the database.
正解：A
解説：
Database replication is a feature that allows you to create a copy of a database in another account, region, or cloud platform for disaster recovery or business continuity purposes. However, not all database objects can be replicated. External tables are one of the exceptions, as they reference data files stored in an external stage that is not part of Snowflake. Therefore, to replicate a database that contains external tables, you need to move the external tables to a separate database that is not replicated, and then replicate the primary database that contains the other objects. This way, you can avoid replication errors and ensure consistency between the primary and secondary databases. The other options are incorrect because they either do not address the issue of external tables, or they use an alternative method that is not supported by Snowflake. You cannot create a clone of the primary database and then replicate it, as replication only works on the original database, not on its clones. You also cannot share the primary database with another account, as sharing is a different feature that does not create a copy of the database, but rather grants access to the shared objects. Finally, you do not need to ensure that the replicated database is in the same region as the external tables, as external tables can access data files stored in any region or cloud platform, as long as the stage URL is valid and accessible. References:
* [Replication and Failover/Failback] 1
* [Introduction to External Tables] 2
* [Working with External Tables] 3
* [Replication : How to migrate an account from One Cloud Platform or Region to another in
* Snowflake] 4
質問 # 104
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