100%合格、売れ筋最上位ARA-C01試験材料は2024年最新のSnowflake練習試験合格させます
SnowPro Advanced Certification問題集でARA-C01試験完全版問題、試験学習ガイド
Snowflake ARA-C01（Snowpro Advanced Architect認定）認定試験は、スノーフレークを使用する企業や組織によってグローバルに認識される非常に評判の良い認定です。この認定試験は、データウェアハウジングとデータ分析の高度なアーキテクトになりたい個人のスキルと知識をテストするように設計されています。この認定は、これらの分野でのキャリアを前進させたい個人にとって貴重な資産であり、候補者が試験の準備を支援するために利用可能ないくつかのリソースがあります。
質問 # 54
With default settings for multi cluster warehouse, how does snowflake determines when to start a new cluster?
- A. Only if the system estimates there's enough query load to keep the cluster busy for at least 4 minutes.
- B. Only if the system estimates there's enough query load to keep the cluster busy for at least 6 minutes.
- C. Immediately when either a query is queued or the system detects that there's one more query than the currently-running clusters can execute
正解：C
質問 # 55
A company has a Snowflake account named ACCOUNTA in AWS us-east-1 region. The company stores its marketing data in a Snowflake database named MARKET_DB. One of the company's business partners has an account named PARTNERB in Azure East US 2 region. For marketing purposes the company has agreed to share the database MARKET_DB with the partner account.
Which of the following steps MUST be performed for the account PARTNERB to consume data from the MARKET_DB database?
- A. From account ACCOUNTA create a share of database MARKET_DB, and create a new database out of this share locally in AWS us-east-1 region. Then make this database the provider and share it with the PARTNERB account.
- B. Create a share of database MARKET_DB, and create a new database out of this share locally in AWS us-east-1 region. Then replicate this database to the partner's account PARTNERB.
- C. Create a new account (called AZABC123) in Azure East US 2 region. From account ACCOUNTA replicate the database MARKET_DB to AZABC123 and from this account set up the data sharing to the PARTNERB account.
- D. Create a new account (called AZABC123) in Azure East US 2 region. From account ACCOUNTA create a share of database MARKET_DB, create a new database out of this share locally in AWS us-east-1 region, and replicate this new database to AZABC123 account. Then set up data sharing to the PARTNERB account.
正解：C
解説：
Explanation
* Snowflake supports data sharing across regions and cloud platforms using account replication and share replication features. Account replication enables the replication of objects from a source account to one or more target accounts in the same organization. Share replication enables the replication of shares from a source account to one or more target accounts in the same organization1.
* To share data from the MARKET_DB database in the ACCOUNTA account in AWS us-east-1 region with the PARTNERB account in Azure East US 2 region, the following steps must be performed:
* Create a new account (called AZABC123) in Azure East US 2 region. This account will act as a bridge between the source and the target accounts. The new account must be linked to the ACCOUNTA account using an organization2.
* From the ACCOUNTA account, replicate the MARKET_DB database to the AZABC123 account using the account replication feature. This will create a secondary database in the AZABC123 account that is a replica of the primary database in the ACCOUNTA account3.
* From the AZABC123 account, set up the data sharing to the PARTNERB account using the share replication feature. This will create a share of the secondary database in the AZABC123 account and grant access to the PARTNERB account. The PARTNERB account can then create a database from the share and query the data4.
* Therefore, option C is the correct answer.
References: : Replicating Shares Across Regions and Cloud Platforms : Working with Organizations and Accounts : Replicating Databases Across Multiple Accounts : Replicating Shares Across Multiple Accounts
質問 # 56
A Snowflake Architect is designing an application and tenancy strategy for an organization where strong legal isolation rules as well as multi-tenancy are requirements.
Which approach will meet these requirements if Role-Based Access Policies (RBAC) is a viable option for isolating tenants?
- A. Create a multi-tenant table strategy if row level security is not viable for isolating tenants.
- B. Create an object for each tenant strategy if row level security is not viable for isolating tenants.
- C. Create accounts for each tenant in the Snowflake organization.
- D. Create an object for each tenant strategy if row level security is viable for isolating tenants.
正解：B
解説：
This approach meets the requirements of strong legal isolation and multi-tenancy. By creating separate accounts for each tenant, the application can ensure that each tenant has its own dedicated storage, compute, and metadata resources, as well as its own encryption keys and security policies. This provides the highest level of isolation and data protection among the tenancy models. Furthermore, by creating the accounts within the same Snowflake organization, the application can leverage the features of Snowflake Organizations, such as centralized billing, account management, and cross-account data sharing.
Reference:
Snowflake Organizations Overview | Snowflake Documentation
Design Patterns for Building Multi-Tenant Applications on Snowflake
質問 # 57
How is the change of local time due to daylight savings time handled in Snowflake tasks? (Choose two.)
- A. A task schedule will follow only the specified time and will fail to handle lost or duplicated hours.
- B. A frequent task execution schedule like minutes may not cause a problem, but will affect the task history.
- C. Task schedules can be designed to follow specified or local time zones to accommodate the time changes.
- D. A task will move to a suspended state during the daylight savings time change.
- E. A task scheduled in a UTC-based schedule will have no issues with the time changes.
正解：C、E
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 # 58
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 pipe
- B. A copy command at regular intervals
- C. A stream
- D. An external table
正解：A
質問 # 59
select metadata$filename, metadata$file_row_number from @filestage/data1.json.gz;
Please select the correct statements for the above-mentioned query.
- A. FILESTAGE is the stage name, METADATA$FILE_ROW_NUMBER will give the row number for each record in the container staged data file
- B. FILESTAGE is the file name, METADATA$FILE_ROW_NUMBER will give the path to the data file in the stage
- C. FILESTAGE is the stage name, METADATA$FILE_ROW_NUMBER will give the path to the data file in the stage
正解：A
質問 # 60
At which object type level can the APPLY MASKING POLICY, APPLY ROW ACCESS POLICY and APPLY SESSION POLICY privileges be granted?
- A. Global
- B. Table
- C. Database
- D. Schema
正解：B
質問 # 61
Following objects can be cloned in snowflake
- A. External tables
- B. Internal stages
- C. Temporary table
- D. Permanent table
- E. Transient table
正解：A、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 # 62
The IT Security team has identified that there is an ongoing credential stuffing attack on many of their organization's system.
What is the BEST way to find recent and ongoing login attempts to Snowflake?
- A. Query the LOGIN_HISTORY view in the ACCOUNT_USAGE schema in the SNOWFLAKE database.
- B. Call the LOGIN_HISTORY Information Schema table function.
- C. View the Users section in the Account tab in the Snowflake UI and review the last login column.
- D. View the History tab in the Snowflake UI and set up a filter for SQL text that contains the text "LOGIN".
正解：B
質問 # 63
With default settings, how long will a query run on snowflake
- A. Snowflake will cancel the query if the warehouse runs out of memory and hard disk storage
- B. Snowflake will cancel the query if the warehouse runs out of memory
- C. Snowflake will cancel the query if it runs more than 48 hours
- D. Snowflake will cancel the query if it runs more than 24 hours
正解：C
質問 # 64
An Architect runs the following SQL query:
How can this query be interpreted?
- A. FILEROWS is a file. FILE_ROW_NUMBER is the file format location.
- B. FILERONS is the file format location. FILE_ROW_NUMBER is a stage.
- C. FILEROWS is the table. FILE_ROW_NUMBER is the line number in the table.
- D. FILEROWS is a stage. FILE_ROW_NUMBER is line number in file.
正解：D
解説：
A stage is a named location in Snowflake that can store files for data loading and unloading. A stage can be internal or external, depending on where the files are stored.
The query in the question uses the LIST function to list the files in a stage named FILEROWS. The function returns a table with various columns, including FILE_ROW_NUMBER, which is the line number of the file in the stage.
Therefore, the query can be interpreted as listing the files in a stage named FILEROWS and showing the line number of each file in the stage.
Reference:
1: Stages
2: LIST Function
質問 # 65
A retail company has 2000+ stores spread across the country. Store Managers report that they are having trouble running key reports related to inventory management, sales targets, payroll, and staffing during business hours. The Managers report that performance is poor and time-outs occur frequently.
Currently all reports share the same Snowflake virtual warehouse.
How should this situation be addressed? (Select TWO).
- A. Advise the Store Manager team to defer report execution to off-business hours.
- B. Use a Business Intelligence tool for in-memory computation to improve performance.
- C. Configure a dedicated virtual warehouse for the Store Manager team.
- D. Configure the virtual warehouse to size 4-XL
- E. Configure the virtual warehouse to be multi-clustered.
正解：C、E
解説：
The best way to address the performance issues and time-outs faced by the Store Manager team is to configure a dedicated virtual warehouse for them and make it multi-clustered. This will allow them to run their reports independently from other workloads and scale up or down the compute resources as needed. A dedicated virtual warehouse will also enable them to apply specific security and access policies for their data. A multi-clustered virtual warehouse will provide high availability and concurrency for their queries and avoid queuing or throttling.
Using a Business Intelligence tool for in-memory computation may improve performance, but it will not solve the underlying issue of insufficient compute resources in the shared virtual warehouse. It will also introduce additional costs and complexity for the data architecture.
Configuring the virtual warehouse to size 4-XL may increase the performance, but it will also increase the cost and may not be optimal for the workload. It will also not address the concurrency and availability issues that may arise from sharing the virtual warehouse with other workloads.
Advising the Store Manager team to defer report execution to off-business hours may reduce the load on the shared virtual warehouse, but it will also reduce the timeliness and usefulness of the reports for the business. It will also not guarantee that the performance issues and time-outs will not occur at other times.
Reference:
Snowflake Architect Training
Snowflake SnowPro Advanced Architect Certification - Preparation Guide
SnowPro Advanced: Architect Exam Study Guide
質問 # 66
A company has a source system that provides JSON records for various loT operations. The JSON Is loading directly into a persistent table with a variant field. The data Is quickly growing to 100s of millions of records and performance to becoming an issue. There is a generic access pattern that Is used to filter on the create_date key within the variant field.
What can be done to improve performance?
- A. Incorporate the use of multiple tables partitioned by date ranges. When a user or process needs to query a particular date range, ensure the appropriate base table Is used.
- B. Alter the target table to include additional fields pulled from the JSON records. This would include a create_date field with a datatype of varchar. When this field is used in the filter, partition pruning will occur.
- C. Alter the target table to Include additional fields pulled from the JSON records. This would Include a create_date field with a datatype of time stamp. When this field Is used in the filter, partition pruning will occur.
- D. Validate the size of the warehouse being used. If the record count is approaching 100s of millions, size XL will be the minimum size required to process this amount of data.
正解：C
解説：
The correct answer is A because it improves the performance of queries by reducing the amount of data scanned and processed. By adding a create_date field with a timestamp data type, Snowflake can automatically cluster the table based on this field and prune the micro-partitions that do not match the filter condition. This avoids the need to parse the JSON data and access the variant field for every record.
Option B is incorrect because it does not improve the performance of queries. By adding a create_date field with a varchar data type, Snowflake cannot automatically cluster the table based on this field and prune the micro-partitions that do not match the filter condition. This still requires parsing the JSON data and accessing the variant field for every record.
Option C is incorrect because it does not address the root cause of the performance issue. By validating the size of the warehouse being used, Snowflake can adjust the compute resources to match the data volume and parallelize the query execution. However, this does not reduce the amount of data scanned and processed, which is the main bottleneck for queries on JSON data.
Option D is incorrect because it adds unnecessary complexity and overhead to the data loading and querying process. By incorporating the use of multiple tables partitioned by date ranges, Snowflake can reduce the amount of data scanned and processed for queries that specify a date range. However, this requires creating and maintaining multiple tables, loading data into the appropriate table based on the date, and joining the tables for queries that span multiple date ranges. Reference:
Snowflake Documentation: Loading Data Using Snowpipe: This document explains how to use Snowpipe to continuously load data from external sources into Snowflake tables. It also describes the syntax and usage of the COPY INTO command, which supports various options and parameters to control the loading behavior, such as ON_ERROR, PURGE, and SKIP_FILE.
Snowflake Documentation: Date and Time Data Types and Functions: This document explains the different data types and functions for working with date and time values in Snowflake. It also describes how to set and change the session timezone and the system timezone.
Snowflake Documentation: Querying Metadata: This document explains how to query the metadata of the objects and operations in Snowflake using various functions, views, and tables. It also describes how to access the copy history information using the COPY_HISTORY function or the COPY_HISTORY view.
Snowflake Documentation: Loading JSON Data: This document explains how to load JSON data into Snowflake tables using various methods, such as the COPY INTO command, the INSERT command, or the PUT command. It also describes how to access and query JSON data using the dot notation, the FLATTEN function, or the LATERAL join.
Snowflake Documentation: Optimizing Storage for Performance: This document explains how to optimize the storage of data in Snowflake tables to improve the performance of queries. It also describes the concepts and benefits of automatic clustering, search optimization service, and materialized views.
質問 # 67
What are some of the characteristics of result set caches? (Choose three.)
- A. Time Travel queries can be executed against the result set cache.
- B. The data stored in the result cache will contribute to storage costs.
- C. The result set cache is not shared between warehouses.
- D. Each time persisted results for a query are used, a 24-hour retention period is reset.
- E. Snowflake persists the data results for 24 hours.
- F. The retention period can be reset for a maximum of 31 days.
正解：D、E、F
質問 # 68
What is a key consideration when setting up search optimization service for a table?
- A. Search optimization service works best with a column that has a minimum of 100 K distinct values.
- 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 can help to optimize storage usage by compressing the data into a GZIP format.
正解：A
解説：
Search optimization service is a feature of Snowflake that can significantly improve the performance of certain types of lookup and analytical queries on tables. Search optimization service creates and maintains a persistent data structure called a search access path, which keeps track of which values of the table's columns might be found in each of its micro-partitions, allowing some micro-partitions to be skipped when scanning the table1.
Search optimization service can significantly improve query performance on partitioned external tables, which are tables that store data in external locations such as Amazon S3 or Google Cloud Storage. Partitioned external tables can leverage the search access path to prune the partitions that do not contain the relevant data, reducing the amount of data that needs to be scanned and transferred from the external location2.
The other options are not correct because:
A) Search optimization service works best with a column that has a high cardinality, which means that the column has many distinct values. However, there is no specific minimum number of distinct values required for search optimization service to work effectively. The actual performance improvement depends on the selectivity of the queries and the distribution of the data1.
C) Search optimization service does not help to optimize storage usage by compressing the data into a GZIP format. Search optimization service does not affect the storage format or compression of the data, which is determined by the file format options of the table. Search optimization service only creates an additional data structure that is stored separately from the table data1.
D) The table does not need to be clustered with a key having multiple columns for effective search optimization. Clustering is a feature of Snowflake that allows ordering the data in a table or a partitioned external table based on one or more clustering keys. Clustering can improve the performance of queries that filter on the clustering keys, as it reduces the number of micro-partitions that need to be scanned. However, clustering is not required for search optimization service to work, as search optimization service can skip micro-partitions based on any column that has a search access path, regardless of the clustering key3.
Reference:
1: Search Optimization Service | Snowflake Documentation
2: Partitioned External Tables | Snowflake Documentation
3: Clustering Keys | Snowflake Documentation
質問 # 69
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