無料2025年最新のARA-C01問題集で100%合格保証には最新の サンプル [Q55-Q75]

無料2025年最新のARA-C01問題集で100%合格保証には最新の サンプル

準備ARA-C01問題解答無料更新には100%試験合格保証 [2025]

Snowflake ARA-C01試験の資格を得るには、まずSnowPro Advanced Architect認定を取得する必要があります。この認定には、SnowPro Core試験に合格し、データモデリングおよびパフォーマンスチューニングの能力を示す必要があります。候補者がこの認定を取得した後、ARA-C01試験を受けて、さらに専門知識を検証することができます。

 

質問 # 55
To convert JSON null value to SQL null value, you will use

• A. NULL_IF
• B. IS_NULL_VALUE
• C. STRIP_NULL_VALUE

正解：C

質問 # 56
How can the Snowpipe REST API be used to keep a log of data load history?

• A. Call insertReport every 20 minutes, fetching the last 10,000 entries.
• B. Call loadHistoryScan every 10 minutes for a 15-minute time range.
• C. Call loadHistoryScan every minute for the maximum time range.
• D. Call insertReport every 8 minutes for a 10-minute time range.

正解：B

解説：
* Snowpipe is a service that automates and optimizes the loading of data from external stages into Snowflake tables. Snowpipe uses a queue to ingest files as they become available in the stage. Snowpipe also provides REST endpoints to load data and retrieve load history reports1.
* The loadHistoryScan endpoint returns the history of files that have been ingested by Snowpipe within a specified time range. The endpoint accepts the following parameters2:
* pipe: The fully-qualified name of the pipe to query.
* startTimeInclusive: The start of the time range to query, in ISO 8601 format. The value must be within the past 14 days.
* endTimeExclusive: The end of the time range to query, in ISO 8601 format. The value must be later than the start time and within the past 14 days.
* recentFirst: A boolean flag that indicates whether to return the most recent files first or last. The default value is false, which means the oldest files are returned first.
* showSkippedFiles: A boolean flag that indicates whether to include files that were skipped by Snowpipe in the response. The default value is false, which means only files that were loaded are returned.
* The loadHistoryScan endpoint can be used to keep a log of data load history by calling it periodically with a suitable time range. The best option among the choices is D, which is to call loadHistoryScan every 10 minutes for a 15-minute time range. This option ensures that the endpoint is called frequently enough to capture the latest files that have been ingested, and that the time range is wide enough to avoid missing any files that may have been delayed or retried by Snowpipe. The other options are either too infrequent, too narrow, or use the wrong endpoint3.
References:
* 1: Introduction to Snowpipe | Snowflake Documentation
* 2: loadHistoryScan | Snowflake Documentation
* 3: Monitoring Snowpipe Load History | Snowflake Documentation

質問 # 57
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. A stream
• B. A copy command at regular intervals
• C. A pipe
• D. An external table

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.
References: : Pipes : Loading Data Using Snowpipe : External Tables : Streams : COPY INTO <table>

質問 # 58
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 return an warning message
• B. Task will be executed but no rows will be merged
• C. Task will be skipped

正解：C

質問 # 59
Who can provide permission to EXECUTE TASK?

• A. SYSADMIN
• B. THE TASK OWNER
• C. ACCOUNTADMIN

正解：C

質問 # 60
What built-in Snowflake features make use of the change tracking metadata for a table? (Choose two.)

• A. The MERGE command
• B. The UPSERT command
• C. TheCHANGE_DATA_CAPTURE command
• D. The CHANGES clause
• E. A STREAM object

正解：D、E

解説：
Explanation
The built-in Snowflake features that make use of the change tracking metadata for a table are the CHANGES clause and a STREAM object. The CHANGES clause enables querying the change tracking metadata for a table or view within a specified interval of time without having to create a stream with an explicit transactional offset1. A STREAM object records data manipulation language (DML) changes made to tables, including inserts, updates, and deletes, as well as metadata about each change, so that actions can be taken using the changed data. This process is referred to as change data capture (CDC)2. The other options are incorrect because they do not make use of the change tracking metadata for a table. The MERGE command performs insert, update, or delete operations on a target table based on the results of a join with a source table3. The UPSERT command is not a valid Snowflake command. The CHANGE_DATA_CAPTURE command is not a valid Snowflake command. References: CHANGES | Snowflake Documentation, Change Tracking Using Table Streams | Snowflake Documentation, MERGE | Snowflake Documentation

質問 # 61
What step will im the performance of queries executed against an external table?

• A. Convert the source files' character encoding to UTF-8.
• B. Partition the external table.
• C. Shorten the names of the source files.
• D. Use an internal stage instead of an external stage to store the source files.

正解：B

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 # 62
An Architect needs to improve the performance of reports that pull data from multiple Snowflake tables, join, and then aggregate the data. Users access the reports using several dashboards. There are performance issues on Monday mornings between 9:00am-11:00am when many users check the sales reports.
The size of the group has increased from 4 to 8 users. Waiting times to refresh the dashboards has increased significantly. Currently this workload is being served by a virtual warehouse with the following parameters:
AUTO-RESUME = TRUE AUTO_SUSPEND = 60 SIZE = Medium
What is the MOST cost-effective way to increase the availability of the reports?

• A. Use materialized views and pre-calculate the data.
• B. Use a multi-cluster warehouse in maximized mode with 2 size Medium clusters.
• C. Increase the warehouse to size Large and set auto_suspend = 600.
• D. Use a multi-cluster warehouse in auto-scale mode with 1 size Medium cluster, and set min_cluster_count = 1 and max_cluster_count = 4.

正解：D

解説：
The most cost-effective way to increase the availability and performance of the reports during peak usage times, while keeping costs under control, is to use a multi-cluster warehouse in auto-scale mode. Option D suggests using a multi-cluster warehouse with 1 size Medium cluster and allowing it to auto-scale between 1 and 4 clusters based on demand. This setup ensures that additional computing resources are available when needed (e.g., during Monday morning peaks) and are scaled down to minimize costs when the demand decreases. This approach optimizes resource utilization and cost by adjusting the compute capacity dynamically, rather than maintaining a larger fixed size or multiple clusters continuously.References:
Snowflake's official documentation on managing warehouses and using auto-scaling features.

質問 # 63
Company A would like to share data in Snowflake with Company B.
Company B is not on the same cloud platform as Company A.
What is required to allow data sharing between these two companies?

• A. Setup data replication to the region and cloud platform where the consumer resides.
• B. Create a pipeline to write shared data to a cloud storage location in the target cloud provider.
• C. Ensure that all views are persisted, as views cannot be shared across cloud platforms.
• D. Company A and Company B must agree to use a single cloud platform: Data sharing is only possible if the companies share the same cloud provider.

正解：A

質問 # 64
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. C2, C1, C3
• C. C3, C4, C5
• D. C1, C3, C2

正解：D

解説：
Explanation
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.
References: 1: Considerations for Choosing Clustering for a Table | Snowflake Documentation

質問 # 65
Company A has recently acquired company B.
The Snowflake deployment for company B is located in the Azure West Europe region.
As part of the integration process, an Architect has been asked to consolidate company B's sales data into company A's Snowflake account which is located in the AWS us-east-1 region.
How can this requirement be met?

• A. Replicate the sales data from company B's Snowflake account into company A's Snowflake account using cross-region data replication within Snowflake. Configure a direct share from company B's account to company A's account.
• B. Build a custom data pipeline using Azure Data Factory or a similar tool to extract the sales data from company B's Snowflake account. Transform the data, then load it into company A's Snowflake account.
• C. Migrate company B's Snowflake deployment to the same region as company A's Snowflake deployment, ensuring data locality. Then perform a direct database-to-database merge of the sales data.
• D. Export the sales data from company B's Snowflake account as CSV files, and transfer the files to company A's Snowflake account. Import the data using Snowflake's data loading capabilities.

正解：A

解説：
The best way to meet the requirement of consolidating company B's sales data into company A's Snowflake account is to use cross-region data replication within Snowflake. This feature allows data providers to securely share data with data consumers across different regions and cloud platforms. By replicating the sales data from company B's account in Azure West Europe region to company A's account in AWS us-east-1 region, the data will be synchronized and available for consumption. To enable data replication, the accounts must be linked and replication must be enabled by a user with the ORGADMIN role. Then, a replication group must be created and the sales database must be added to the group. Finally, a direct share must be configured from company B's account to company A's account to grant access to the replicated data. This option is more efficient and secure than exporting and importing data using CSV files or migrating the entire Snowflake deployment to another region or cloud platform. It also does not require building a custom data pipeline using external tools.
Reference:
Sharing data securely across regions and cloud platforms
Introduction to replication and failover
Replication considerations
Replicating account objects

質問 # 66
Company A has recently acquired company B. The Snowflake deployment for company B is located in the Azure West Europe region.
As part of the integration process, an Architect has been asked to consolidate company B's sales data into company A's Snowflake account which is located in the AWS us-east-1 region.
How can this requirement be met?

• A. Replicate the sales data from company B's Snowflake account into company A's Snowflake account using cross-region data replication within Snowflake. Configure a direct share from company B's account to company A's account.
• B. Build a custom data pipeline using Azure Data Factory or a similar tool to extract the sales data from company B's Snowflake account. Transform the data, then load it into company A's Snowflake account.
• C. Migrate company B's Snowflake deployment to the same region as company A's Snowflake deployment, ensuring data locality. Then perform a direct database-to-database merge of the sales data.
• D. Export the sales data from company B's Snowflake account as CSV files, and transfer the files to company A's Snowflake account. Import the data using Snowflake's data loading capabilities.

正解：A

解説：
The best way to meet the requirement of consolidating company B's sales data into company A's Snowflake account is to use cross-region data replication within Snowflake. This feature allows data providers to securely share data with data consumers across different regions and cloud platforms. By replicating the sales data from company B's account in Azure West Europe region to company A's account in AWS us-east-1 region, the data will be synchronized and available for consumption. To enable data replication, the accounts must be linked and replication must be enabled by a user with the ORGADMIN role. Then, a replication group must be created and the sales database must be added to the group. Finally, a direct share must be configured from company B's account to company A's account to grant access to the replicated data. This option is more efficient and secure than exporting and importing data using CSV files or migrating the entire Snowflake deployment to another region or cloud platform. It also does not require building a custom data pipeline using external tools.
Reference:
Sharing data securely across regions and cloud platforms
Introduction to replication and failover
Replication considerations
Replicating account objects

質問 # 67
Dynamic data masking is supported in which editions of snowflake

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

正解：A、C、D

質問 # 68
Which command below will load data from result_scan to a table?

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

正解：A

質問 # 69
An Architect is designing a pipeline to stream event data into Snowflake using the Snowflake Kafka connector. The Architect's highest priority is to configure the connector to stream data in the MOST cost-effective manner.
Which of the following is recommended for optimizing the cost associated with the Snowflake Kafka connector?

• A. Utilize a higher Buffer.flush.time in the connector configuration.
• B. Utilize a lower Buffer.size.bytes in the connector configuration.
• C. Utilize a higher Buffer.size.bytes in the connector configuration.
• D. Utilize a lower Buffer.count.records in the connector configuration.

正解：D

質問 # 70
What integration object should be used to place restrictions on where data may be exported?

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

正解：C

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 # 71
Consider the following COPY command which is loading data with CSV format into a Snowflake table from an internal stage through a data transformation query.

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

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

正解：B

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

質問 # 72
All multi cluster warehouses that were using the Legacy policy now use the default Standard policy

• A. TRUE
• B. FALSE

正解：A

質問 # 73
When loading data from stage using COPY INTO, what options can you specify for the ON_ERROR clause?

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

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 # 74
It is recommended to assign ACCOUNTADMIN role to atleast two user

• A. TRUE
• B. FALSE

正解：A

質問 # 75
......

リアル問題集Snowflake ARA-C01試験問題 [更新されたのは2025年]：https://www.jpntest.com/shiken/ARA-C01-mondaishu

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