[2025年04月] 合格 Snowflake ARA-C01 テストエンジンpdf - 完全版無料問題集 [Q16-Q37]

[2025年04月] 合格させるSnowflake ARA-C01テストエンジンPDFで完全版無料問題集

SnowPro Advanced Architect Certification練習テスト2025年最新のARA-C01ストレスなしで合格！

質問 # 16
Following objects can be cloned in snowflake

A. Temporary table
B. Transient table
C. Internal stages
D. External tables
E. Permanent table

正解：B、D、E

解説：
Explanation
* Snowflake supports cloning of various objects, such as databases, schemas, tables, stages, file formats, sequences, streams, tasks, and roles. Cloning creates a copy of an existing object in the system without copying the data or metadata. Cloning is also known as zero-copy cloning1.
* Among the objects listed in the question, the following ones can be cloned in Snowflake:
* Permanent table: A permanent table is a type of table that has a Fail-safe period and a Time Travel retention period of up to 90 days. A permanent table can be cloned using the CREATE TABLE ...
CLONE command2. Therefore, option A is correct.
* Transient table: A transient table is a type of table that does not have a Fail-safe period and can have a Time Travel retention period of either 0 or 1 day. A transient table can also be cloned using the CREATE TABLE ... CLONE command2. Therefore, option B is correct.
* External table: An external table is a type of table that references data files stored in an external location, such as Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage. An external table can be cloned using the CREATE EXTERNAL TABLE ... CLONE command3.
Therefore, option D is correct.
* The following objects listed in the question cannot be cloned in Snowflake:
* Temporary table: A temporary table is a type of table that is automatically dropped when the session ends or the current user logs out. Temporary tables do not support cloning4. Therefore, option C is incorrect.
* Internal stage: An internal stage is a type of stage that is managed by Snowflake and stores files in Snowflake's internal cloud storage. Internal stages do not support cloning5. Therefore, option E is incorrect.
References: : Cloning Considerations : CREATE TABLE ... CLONE : CREATE EXTERNAL TABLE ...
CLONE : Temporary Tables : Internal Stages

質問 # 17
What are characteristics of Dynamic Data Masking? (Select TWO).

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

正解：A、D

解説：
Dynamic Data Masking is a feature that allows masking sensitive data in query results based on the role of the user who executes the query. A masking policy is a user-defined function that specifies the masking logic and can be applied to one or more columns in one or more tables. A masking policy that is currently set on a table can be dropped using the ALTER TABLE command. A single masking policy can be applied to columns in different tables using the ALTER TABLE command with the SET MASKING POLICY clause. The other options are either incorrect or not supported by Snowflake. A masking policy cannot be applied to the value column of an external table, as external tables do not support column-level security. The role that creates the masking policy will not always see unmasked data in query results, as the masking policy can be applied to the owner role as well. A masking policy cannot be applied to a column with the GEOGRAPHY data type, as Snowflake only supports masking policies for scalar data types. Reference: Snowflake Documentation: Dynamic Data Masking, Snowflake Documentation: ALTER TABLE

質問 # 18
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 copy command at regular intervals
C. A stream
D. A pipe

正解：D

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 # 19
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. The copy into command with a task scheduled to run every second should be used to achieve the near-real time requirement.
B. 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.
C. 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.
D. All files should be concatenated before ingestion into Snowflake to avoid micro-ingestion.
E. 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.

正解：B、C

解説：
To provide near real-time sales results to category managers, the Architect can use the following steps:
* Create an external stage that references the cloud storage location where the POS sends the sales transactions files. The external stage should use the file format and encryption settings that match the source files2
* Create a Snowpipe that loads the files from the external stage into a target table in Snowflake. The Snowpipe should be configured with AUTO_INGEST = true, which means that it will automatically detect and ingest new files as they arrive in the external stage. The Snowpipe should also use a copy
* option to purge the files from the external stage after loading, to avoid duplicate ingestion3
* Create a stream on the target table that captures the INSERTS made by the Snowpipe. The stream should include the metadata columns that provide information about the file name, path, size, and last modified time. The stream should also have a retention period that matches the real-time analytics needs4
* Create a task that runs a query on the stream to process the near real-time data. The query should use the stream metadata to extract the store number and timestamps from the file name and path, and perform the calculations for exceptions, aggregations, and scoring using external functions. The query should also output the results to another table or view that can be accessed by the category managers. The task should be scheduled to run at a frequency that matches the real-time analytics needs, such as every minute or every 5 minutes.
The other options are not optimal or feasible for providing near real-time results:
* All files should be concatenated before ingestion into Snowflake to avoid micro-ingestion. This option is not recommended because it would introduce additional latency and complexity in the data pipeline.
Concatenating files would require an external process or service that monitors the cloud storage location and performs the file merging operation. This would delay the ingestion of new files into Snowflake and increase the risk of data loss or corruption. Moreover, concatenating files would not avoid micro-ingestion, as Snowpipe would still ingest each concatenated file as a separate load.
* 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. This option is not necessary because Snowpipe can automatically ingest new files from the external stage without requiring an external trigger or scheduler. Using an external scheduler would add more overhead and dependency to the data pipeline, and it would not guarantee near real-time ingestion, as it would depend on the polling interval and the availability of the external scheduler.
* The copy into command with a task scheduled to run every second should be used to achieve the near-real time requirement. This option is not feasible because tasks cannot be scheduled to run every second in Snowflake. The minimum interval for tasks is one minute, and even that is not guaranteed, as tasks are subject to scheduling delays and concurrency limits. Moreover, using the copy into command with a task would not leverage the benefits of Snowpipe, such as automatic file detection, load balancing, and micro-partition optimization. References:
* 1: SnowPro Advanced: Architect | Study Guide
* 2: Snowflake Documentation | Creating Stages
* 3: Snowflake Documentation | Loading Data Using Snowpipe
* 4: Snowflake Documentation | Using Streams and Tasks for ELT
* : Snowflake Documentation | Creating Tasks
* : Snowflake Documentation | Best Practices for Loading Data
* : Snowflake Documentation | Using the Snowpipe REST API
* : Snowflake Documentation | Scheduling Tasks
* : SnowPro Advanced: Architect | Study Guide
* : Creating Stages
* : Loading Data Using Snowpipe
* : Using Streams and Tasks for ELT
* : [Creating Tasks]
* : [Best Practices for Loading Data]
* : [Using the Snowpipe REST API]
* : [Scheduling Tasks]

質問 # 20
To convert JSON null value to SQL null value, you will use

A. IS_NULL_VALUE
B. STRIP_NULL_VALUE
C. NULL_IF

正解：B

質問 # 21
How is the change of local time due to daylight savings time handled in Snowflake tasks? (Choose two.)

A. A frequent task execution schedule like minutes may not cause a problem, but will affect the task history.
B. Task schedules can be designed to follow specified or local time zones to accommodate the time changes.
C. A task scheduled in a UTC-based schedule will have no issues with the time changes.
D. A task will move to a suspended state during the daylight savings time change.
E. A task schedule will follow only the specified time and will fail to handle lost or duplicated hours.

正解：B、C

解説：
According to the Snowflake documentation1 and the web search results2, these two statements are true about how the change of local time due to daylight savings time is handled in Snowflake tasks. A task is a feature that allows scheduling and executing SQL statements or stored procedures in Snowflake. A task can be scheduled using a cron expression that specifies the frequency and time zone of the task execution.
A task scheduled in a UTC-based schedule will have no issues with the time changes. UTC is a universal time standard that does not observe daylight savings time. Therefore, a task that uses UTC as the time zone will run at the same time throughout the year, regardless of the local time changes1.
Task schedules can be designed to follow specified or local time zones to accommodate the time changes. Snowflake supports using any valid IANA time zone identifier in the cron expression for a task. This allows the task to run according to the local time of the specified time zone, which may include daylight savings time adjustments. For example, a task that uses Europe/London as the time zone will run one hour earlier or later when the local time switches between GMT and BST12.
Reference:
Snowflake Documentation: Scheduling Tasks
Snowflake Community: Do the timezones used in scheduling tasks in Snowflake adhere to daylight savings?

質問 # 22
What are characteristics of Dynamic Data Masking? (Select TWO).

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

正解：A、D

解説：
Dynamic Data Masking is a feature that allows masking sensitive data in query results based on the role of the user who executes the query. A masking policy is a user-defined function that specifies the masking logic and can be applied to one or more columns in one or more tables. A masking policy that is currently set on a table can be dropped using the ALTER TABLE command. A single masking policy can be applied to columns in different tables using the ALTER TABLE command with the SET MASKING POLICY clause. The other options are either incorrect or not supported by Snowflake. A masking policy cannot be applied to the value column of an external table, as external tables do not support column-level security. The role that creates the masking policy will not always see unmasked data in query results, as the masking policy can be applied to the owner role as well. A masking policy cannot be applied to a column with the GEOGRAPHY data type, as Snowflake only supports masking policies for scalar data types. References: Snowflake Documentation:
Dynamic Data Masking, Snowflake Documentation: ALTER TABLE

質問 # 23
What are purposes for creating a storage integration? (Choose three.)

A. Support multiple external stages using one single Snowflake object.
B. Create private VPC endpoints that allow direct, secure connectivity between VPCs without traversing the public internet.
C. Avoid supplying credentials when creating a stage or when loading or unloading data.
D. Store a generated identity and access management (IAM) entity for an external cloud provider regardless of the cloud provider that hosts the Snowflake account.
E. Manage credentials from multiple cloud providers in one single Snowflake object.
F. Control access to Snowflake data using a master encryption key that is maintained in the cloud provider's key management service.

正解：A、C、D

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

正解：A

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
What are purposes for creating a storage integration? (Choose three.)

A. Support multiple external stages using one single Snowflake object.
B. Create private VPC endpoints that allow direct, secure connectivity between VPCs without traversing the public internet.
C. Avoid supplying credentials when creating a stage or when loading or unloading data.
D. Store a generated identity and access management (IAM) entity for an external cloud provider regardless of the cloud provider that hosts the Snowflake account.
E. Manage credentials from multiple cloud providers in one single Snowflake object.
F. Control access to Snowflake data using a master encryption key that is maintained in the cloud provider's key management service.

正解：A、C、D

解説：
The purpose of creating a storage integration in Snowflake includes:
B: Store a generated identity and access management (IAM) entity for an external cloud provider - This helps in managing authentication and authorization with external cloud storage without embedding credentials in Snowflake. It supports various cloud providers like AWS, Azure, or GCP, ensuring that the identity management is streamlined across platforms.
C: Support multiple external stages using one single Snowflake object - Storage integrations allow you to set up access configurations that can be reused across multiple external stages, simplifying the management of external data integrations.
D: Avoid supplying credentials when creating a stage or when loading or unloading data - By using a storage integration, Snowflake can interact with external storage without the need to continuously manage or expose sensitive credentials, enhancing security and ease of operations.
Reference: Snowflake documentation on storage integrations, found within the SnowPro Advanced: Architect course materials.

質問 # 26
An Architect has a design where files arrive every 10 minutes and are loaded into a primary database table using Snowpipe. A secondary database is refreshed every hour with the latest data from the primary database.
Based on this scenario, what Time Travel query options are available on the secondary database?

A. Using Time Travel, secondary database users can query every iterative version within each hour (the individual Snowpipe loads) in the retention window.
B. Using Time Travel, secondary database users can query every iterative version within each hour (the individual Snowpipe loads) and outside the retention window.
C. A query using Time Travel in the secondary database is available for every hourly table version within the retention window.
D. A query using Time Travel in the secondary database is available for every hourly table version within and outside the retention window.

正解：C

解説：
Snowflake's Time Travel feature allows users to query historical data within a defined retention period. In the given scenario, since the secondary database is refreshed every hour, Time Travel can be used to query each hourly version of the table as long as it falls within the retention window. This does not include individual Snowpipe loads within each hour unless they coincide with the hourly refresh.
References: The answer is verified using Snowflake's official documentation, which provides detailed information on Time Travel and its usage within the retention period123.

質問 # 27
Following objects can be cloned in snowflake

A. Temporary table
B. Transient table
C. Internal stages
D. External tables
E. Permanent table

正解：B、D、E

解説：
* Snowflake supports cloning of various objects, such as databases, schemas, tables, stages, file formats, sequences, streams, tasks, and roles. Cloning creates a copy of an existing object in the system without copying the data or metadata. Cloning is also known as zero-copy cloning1.
* Among the objects listed in the question, the following ones can be cloned in Snowflake:
* Permanent table: A permanent table is a type of table that has a Fail-safe period and a Time Travel retention period of up to 90 days. A permanent table can be cloned using the CREATE TABLE ...
CLONE command2. Therefore, option A is correct.
* Transient table: A transient table is a type of table that does not have a Fail-safe period and can have a Time Travel retention period of either 0 or 1 day. A transient table can also be cloned using the CREATE TABLE ... CLONE command2. Therefore, option B is correct.
* External table: An external table is a type of table that references data files stored in an external
* location, such as Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage. An external table can be cloned using the CREATE EXTERNAL TABLE ... CLONE command3.
Therefore, option D is correct.
* The following objects listed in the question cannot be cloned in Snowflake:
* Temporary table: A temporary table is a type of table that is automatically dropped when the session ends or the current user logs out. Temporary tables do not support cloning4. Therefore, option C is incorrect.
* Internal stage: An internal stage is a type of stage that is managed by Snowflake and stores files in Snowflake's internal cloud storage. Internal stages do not support cloning5. Therefore, option E is incorrect.
References: : Cloning Considerations : CREATE TABLE ... CLONE : CREATE EXTERNAL TABLE ...
CLONE : Temporary Tables : Internal Stages

質問 # 28
A company is trying to Ingest 10 TB of CSV data into a Snowflake table using Snowpipe as part of Its migration from a legacy database platform. The records need to be ingested in the MOST performant and cost-effective way.
How can these requirements be met?

A. Use on error = SKIP_FILE in the copy into command.
B. Use ON_ERROR = continue in the copy into command.
C. Use FURGE = FALSE in the copy into command.
D. Use purge = TRUE in the copy into command.

正解：A

解説：
For ingesting a large volume of CSV data into Snowflake using Snowpipe, especially for a substantial amount like 10 TB, the on error = SKIP_FILE option in the COPY INTO command can be highly effective. This approach allows Snowpipe to skip over files that cause errors during the ingestion process, thereby not halting or significantly slowing down the overall data load. It helps in maintaining performance and cost-effectiveness by avoiding the reprocessing of problematic files and continuing with the ingestion of other data.

質問 # 29
If a multi-cluster warehouse is resized, the new size applies to

A. Clusters that are currently running
B. Clusters that are started after the warehouse is resized
C. All of the above

正解：C

質問 # 30
What is a key consideration when setting up search optimization service for a table?

A. Search optimization service can help to optimize storage usage by compressing the data into a GZIP format.
B. The table must be clustered with a key having multiple columns for effective search optimization.
C. Search optimization service can significantly improve query performance on partitioned external tables.
D. Search optimization service works best with a column that has a minimum of 100 K distinct values.

正解：D

解説：
Explanation:

質問 # 31
What are characteristics of the use of transactions in Snowflake? (Select TWO).

A. Explicit transactions can contain DDL, DML, and query statements.
B. Explicit transactions should contain only DML statements and query statements. All DDL statements implicitly commit active transactions.
C. A transaction can be started explicitly by executing a begin transaction statement and end explicitly by executing an end transaction statement.
D. A transaction can be started explicitly by executing a begin work statement and end explicitly by executing a commit work statement.
E. The autocommit setting can be changed inside a stored procedure.

正解：A、C

解説：
In Snowflake, a transaction is a sequence of SQL statements that are processed as an atomic unit. All statements in the transaction are either applied (i.e. committed) or undone (i.e. rolled back) together.
Snowflake transactions guarantee ACID properties. A transaction can include both reads and writes1.
Explicit transactions are transactions that are started and ended explicitly by using the BEGIN TRANSACTION, COMMIT, and ROLLBACK statements. Snowflake supports the synonyms BEGIN WORK and BEGIN TRANSACTION, and COMMIT WORK and ROLLBACK WORK. Explicit transactions can contain DDL, DML, and query statements. However, explicit transactions should contain only DML statements and query statements, because DDL statements implicitly commit active transactions. This means that any changes made by the previous statements in the transaction are applied, and any changes made by the subsequent statements in the transaction are not part of the same transaction1.
The other options are not correct because:
* B. The autocommit setting can be changed inside a stored procedure, but this does not affect the use of transactions in Snowflake. The autocommit setting determines whether each statement is executed in its own implicit transaction or not. If autocommit is enabled, each statement is committed automatically. If autocommit is disabled, each statement is executed in an implicit transaction until an explicit COMMIT or ROLLBACK is issued. Changing the autocommit setting inside a stored procedure only affects the statements within the stored procedure, and does not affect the statements outside the stored procedure2.
* C. A transaction can be started explicitly by executing a BEGIN WORK statement and end explicitly by executing a COMMIT WORK statement, but this is not a characteristic of the use of transactions in Snowflake. This is just one way of writing the statements that start and end an explicit transaction. Snowflake also supports the synonyms BEGIN TRANSACTION and COMMIT, which are recommended over BEGIN WORK and COMMIT WORK1.
* D. A transaction can be started explicitly by executing a BEGIN TRANSACTION statement and end explicitly by executing an END TRANSACTION statement, but this is not a valid syntax in Snowflake.
Snowflake does not support the END TRANSACTION statement. The correct way to end an explicit transaction is to use the COMMIT or ROLLBACK statement1.
References:
* 1: Transactions | Snowflake Documentation
* 2: AUTOCOMMIT | Snowflake Documentation

質問 # 32
All multi cluster warehouses that were using the Legacy policy now use the default Standard policy

A. FALSE
B. TRUE

正解：B

質問 # 33
What actions are permitted when using the Snowflake SQL REST API? (Select TWO).

A. The use of a PUT command
B. Submitting multiple SQL statements in a single call
C. The use of a CALL command to a stored procedure which returns a table
D. The use of a GET command
E. The use of a ROLLBACK command

正解：C、D

解説：
A: The Snowflake SQL REST API does support the use of a GET command, which can be used to retrieve the status of a previously submitted query or to fetch the results of a query once it has been executed.
D: The use of a CALL command to a stored procedure is supported, which can return a result set, including a table. This allows the invocation of stored procedures within Snowflake through the SQL REST API.

質問 # 34
Snowflake data replication can be used to replicate data between cloud providers.

A. FALSE
B. TRUE

正解：B

質問 # 35
JSON and PARQUET files can be loaded to columns in the same table

A. TRUE
B. FALSE

正解：B

質問 # 36
The Business Intelligence team reports that when some team members run queries for their dashboards in parallel with others, the query response time is getting significantly slower What can a Snowflake Architect do to identify what is occurring and troubleshoot this issue?