合格させるSnowflake ARA-R01試験には保証が付きます。更新されたのは163問があります [Q35-Q56]

35、A Snowflake Architect is designing a multiple-account design strategy.
This strategy will be MOST cost-effective with which scenarios? (Select TWO).

The company wants to clone a production database that resides on AWS to a development database that resides on Azure.

The company needs to share data between two databases, where one must support Payment Card Industry Data Security Standard (PCI DSS) compliance but the other one does not.

The company needs to support different role-based access control features for the development, test, and production environments.

The company security policy mandates the use of different Active Directory instances for the development, test, and production environments.

The company must use a specific network policy for certain users to allow and block given IP addresses.

36、An Architect is troubleshooting a query with poor performance using the QUERY_HIST0RY function. The Architect observes that the COMPILATIONJHME is greater than the EXECUTIONJTIME.
What is the reason for this?

The query is processing a very large dataset.

The query has overly complex logic.

The query is queued for execution.

The query is reading from remote storage.

37、Which Snowflake architecture recommendation needs multiple Snowflake accounts for implementation?

Enable a disaster recovery strategy across multiple cloud providers.

Create external stages pointing to cloud providers and regions other than the region hosting the Snowflake account.

Enable zero-copy cloning among the development, test, and production environments.

Enable separation of the development, test, and production environments.

38、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?

An external table

A pipe

A stream

A copy command at regular intervals

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>

39、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 schema. 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.)

The staging schema has not been setup for MANAGED ACCESS.

The DATA_RETENTION_TIME_IN_DAYS for the staging schema has been set to 1 day.

The tables exceed the 1 TB limit for data recovery.

The staging tables are of the TRANSIENT type.

The DevOps role should be granted ALLOW_RECOVERY privilege on the staging schema.

The DATA_RETENTION_TIME_IN_DAYS parameter controls the Time Travel retention period for an object (database, schema, or table) in Snowflake. This parameter specifies the numberof days for which historical data is preserved and can be accessed using Time Travel operations (SELECT, CREATE … CLONE, UNDROP)1.
The requirement for recovery of staging tables on a rolling 7-day basis means that the DATA_RETENTION_TIME_IN_DAYS parameter should be set to 7 at the database level. However, this parameter can be overridden at the lower levels (schema or table) if they have a different value1.
Therefore, one possible cause for certain tables to remain unrecoverable past 1 day is that the DATA_RETENTION_TIME_IN_DAYS for the staging schema has been set to 1 day. This would override the database level setting and limit the Time Travel retention period for all the tables in the schema to 1 day. To fix this, the parameter should be unset or set to 7 at the schema level1. Therefore, option B is correct.
Another possible cause for certain tables to remain unrecoverable past 1 day is that the staging tables are of the TRANSIENT type. Transient tables are tables that do not have a Fail-safe period and can have a Time Travel retention period of either 0 or 1 day. Transient tables are suitable for temporary or intermediate data that can be easily reproduced or replicated2. To fix this, the tables should be created as permanent tables, which can have a Time Travel retention period of up to 90 days1. Therefore, option D is correct.
Option A is incorrect because the MANAGED ACCESS feature is not related to the data recovery requirement. MANAGED ACCESS is a feature that allows granting access privileges to objects without explicitly granting the privileges to roles. It does not affect the Time Travel retention period or the data availability3.
Option C is incorrect because there is no 1 TB limit for data recovery in Snowflake. The data storage size does not affect the Time Travel retention period or the data availability4.
Option E is incorrect because there is no ALLOW_RECOVERY privilege in Snowflake. The privilege required to perform Time Travel operations is SELECT, which allows querying historical data in tables5.
References: : Understanding & Using Time Travel : Transient Tables : Managed Access : Understanding Storage Cost : Table Privileges

40、A company’s client application supports multiple authentication methods, and is using Okta.
What is the best practice recommendation for the order of priority when applications authenticate to Snowflake?

1) OAuth (either Snowflake OAuth or External OAuth)
2) External browser
3) Okta native authentication
4) Key Pair Authentication, mostly used for service account users
5) Password

1) External browser, SSO
2) Key Pair Authentication, mostly used for development environment users
3) Okta native authentication
4) OAuth (ether Snowflake OAuth or External OAuth)
5) Password

1) Okta native authentication
2) Key Pair Authentication, mostly used for production environment users
3) Password
4) OAuth (either Snowflake OAuth or External OAuth)
5) External browser, SSO

1) Password
2) Key Pair Authentication, mostly used for production environment users
3) Okta native authentication
4) OAuth (either Snowflake OAuth or External OAuth)
5) External browser, SSO

This is the best practice recommendation for the order of priority when applications authenticate to Snowflake, according to the Snowflake documentation and the web search results. Authentication is the process of verifying the identity of a user or application that connects to Snowflake. Snowflake supports multiple authentication methods, each with different advantages and disadvantages. The recommended order of priority is based on the following factors:
* Security: The authentication method should provide a high level of security and protection against unauthorized access or data breaches. The authentication method should also support multi-factor authentication (MFA) or single sign-on (SSO) for additional security.
* Convenience: The authentication method should provide a smooth and easy user experience, without requiring complex or manual steps. The authentication method should also support seamless integration with external identity providers or applications.
* Flexibility: The authentication method should provide a range of options and features to suit different use cases and scenarios. The authentication method should also support customization and configuration to meet specific requirements.
Based on these factors, the recommended order of priority is:
* OAuth (either Snowflake OAuth or External OAuth): OAuth is an open standard for authorization that allows applications to access Snowflake resources on behalf of a user, without exposing the user’s credentials. OAuth provides a high level of security, convenience, and flexibility, as it supports MFA, SSO, token-based authentication, and various grant types and scopes. OAuth can be implemented using either Snowflake OAuth or External OAuth, depending on the identity provider and the application12.
* External browser: External browser is an authentication method that allows users to log in to Snowflake
* using a web browser and an external identity provider, such as Okta, Azure AD, or Ping Identity.
External browser provides a high level of security and convenience, as it supports MFA, SSO, and federated authentication. External browser also provides a consistent user interface and experience across different platforms and devices34.
* Okta native authentication: Okta native authentication is an authentication method that allows users to log in to Snowflake using Okta as the identity provider, without using a web browser. Okta native authentication provides a high level of security and convenience, as it supports MFA, SSO, and federated authentication. Okta native authentication also provides a native user interface and experience for Okta users, and supports various Okta features, such as password policies and user management56.
* Key Pair Authentication: Key Pair Authentication is an authentication method that allows users to log in to Snowflake using a public-private key pair, without using a password. Key Pair Authentication provides a high level of security, as it relies on asymmetric encryption and digital signatures. Key Pair Authentication also provides a flexible and customizable authentication option, as it supports various key formats, algorithms, and expiration times. Key Pair Authentication is mostly used for service account users, such as applications or scripts that connect to Snowflake programmatically7 .
* Password: Password is the simplest and most basic authentication method that allows users to log in to Snowflake using a username and password. Password provides a low level of security, as it relies on symmetric encryption and is vulnerable to brute force attacks or phishing. Password also provides a low level of convenience and flexibility, as it requires manual input and management, and does not support MFA or SSO. Password is the least recommended authentication method, and should be used only as a last resort or for testing purposes .
References:
* Snowflake Documentation: Snowflake OAuth
* Snowflake Documentation: External OAuth
* Snowflake Documentation: External Browser Authentication
* Snowflake Blog: How to Use External Browser Authentication with Snowflake
* Snowflake Documentation: Okta Native Authentication
* Snowflake Blog: How to Use Okta Native Authentication with Snowflake
* Snowflake Documentation: Key Pair Authentication
* [Snowflake Blog: How to Use Key Pair Authentication with Snowflake]
* [Snowflake Documentation: Password Authentication]
* [Snowflake Blog: How to Use Password Authentication with Snowflake]

41、A global company needs to securely share its sales and Inventory data with a vendor using a Snowflake account.
The company has its Snowflake account In the AWS eu-west 2 Europe (London) region. The vendor’s Snowflake account Is on the Azure platform in the West Europe region. How should the company’s Architect configure the data share?

1. Create a share.
2. Add objects to the share.
3. Add a consumer account to the share for the vendor to access.

1. Create a share.
2. Create a reader account for the vendor to use.
3. Add the reader account to the share.

1. Create a new role called db_share.
2. Grant the db_share role privileges to read data from the company database and schema.
3. Create a user for the vendor.
4. Grant the ds_share role to the vendor’s users.

1. Promote an existing database in the company’s local account to primary.
2. Replicate the database to Snowflake on Azure in the West-Europe region.
3. Create a share and add objects to the share.
4. Add a consumer account to the share for the vendor to access.

42、An Architect entered the following commands in sequence:

USER1 cannot find the table.
Which of the following commands does the Architect need to run for USER1 to find the tables using the Principle of Least Privilege? (Choose two.)

GRANT ROLE PUBLIC TO ROLE INTERN;

GRANT USAGE ON DATABASE SANDBOX TO ROLE INTERN;

GRANT USAGE ON SCHEMA SANDBOX.PUBLIC TO ROLE INTERN;

GRANT OWNERSHIP ON DATABASE SANDBOX TO USER INTERN;

GRANT ALL PRIVILEGES ON DATABASE SANDBOX TO ROLE INTERN;

43、An Architect for a multi-national transportation company has a system that is used to check the weather conditions along vehicle routes. The data is provided to drivers.
The weather information is delivered regularly by a third-party company and this information is generated as JSON structure. Then the data is loaded into Snowflake in a column with a VARIANT data type. This table is directly queried to deliver the statistics to the drivers with minimum time lapse.
A single entry includes (but is not limited to):
– Weather condition; cloudy, sunny, rainy, etc.
– Degree
– Longitude and latitude
– Timeframe
– Location address
– Wind
The table holds more than 10 years’ worth of data in order to deliver the statistics from different years and locations. The amount of data on the table increases every day.
The drivers report that they are not receiving the weather statistics for their locations in time.
What can the Architect do to deliver the statistics to the drivers faster?

Create an additional table in the schema for longitude and latitude. Determine a regular task to fill this information by extracting it from the JSON dataset.

Add search optimization service on the variant column for longitude and latitude in order to query the information by using specific metadata.

Divide the table into several tables for each year by using the timeframe information from the JSON dataset in order to process the queries in parallel.

Divide the table into several tables for each location by using the location address information from the JSON dataset in order to process the queries in parallel.

44、Which system functions does Snowflake provide to monitor clustering information within a table (Choose two.)

SYSTEM$CLUSTERING_INFORMATION

SYSTEM$CLUSTERING_USAGE

SYSTEM$CLUSTERING_DEPTH

SYSTEM$CLUSTERING_KEYS

SYSTEM$CLUSTERING_PERCENT

45、An Architect is troubleshooting a query with poor performance using the QUERY function. The Architect observes that the COMPILATION_TIME Is greater than the EXECUTION_TIME.
What is the reason for this?

The query is processing a very large dataset.

The query has overly complex logic.

The query Is queued for execution.

The query Is reading from remote storage

The correct answer is B because the compilation time is the time it takes for the optimizer to create an optimal query plan for the efficient execution of the query. The compilation time depends on the complexity of the query, such as the number of tables, columns, joins, filters, aggregations, subqueries, etc. The more complex the query, the longer it takes to compile.
Option A is incorrect because the query processing time is not affected by the size of the dataset, but by the size of the virtual warehouse. Snowflake automatically scales the compute resources to match the data volume and parallelizes the query execution. The size of the dataset may affect the execution time, but not the compilation time.
Option C is incorrect because the query queue time is not part of the compilation time or the execution time. It is a separate metric that indicates how long the query waits for a warehouse slot before it starts running. The query queue time depends on the warehouse load, concurrency, and priority settings.
Option D is incorrect because the query remote IO time is not part of the compilation time or the execution time. It is a separate metric that indicates how long the query spends reading data from remote storage, such as S3 or Azure Blob Storage. The query remote IO time depends on the network latency, bandwidth, and caching efficiency. References:
Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time: This article explains why the total duration (compilation + execution) time is an essential metric to measure query performance in Snowflake. It discusses the reasons for the long compilation time, including query complexity and the number of tables and columns.
Exploring Execution Times: This document explains how to examine the past performance of queries and tasks using Snowsight or by writing queries against views in the ACCOUNT_USAGE schema. It also describes the different metrics and dimensions that affect query performance, such as duration, compilation, execution, queue, and remote IO time.
What is the “compilation time” and how to optimize it?: This community post provides some tips and best practices on how to reduce the compilation time, such as simplifying the query logic, using views or common table expressions, and avoiding unnecessary columns or joins.

46、A group of Data Analysts have been granted the role analyst role. They need a Snowflake database where they can create and modify tables, views, and other objects to load with their own data. The Analysts should not have the ability to give other Snowflake users outside of their role access to this data.
How should these requirements be met?

Grant ANALYST_R0LE OWNERSHIP on the database, but make sure that ANALYST_ROLE does not have the MANAGE GRANTS privilege on the account.

Grant SYSADMIN ownership of the database, but grant the create schema privilege on the database to the ANALYST_ROLE.

Make every schema in the database a managed access schema, owned by SYSADMIN, and grant create privileges on each schema to the ANALYST_ROLE for each type of object that needs to be created.

Grant ANALYST_ROLE ownership on the database, but grant the ownership on future [object type] s in database privilege to SYSADMIN.

47、An Architect would like to save quarter-end financial results for the previous six years.
Which Snowflake feature can the Architect use to accomplish this?

Search optimization service

Materialized view

Time Travel

Zero-copy cloning

Secure views

48、Consider the following scenario where a masking policy is applied on the CREDICARDND column of the CREDITCARDINFO table. The masking policy definition Is as follows:

Sample data for the CREDITCARDINFO table is as follows:
NAME EXPIRYDATE CREDITCARDNO
JOHN DOE 2022-07-23 4321 5678 9012 1234
if the Snowflake system rotes have not been granted any additional roles, what will be the result?

The sysadmin can see the CREDICARDND column data in clear text.

The owner of the table will see the CREDICARDND column data in clear text.

Anyone with the Pl_ANALYTICS role will see the last 4 characters of the CREDICARDND column data in dear text.

Anyone with the Pl_ANALYTICS role will see the CREDICARDND column as*** ‘MASKED* **’.

49、What is a characteristic of Role-Based Access Control (RBAC) as used in Snowflake?

Privileges can be granted at the database level and can be inherited by all underlying objects.

A user can use a “super-user” access along with securityadmin to bypass authorization checks and access all databases, schemas, and underlying objects.

A user can create managed access schemas to support future grants and ensure only schema owners can grant privileges to other roles.

A user can create managed access schemas to support current and future grants and ensure only object owners can grant privileges to other roles.

Role-Based Access Control (RBAC) is the Snowflake Access Control Framework that allows privileges to be granted by object owners to roles, and roles, in turn, can be assigned to users to restrict or allow actions to be performed on objects. A characteristic of RBAC as used in Snowflake is:
Privileges can be granted at the database level and can be inherited by all underlying objects. This means that a role that has a certain privilege on a database, such as CREATE SCHEMA or USAGE, can also perform the same action on any schema, table, view, or other object within that database, unless explicitly revoked. This simplifies the access control management and reduces the number of grants required.
A user can create managed access schemas to support future grants and ensure only schema owners can grant privileges to other roles. This means that a user can create a schema with the MANAGED ACCESS option, which changes the default behavior of object ownership and privilege granting within the schema. In a managed access schema, object owners lose the ability to grant privileges on their objects to other roles, and only the schema owner or a role with the MANAGE GRANTS privilege can do so. This enhances the security and governance of the schema and its objects.
The other options are not characteristics of RBAC as used in Snowflake:
A user can use a “super-user” access along with securityadmin to bypass authorization checks and access all databases, schemas, and underlying objects. This is not true, as there is no such thing as a
“super-user” access in Snowflake. The securityadmin role is a predefined role that can manage users and roles, but it does not have any privileges on any database objects by default. To access any object, the securityadmin role must be explicitly granted the appropriate privilege by the object owner or another role with the grant option.
A user can create managed access schemas to support current and future grants and ensure only object owners can grant privileges to other roles. This is not true, as this contradicts the definition of a managed access schema. In a managed access schema, object owners cannot grant privileges on their objects to other roles, and only the schema owner or a role with the MANAGE GRANTS privilege can do so.
References:
Overview of Access Control
A Functional Approach For Snowflake’s Role-Based Access Controls
Snowflake Role-Based Access Control simplified
Snowflake RBAC security prefers role inheritance to role composition
Overview of Snowflake Role Based Access Control

50、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).

Snowflake cannot handle the joins implied in a 3NF data model.

The Architect wants to remove data duplication from the data stored in Snowflake.

The Architect is designing a landing zone to receive raw data into Snowflake.

The Bl tool needs a data model that allows users to summarize facts across different dimensions, or to drill down from the summaries.

The Architect wants to present a simple flattened single view of the data to a particular group of end users.

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.
References:
* 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

51、Which of the following ingestion methods can be used to load near real-time data by using the messaging services provided by a cloud provider?

Snowflake Connector for Kafka

Snowflake streams

Snowpipe

Spark

52、When loading data from stage using COPY INTO, what options can you specify for the ON_ERROR clause?

CONTINUE

SKIP_FILE

ABORT_STATEMENT

FAIL

53、Which Snowflake data modeling approach is designed for BI queries?

3 NF

Star schema

Data Vault

Snowflake schema

54、Which statements describe characteristics of the use of materialized views in Snowflake? (Choose two.)

They can include ORDER BY clauses.

They cannot include nested subqueries.

They can include context functions, such as CURRENT_TIME().

They can support MIN and MAX aggregates.

They can support inner joins, but not outer joins.

55、Is it possible for a data provider account with a Snowflake Business Critical edition to share data with an Enterprise edition data consumer account?

A Business Critical account cannot be a data sharing provider to an Enterprise consumer. Any consumer accounts must also be Business Critical.

If a user in the provider account with role authority to create or alter share adds an Enterprise account as a consumer, it can import the share.

If a user in the provider account with a share owning role sets share_restrictions to False when adding an Enterprise consumer account, it can import the share.

If a user in the provider account with a share owning role which also has override share restrictions privilege share_restrictions set to False when adding an Enterprise consumer account, it can import the share.

Data sharing is a feature that allows Snowflake accounts to share data with each other without the need for data movement or copying1. Data sharing is enabled by creating shares, which are collections of database objects (tables, views, secure views, and secure UDFs) that can be accessed by other accounts, called consumers2.
By default, Snowflake does not allow sharing data from a Business Critical edition account to a non-Business Critical edition account. This is because Business Critical edition offers higher levels of data protection and encryption than other editions, and sharing data with lower editions may compromise the security and compliance of the data3.
However, Snowflake provides the OVERRIDE SHARE RESTRICTIONS global privilege, which allows a user to override the default restriction and share data from a Business Critical edition account to a non-Business Critical edition account. This privilege is granted to the ACCOUNTADMIN role by default, and can be granted to other roles as well4.
To enable data sharing from a Business Critical edition account to an Enterprise edition account, the following steps are required34:
A user in the provider account with the OVERRIDE SHARE RESTRICTIONS privilege must create or alter a share and add the Enterprise edition account as a consumer. The user must also set the share_restrictions parameter to False when adding the consumer. This parameter indicates whether the share is restricted to Business Critical edition accounts only. Setting it to False allows the share to be imported by lower edition accounts.
A user in the consumer account with the IMPORT SHARE privilege must import the share and grant access to the share objects to other roles in the account. The user must also set the share_restrictions parameter to False when importing the share. This parameter indicates whether the consumer account accepts shares from Business Critical edition accounts only. Setting it to False allows the consumer account to import shares from lower edition accounts.
References:
1: Introduction to Secure Data Sharing | Snowflake Documentation
2: Creating Secure Data Shares | Snowflake Documentation
3: Enable Data Share:Business Critical Account to Lower Edition | Medium
4: Enabling sharing from a Business critical account to a non-business … | Snowflake Documentation

56、A company has built a data pipeline using Snowpipe to ingest files from an Amazon S3 bucket. Snowpipe is configured to load data into staging database tables. Then a task runs to load the data from the staging database tables into the reporting database tables.
The company is satisfied with the availability of the data in the reporting database tables, but the reporting tables are not pruning effectively. Currently, a size 4X-Large virtual warehouse is being used to query all of the tables in the reporting database.
What step can be taken to improve the pruning of the reporting tables?

Eliminate the use of Snowpipe and load the files into internal stages using PUT commands.

Increase the size of the virtual warehouse to a size 5X-Large.

Use an ORDER BY <cluster_key (s) > command to load the reporting tables.

Create larger files for Snowpipe to ingest and ensure the staging frequency does not exceed 1 minute.

合格させるSnowflake ARA-R01試験には保証が付きます。更新されたのは163問があります [Q35-Q56]

コメントを残すコメントをキャンセル

関連認証

ARA-R01 練習テスト

最近の投稿

アーカイブ

カテゴリー

合格させるSnowflake ARA-R01試験には保証が付きます。更新されたのは163問があります [Q35-Q56]

コメントを残す コメントをキャンセル

関連認証

ARA-R01 練習テスト

最近の投稿

アーカイブ

カテゴリー

コメントを残すコメントをキャンセル