更新された2025年03月13日検証済み！ARA-C01問題集と解答で100%合格できる [Q57-Q77]

更新された2025年03月13日検証済み！ARA-C01問題集と解答で100%合格できる

2025年最新のの問題ARA-C01問題集を試そう！更新されたSnowflake試験合格させます

Snowflake ARA-C01（Snowpro Advanced Architect認定）認定試験は、スノーフレークアーキテクトの高度なスキルと知識を検証するように設計されたクラウドベースの認定試験です。この認定試験は、スノーフレークデータ倉庫とその建築を深く理解し、ベストプラクティスを使用して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できる専門家を対象としています。 Snowpro Advanced Architect認定試験は、ベンダーの中立認定であり、特定のベンダーまたはテクノロジーとは関係がないことを意味します。

 

質問 # 57
A Snowflake Architect created a new data share and would like to verify that only specific records in secure views are visible within the data share by the consumers.
What is the recommended way to validate data accessibility by the consumers?

• A. Alter the share settings as shown below, in order to impersonate a specific consumer account.
  alter share sales share set accounts = 'Consumerl' share restrictions = true
• B. Set the session parameter called SIMULATED_DATA_SHARING_C0NSUMER as shown below in order to impersonate the consumer accounts.
  alter session set simulated_data_sharing_consumer - 'Consumer Acctl*
• C. Create a row access policy as shown below and assign it to the data share.
  create or replace row access policy rap_acct as (acct_id varchar) returns boolean -> case when
  'acctl_role' = current_role() then true else false end;
• D. Create reader accounts as shown below and impersonate the consumers by logging in with their credentials.
  create managed account reader_acctl admin_name = userl , adroin_password 'Sdfed43da!44T , type = reader;

正解：B

解説：
The SIMULATED_DATA_SHARING_CONSUMER session parameter allows a data provider to simulate the data access of a consumer account without creating a reader account or logging in with the consumer credentials. This parameter can be used to validate the data accessibility by the consumers in a data share, especially when using secure views or secure UDFs that filter data based on the current account or role. By setting this parameter to the name of a consumer account, the data provider can see the same data as the consumer would see when querying the shared database. This is a convenient and efficient way to test the data sharing functionality and ensure that only the intended data is visible to the consumers.
References:
* Using the SIMULATED_DATA_SHARING_CONSUMER Session Parameter
* SnowPro Advanced: Architect Exam Study Guide

質問 # 58
VALIDATION_MODE does not support COPY statements that transform data during a load

• A. TRUE
• B. FALSE

正解：A

質問 # 59
A new user user_01 is created within Snowflake. The following two commands are executed:
Command 1-> show grants to user user_01;
Command 2 ~> show grants on user user 01;
What inferences can be made about these commands?

• A. Command 1 defines all the grants which are given to user_01
  Command 2 defines which role owns user 01
• B. Command 1 defines which user owns user_01
  Command 2 defines all the grants which have been given to user_01
• C. Command 1 defines all the grants which are given to user_01 Command 2 defines which user owns user_01
• D. Command 1 defines which role owns user_01
  Command 2 defines all the grants which have been given to user_01

正解：A

解説：
The SHOW GRANTS command in Snowflake can be used to list all the access control privileges that have been explicitly granted to roles, users, and shares. The syntax and the output of the command vary depending on the object type and the grantee type specified in the command1. In this question, the two commands have the following meanings:
Command 1: show grants to user user_01; This command lists all the roles granted to the user user_01. The output includes the role name, the grantee name, and the granted by role name for each grant. This command is equivalent to show grants to user current_user if user_01 is the current user1.
Command 2: show grants on user user_01; This command lists all the privileges that have been granted on the user object user_01. The output includes the privilege name, the grantee name, and the granted by role name for each grant. This command shows which role owns the user object user_01, as the owner role has the privilege to modify or drop the user object2.
Therefore, the correct inference is that command 1 defines all the grants which are given to user_01, and command 2 defines which role owns user_01.
Reference:
SHOW GRANTS
Understanding Access Control in Snowflake

質問 # 60
A DevOps team has a requirement for recovery of staging tables used in a complex set of data pipelines. The staging tables are all located in the same staging schem a. One of the requirements is to have online recovery of data on a rolling 7-day basis.
After setting up the DATA_RETENTION_TIME_IN_DAYS at the database level, certain tables remain unrecoverable past 1 day.
What would cause this to occur? (Choose two.)

• A. The DATA_RETENTION_TIME_IN_DAYS for the staging schema has been set to 1 day.
• B. The staging schema has not been setup for MANAGED ACCESS.
• C. The DevOps role should be granted ALLOW_RECOVERY privilege on the staging schema.
• D. The tables exceed the 1 TB limit for data recovery.
• E. The staging tables are of the TRANSIENT type.

正解：A、E

質問 # 61
What is a characteristic of loading data into Snowflake using the Snowflake Connector for Kafka?

• A. Loads using the Connector will have lower latency than Snowpipe and will ingest data in real time.
• B. The Connector works with all file formats, including text, JSON, Avro, Ore, Parquet, and XML.
• C. The Connector only works in Snowflake regions that use AWS infrastructure.
• D. The Connector creates and manages its own stage, file format, and pipe objects.

正解：D

解説：
According to the SnowPro Advanced: Architect documents and learning resources, a characteristic of loading data into Snowflake using the Snowflake Connector for Kafka is that the Connector creates and manages its own stage, file format, and pipe objects. The stage is an internal stage that is used to store the data files from the Kafka topics. The file format is a JSON or Avro file format that is used to parse the data files. The pipe is a Snowpipe object that is used to load the data files into the Snowflake table. The Connector automatically creates and configures these objects based on the Kafka configuration properties, and handles the cleanup and maintenance of these objects1.
The other options are incorrect because they are not characteristics of loading data into Snowflake using the Snowflake Connector for Kafka. Option A is incorrect because the Connector works in Snowflake regions that use any cloud infrastructure, not just AWS. The Connector supports AWS, Azure, and Google Cloud platforms, and can load data across different regions and cloud platforms using data replication2. Option B is incorrect because the Connector does not work with all file formats, only JSON and Avro. The Connector expects the data in the Kafka topics to be in JSON or Avro format, and parses the data accordingly. Other file formats, such as text, ORC, Parquet, or XML, are not supported by the Connector3. Option D is incorrect because loads using the Connector do not have lower latency than Snowpipe, and do not ingest data in real time. The Connector uses Snowpipe to load data into Snowflake, and inherits the same latency and performance characteristics of Snowpipe. The Connector does not provide real-time ingestion, but near real-time ingestion, depending on the frequency and size of the data files4. Reference: Installing and Configuring the Kafka Connector | Snowflake Documentation, Sharing Data Across Regions and Cloud Platforms | Snowflake Documentation, Overview of the Kafka Connector | Snowflake Documentation, Using Snowflake Connector for Kafka With Snowpipe Streaming | Snowflake Documentation

質問 # 62
Which organization-related tasks can be performed by the ORGADMIN role? (Choose three.)

• A. Deleting an account
• B. Changing the name of the organization
• C. Enabling the replication of a database
• D. Changing the name of an account
• E. Viewing a list of organization accounts
• F. Creating an account

正解：C、E、F

解説：
Explanation
According to the SnowPro Advanced: Architect documents and learning resources, the organization-related tasks that can be performed by the ORGADMIN role are:
* Creating an account in the organization. A user with the ORGADMIN role can use the CREATE ACCOUNT command to create a new account that belongs to the same organization as the current account1.
* Viewing a list of organization accounts. A user with the ORGADMIN role can use the SHOW ORGANIZATION ACCOUNTS command to view the names and properties of all accounts in the organization2. Alternatively, the user can use the Admin a Accounts page in the web interface to view the organization name and account names3.
* Enabling the replication of a database. A user with the ORGADMIN role can use the SYSTEM$GLOBAL_ACCOUNT_SET_PARAMETER function to enable database replication for an account in the organization. This allows the user to replicate databases across accounts in different regions and cloud platforms for data availability and durability4.
The other options are incorrect because they are not organization-related tasks that can be performed by the ORGADMIN role. Option A is incorrect because changing the name of the organization is not a task that can be performed by the ORGADMIN role. To change the name of an organization, the user must contact Snowflake Support3. Option D is incorrect because changing the name of an account is not a task that can be performed by the ORGADMIN role. To change the name of an account, the user must contact Snowflake Support5. Option E is incorrect because deleting an account is not a task that can be performed by the ORGADMIN role. To delete an account, the user must contact Snowflake Support. References: CREATE ACCOUNT | Snowflake Documentation, SHOW ORGANIZATION ACCOUNTS | Snowflake Documentation, Getting Started with Organizations | Snowflake Documentation, SYSTEM$GLOBAL_ACCOUNT_SET_PARAMETER | Snowflake Documentation, ALTER ACCOUNT | Snowflake Documentation, [DROP ACCOUNT | Snowflake Documentation]

質問 # 63
Files arrive in an external stage every 10 seconds from a proprietary system. The files range in size from 500 K to 3 MB. The data must be accessible by dashboards as soon as it arrives.
How can a Snowflake Architect meet this requirement with the LEAST amount of coding? (Choose two.)

• A. Use the COPY INTO command.
• B. Use a COPY command with a task.
• C. Use Snowpipe with auto-ingest.
• D. Use a materialized view on an external table.
• E. Use a combination of a task and a stream.

正解：C、D

解説：
Explanation
These two options are the best ways to meet the requirement of loading data from an external stage and making it accessible by dashboards with the least amount of coding.
* Snowpipe with auto-ingest is a feature that enables continuous and automated data loading from an external stage into a Snowflake table. Snowpipe uses event notifications from the cloud storage service to detect new or modified files in the stage and triggers a COPY INTO command to load the data into the table. Snowpipe is efficient, scalable, and serverless, meaning it does not require any infrastructure or maintenance from the user. Snowpipe also supports loading data from files of any size, as long as they are in a supported format1.
* A materialized view on an external table is a feature that enables creating a pre-computed result set from an external table and storing it in Snowflake. A materialized view can improve the performance and efficiency of querying data from an external table, especially for complex queries or dashboards. A materialized view can also support aggregations, joins, and filters on the external table data. A materialized view on an external table is automatically refreshed when the underlying data in the external stage changes, as long as the AUTO_REFRESH parameter is set to true2.
References:
* Snowpipe Overview | Snowflake Documentation
* Materialized Views on External Tables | Snowflake Documentation

質問 # 64
An Architect has been asked to clone schema STAGING as it looked one week ago, Tuesday June 1st at 8:00 AM, to recover some objects.
The STAGING schema has 50 days of retention.
The Architect runs the following statement:
CREATE SCHEMA STAGING_CLONE CLONE STAGING at (timestamp => '2021-06-01 08:00:00'); The Architect receives the following error: Time travel data is not available for schema STAGING. The requested time is either beyond the allowed time travel period or before the object creation time.
The Architect then checks the schema history and sees the following:
CREATED_ON|NAME|DROPPED_ON
2021-06-02 23:00:00 | STAGING | NULL
2021-05-01 10:00:00 | STAGING | 2021-06-02 23:00:00
How can cloning the STAGING schema be achieved?

• A. Modify the statement: CREATE SCHEMA STAGING_CLONE CLONE STAGING at (timestamp =>
  '2021-05-01 10:00:00');
• B. Rename the STAGING schema and perform an UNDROP to retrieve the previous STAGING schema version, then run the CLONE statement.
• C. Undrop the STAGING schema and then rerun the CLONE statement.
• D. Cloning cannot be accomplished because the STAGING schema version was not active during the proposed Time Travel time period.

正解：D

解説：
The error encountered during the cloning attempt arises because the schema STAGING as it existed on June
1st, 2021, is not within the Time Travel retention period. According to the schema history, STAGING was recreated on June 2nd, 2021, after being dropped on the same day. The requested timestamp of '2021-06-01
08:00:00' is prior to this recreation, hence not available. The STAGING schema from before June 2nd was dropped and exceeded the Time Travel period for retrieval by the time of the cloning attempt. Therefore, cloning STAGING as it looked on June 1st, 2021, cannot be achieved because the data from that time is no longer available within the allowed Time Travel window.References: Snowflake documentation on Time Travel and data cloning, which is covered under the SnowPro Advanced: Architect certification resources.

質問 # 65
Which Snowflake data modeling approach is designed for BI queries?

• A. Data Vault
• B. 3 NF
• C. Star schema
• D. Snowflake schema

正解：C

解説：
A star schema is a Snowflake data modeling approach that is designed for BI queries. A star schema is a type of dimensional modeling that organizes data into fact tables and dimension tables. A fact table contains the measures or metrics of the business process, such as sales amount, order quantity, or profit margin. A dimension table contains the attributes or descriptors of the business process, such as product name, customer name, or order date. A star schema is called so because it resembles a star, with one fact table in the center and multiple dimension tables radiating from it. A star schema can improve the performance and simplicity of BI queries by reducing the number of joins, providing fast access to aggregated data, and enabling intuitive query syntax. A star schema can also support various types of analysis, such as trend analysis, slice and dice, drill down, and roll up12.
Reference:
Snowflake Documentation: Dimensional Modeling
Snowflake Documentation: Star Schema

質問 # 66
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 References:
* 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
* : SnowPro Advanced: Architect | Study Guide
* : Partitioning External Tables
* : Creating External Tables
* : Supported File Formats and Compression for Staged Data Files
* : Overview of Stages

質問 # 67
How do Snowflake databases that are created from shares differ from standard databases that are not created from shares? (Choose three.)

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

正解：A、C、F

質問 # 68
Which statements describe characteristics of the use of materialized views in Snowflake? (Choose two.)

• A. They cannot include nested subqueries.
• B. They can include context functions, such as CURRENT_TIME().
• C. They can support inner joins, but not outer joins.
• D. They can include ORDER BY clauses.
• E. They can support MIN and MAX aggregates.

正解：A、E

解説：
Explanation
According to the Snowflake documentation, materialized views have some limitations on the query specification that defines them. One of these limitations is that they cannot include nested subqueries, such as subqueries in the FROM clause or scalar subqueries in the SELECT list. Another limitation is that they cannot include ORDER BY clauses, context functions (such as CURRENT_TIME()), or outer joins. However, materialized views can support MIN and MAX aggregates, as well as other aggregate functions, such as SUM, COUNT, and AVG.
References:
* Limitations on Creating Materialized Views | Snowflake Documentation
* Working with Materialized Views | Snowflake Documentation

質問 # 69
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. Update the configuration of the Azure AD SSO to use the Private Link URLs.
• B. Open a case with Snowflake Support to authorize the Private Link URLs' access to the account.
• C. Add the IP address in the error message to the allowed list in the network policy.
• D. Generate a new SCIM access token using system$generate_scim_access_token and save it to Azure AD.
• E. Alter the Azure security integration to use the Private Link URLs.

正解：A、C

解説：
Explanation
The error message indicates that the IP address in the error message is not allowed to access Snowflake because it is not in the allowed list of the network policy. The network policy is a feature that allows restricting access to Snowflake based on IP addresses or ranges. To resolve this error, the Architect should take the following steps:
* Add the IP address in the error message to the allowed list in the network policy. This will allow the IP address to access Snowflake using the Private Link URLs. Alternatively, the Architect can disable the network policy if it is not required for security reasons.
* Update the configuration of the Azure AD SSO to use the Private Link URLs. This will ensure that the SSO authentication process uses the Private Link URLs instead of the public URLs. The configuration can be updated by following the steps in the Azure documentation1.
These two steps should resolve the error and ensure that the account is accessed using only Private Link. The other options are not necessary or relevant for this scenario. Altering the Azure security integration to use the Private Link URLs is not required because the security integration is used for SCIM provisioning, not for SSO authentication. Generating a new SCIM access token using system$generate_scim_access_token and saving it to Azure AD is not required because the SCIM access token is used for SCIM provisioning, not for SSO authentication. Opening a case with Snowflake Support to authorize the Private Link URLs' access to the account is not required because the authorization can be done by the account administrator using the SYSTEM$AUTHORIZE_PRIVATELINK function2.

質問 # 70
The diagram shows the process flow for Snowpipe auto-ingest with Amazon Simple Notification Service (SNS) with the following steps:
Step 1: Data files are loaded in a stage.
Step 2: An Amazon S3 event notification, published by SNS, informs Snowpipe - by way of Amazon Simple Queue Service (SQS) - that files are ready to load. Snowpipe copies the files into a queue.
Step 3: A Snowflake-provided virtual warehouse loads data from the queued files into the target table based on parameters defined in the specified pipe.

If an AWS Administrator accidentally deletes the SQS subscription to the SNS topic in Step 2, what will happen to the pipe that references the topic to receive event messages from Amazon S3?

• A. The pipe will continue to receive the messages as Snowflake will automatically restore the subscription by creating a new SNS topic. Snowflake will then recreate the pipe by specifying the new SNS topic name in the pipe definition.
• B. The pipe will no longer be able to receive the messages. To restore the system immediately, the user needs to manually create a new SNS topic with a different name and then recreate the pipe by specifying the new SNS topic name in the pipe definition.
• C. The pipe will no longer be able to receive the messages and the user must wait for 24 hours from the time when the SNS topic subscription was deleted. Pipe recreation is not required as the pipe will reuse the same subscription to the existing SNS topic after 24 hours.
• D. The pipe will continue to receive the messages as Snowflake will automatically restore the subscription to the same SNS topic and will recreate the pipe by specifying the same SNS topic name in the pipe definition.

正解：B

質問 # 71
A user is executing the following command sequentially within a timeframe of 10 minutes from start to finish:

What would be the output of this query?

• A. Syntax error line 1 at position 58 unexpected 'at'.
• B. The offset -> is not a valid clause in the clone operation.
• C. Table T_SALES_CLONE successfully created.
• D. Time Travel data is not available for table T_SALES.

正解：C

解説：
The query is executing a clone operation on an existing table t_sales with an offset to account for the retention time. The syntax used is correct for cloning a table in Snowflake, and the use of the at(offset => -60*30) clause is valid. This specifies that the clone should be based on the state of the table 30 minutes prior (60 seconds * 30). Assuming the table t_sales exists and has been modified within the last 30 minutes, and considering the data_retention_time_in_days is set to 1 day (which enables time travel queries for the past 24 hours), the table t_sales_clone would be successfully created based on the state of t_sales 30 minutes before the clone command was issued.

質問 # 72
A user can change object parameters using which of the following roles?

• A. SECURITYADMIN, USER with PRIVILEGE
• B. ACCOUNTADMIN, USER with PRIVILEGE
• C. SYSADMIN, SECURITYADMIN
• D. ACCOUNTADMIN, SECURITYADMIN

正解：B

解説：
Explanation
According to the Snowflake documentation, object parameters are parameters that can be set on individual objects such as databases, schemas, tables, and stages. Object parameters can be set by users with the appropriate privileges on the objects. For example, to set the object parameter AUTO_REFRESH on a table, the user must have the MODIFY privilege on the table. The ACCOUNTADMIN role has the highest level of privileges on all objects in the account, so it can set any object parameter on any object. However, other roles, such as SECURITYADMIN or SYSADMIN, do not have the same level of privileges on all objects, so they cannot set object parameters on objects they do not own or have the required privileges on. Therefore, the correct answer is C. ACCOUNTADMIN, USER with PRIVILEGE.
References:
* Parameters | Snowflake Documentation
* Object Parameters | Snowflake Documentation
* Object Privileges | Snowflake Documentation

質問 # 73
Privileges granted on database objects are not replicated to a secondary database.

• A. TRUE
• B. FALSE

正解：A

質問 # 74
When loading data from stage using COPY INTO, what options can you specify for the ON_ERROR clause?

• A. FAIL
• B. SKIP_FILE
• C. ABORT_STATEMENT
• D. CONTINUE

正解：B、C、D

解説：
Explanation
* The ON_ERROR clause is an optional parameter for the COPY INTO command that specifies the behavior of the command when it encounters errors in the files. The ON_ERROR clause can have one of the following values1:
* CONTINUE: This value instructs the command to continue loading the file and return an error message for a maximum of one error encountered per data file. The difference between the ROWS_PARSED and ROWS_LOADED column values represents the number of rows that include detected errors. To view all errors in the data files, use the VALIDATION_MODE parameter or query the VALIDATE function1.
* SKIP_FILE: This value instructs the command to skip the file when it encounters a data error on any of the records in the file. The command moves on to the next file in the stage and continues loading. The skipped file is not loaded and no error message is returned for the file1.
* ABORT_STATEMENT: This value instructs the command to stop loading data when the first error is encountered. The command returns an error message for the file and aborts the load operation. This is the default value for the ON_ERROR clause1.
* Therefore, options A, B, and C are correct.
References: : COPY INTO <table>

質問 # 75
What will the below query return
SELECT TOP 10 GRADES FROM STUDENT;

• A. Non-deterministic list of 10 grades
• B. The 10 lowest grades
• C. The top 10 highest grades

正解：A

質問 # 76
A company has several sites in different regions from which the company wants to ingest data.
Which of the following will enable this type of data ingestion?

• A. The company must have a Snowflake account in each cloud region to be able to ingest data to that account.
• B. The company must replicate data between Snowflake accounts.
• C. The company should use a storage integration for the external stage.
• D. The company should provision a reader account to each site and ingest the data through the reader accounts.

正解：C

解説：
This is the correct answer because it allows the company to ingest data from different regions using a storage integration for the external stage. A storage integration is a feature that enables secure and easy access to files in external cloud storage from Snowflake. A storage integration can be used to create an external stage, which is a named location that references the files in the external storage. An external stage can be used to load data into Snowflake tables using the COPY INTO command, or to unload data from Snowflake tables using the COPY INTO LOCATION command. A storage integration can support multiple regions and cloud platforms, as long as the external storage service is compatible with Snowflake12.
References:
* Snowflake Documentation: Storage Integrations
* Snowflake Documentation: External Stages

質問 # 77
......

Snowflake ARA-C01（SnowPro Advanced Architect Certification）試験は、複雑なSnowflakeソリューションの設計と実装に必要なスキルと知識を検証する認定試験です。これは、Snowflakeで長年の経験があるアーキテクトが専門レベルの認定を取得し、その分野での専門知識を証明するために設計されています。試験は、複雑なビジネス要件を満たすSnowflakeソリューションを設計、アーキテクト、実装する能力を試験します。

 

最新のARA-C01試験問題集でSnowflakeトレーニング試験には：https://www.jpntest.com/shiken/ARA-C01-mondaishu

合格できるSnowflake ARA-C01のPDF問題集で最近更新された162問あります：https://drive.google.com/open?id=1LJkMOHGSXn7mpRNTDphaXT_SGKdbqtU0