合格させるDP-203ブレーン問題集で更新されたのは認定サンプル問題
オンラインDP-203テストブレーン問題集とテストエンジン
DP-203 試験は、Azure上のデータエンジニアリングに関連する様々なトピックをカバーしています。これには、データストレージソリューション、データ処理、データ統合、データセキュリティ、データモニタリングおよび最適化などが含まれます。候補者は、Azure Data Factory、Azure Databricks、Azure HDInsight、Azure Synapse Analyticsなどのデータ処理のためのさまざまなAzureサービスやツールの理解を証明する必要があります。
Microsoft DP-203試験は、Azureプラットフォームで働くデータエンジニアにとって必要不可欠な幅広いトピックをカバーしています。この試験は、Azure Blob Storage、Azure Cosmos DB、およびAzure SQL Databaseを使用したデータストレージソリューションの設計と実装、Azure Data Factory、Azure Databricks、およびAzure Stream Analyticsを使用したデータ処理のトピックに焦点を当てています。さらに、この試験では、ビッグデータソリューションの実装にAzure Data Lake StorageとAzure Synapse Analyticsの知識をテストします。
質問 # 179
You have the following Azure Stream Analytics query.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation
Box 1: Yes
You can now use a new extension of Azure Stream Analytics SQL to specify the number of partitions of a stream when reshuffling the data.
The outcome is a stream that has the same partition scheme. Please see below for an example:
WITH step1 AS (SELECT * FROM [input1] PARTITION BY DeviceID INTO 10),
step2 AS (SELECT * FROM [input2] PARTITION BY DeviceID INTO 10)
SELECT * INTO [output] FROM step1 PARTITION BY DeviceID UNION step2 PARTITION BY DeviceID Note: The new extension of Azure Stream Analytics SQL includes a keyword INTO that allows you to specify the number of partitions for a stream when performing reshuffling using a PARTITION BY statement.
Box 2: Yes
When joining two streams of data explicitly repartitioned, these streams must have the same partition key and partition count.
Box 3: Yes
10 partitions x six SUs = 60 SUs is fine.
Note: Remember, Streaming Unit (SU) count, which is the unit of scale for Azure Stream Analytics, must be adjusted so the number of physical resources available to the job can fit the partitioned flow. In general, six SUs is a good number to assign to each partition. In case there are insufficient resources assigned to the job, the system will only apply the repartition if it benefits the job.
Reference:
https://azure.microsoft.com/en-in/blog/maximize-throughput-with-repartitioning-in-azure-stream-analytics/
質問 # 180
You have an Azure Data Lake Storage Gen 2 account named storage1.
You need to recommend a solution for accessing the content in storage1. The solution must meet the following requirements:
List and read permissions must be granted at the storage account level.
Additional permissions can be applied to individual objects in storage1.
Security principals from Microsoft Azure Active Directory (Azure AD), part of Microsoft Entra, must be used for authentication.
What should you use? To answer, drag the appropriate components to the correct requirements. Each component may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
正解：
解説：
質問 # 181
You have an on-premises data warehouse that includes the following fact tables. Both tables have the following columns: DateKey, ProductKey, RegionKey. There are 120 unique product keys and 65 unique region keys.
Queries that use the data warehouse take a long time to complete.
You plan to migrate the solution to use Azure Synapse Analytics. You need to ensure that the Azure-based solution optimizes query performance and minimizes processing skew.
What should you recommend? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point
正解：
解説：
Explanation
Box 1: Hash-distributed
Box 2: ProductKey
ProductKey is used extensively in joins.
Hash-distributed tables improve query performance on large fact tables.
Box 3: Round-robin
Box 4: RegionKey
Round-robin tables are useful for improving loading speed.
Consider using the round-robin distribution for your table in the following scenarios:
When getting started as a simple starting point since it is the default If there is no obvious joining key If there is not good candidate column for hash distributing the table If the table does not share a common join key with other tables If the join is less significant than other joins in the query When the table is a temporary staging table Note: A distributed table appears as a single table, but the rows are actually stored across 60 distributions. The rows are distributed with a hash or round-robin algorithm.
Reference:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables-distribute
質問 # 182
You are designing an Azure Data Lake Storage Gen2 container to store data for the human resources (HR) department and the operations department at your company. You have the following data access requirements:
* After initial processing, the HR department data will be retained for seven years.
* The operations department data will be accessed frequently for the first six months, and then accessed once per month.
You need to design a data retention solution to meet the access requirements. The solution must minimize storage costs.
正解：
解説：
See the answer in explanation.
Explanation
answer is below
質問 # 183
You have an enterprise data warehouse in Azure Synapse Analytics named DW1 on a server named Server1.
You need to verify whether the size of the transaction log file for each distribution of DW1 is smaller than 160 GB.
What should you do?
- A. From Azure Monitor in the Azure portal, execute a query against the logs of DW1.
- B. On the master database, execute a query against the sys.dm_pdw_nodes_os_performance_counters dynamic management view.
- C. On DW1, execute a query against the sys.database_files dynamic management view.
正解：B
解説：
D. Execute a query against the logs of DW1 by using the Get-AzOperationalInsightSearchResult PowerShell cmdlet.
Explanation:
The following query returns the transaction log size on each distribution. If one of the log files is reaching 160 GB, you should consider scaling up your instance or limiting your transaction size.
-- Transaction log size
SELECT
instance_name as distribution_db,
cntr_value*1.0/1048576 as log_file_size_used_GB,
pdw_node_id
FROM sys.dm_pdw_nodes_os_performance_counters
WHERE
instance_name like 'Distribution_%'
AND counter_name = 'Log File(s) Used Size (KB)'
Reference:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-manage-monitor
質問 # 184
What should you do to improve high availability of the real-time data processing solution?
- A. Deploy an Azure Stream Analytics job and use an Azure Automation runbook to check the status of the job and to start the job if it stops.
- B. Set Data Lake Storage to use geo-redundant storage (GRS).
- C. Deploy a High Concurrency Databricks cluster.
- D. Deploy identical Azure Stream Analytics jobs to paired regions in Azure.
正解：D
解説：
Guarantee Stream Analytics job reliability during service updates
Part of being a fully managed service is the capability to introduce new service functionality and improvements at a rapid pace. As a result, Stream Analytics can have a service update deploy on a weekly (or more frequent) basis. No matter how much testing is done there is still a risk that an existing, running job may break due to the introduction of a bug. If you are running mission critical jobs, these risks need to be avoided. You can reduce this risk by following Azure's paired region model.
Scenario: The application development team will create an Azure event hub to receive real-time sales data, including store number, date, time, product ID, customer loyalty number, price, and discount amount, from the point of sale (POS) system and output the data to data storage in Azure Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-job-reliability Monitor and optimize data storage and data processing Question Set 2
質問 # 185
You have an Azure Data Factory pipeline that has the activities shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation
Box 1: succeed
Box 2: failed
Example:
Now let's say we have a pipeline with 3 activities, where Activity1 has a success path to Activity2 and a failure path to Activity3. If Activity1 fails and Activity3 succeeds, the pipeline will fail. The presence of the success path alongside the failure path changes the outcome reported by the pipeline, even though the activity executions from the pipeline are the same as the previous scenario.
Activity1 fails, Activity2 is skipped, and Activity3 succeeds. The pipeline reports failure.
Reference:
https://datasavvy.me/2021/02/18/azure-data-factory-activity-failures-and-pipeline-outcomes/
質問 # 186
You are processing streaming data from vehicles that pass through a toll booth.
You need to use Azure Stream Analytics to return the license plate, vehicle make, and hour the last vehicle passed during each 10-minute window.
How should you complete the query? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Reference:
https://docs.microsoft.com/en-us/stream-analytics-query/tumbling-window-azure-stream-analytics
質問 # 187
You have an Apache Spark DataFrame named temperatures. A sample of the data is shown in the following table.
You need to produce the following table by using a Spark SQL query.
How should you complete the query? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
正解：
解説：
Reference:
https://learnsql.com/cookbook/how-to-convert-an-integer-to-a-decimal-in-sql-server/
https://docs.microsoft.com/en-us/sql/t-sql/queries/from-using-pivot-and-unpivot
質問 # 188
You need to design the partitions for the product sales transactions. The solution must meet the sales transaction dataset requirements.
What should you include in the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-overview-what-is
Topic 2, Litware, inc.
Requirements
Business Goals
Litware wants to create a new analytics environment in Azure to meet the following requirements:
See inventory levels across the stores. Data must be updated as close to real time as possible.
Execute ad hoc analytical queries on historical data to identify whether the loyalty club discounts increase sales of the discounted products.
Every four hours, notify store employees about how many prepared food items to produce based on historical demand from the sales data.
Technical Requirements
Litware identifies the following technical requirements:
Minimize the number of different Azure services needed to achieve the business goals.
Use platform as a service (PaaS) offerings whenever possible and avoid having to provision virtual machines that must be managed by Litware.
Ensure that the analytical data store is accessible only to the company's on-premises network and Azure services.
Use Azure Active Directory (Azure AD) authentication whenever possible.
Use the principle of least privilege when designing security.
Stage Inventory data in Azure Data Lake Storage Gen2 before loading the data into the analytical data store. Litware wants to remove transient data from Data Lake Storage once the data is no longer in use. Files that have a modified date that is older than 14 days must be removed.
Limit the business analysts' access to customer contact information, such as phone numbers, because this type of data is not analytically relevant.
Ensure that you can quickly restore a copy of the analytical data store within one hour in the event of corruption or accidental deletion.
Planned Environment
Litware plans to implement the following environment:
The application development team will create an Azure event hub to receive real-time sales data, including store number, date, time, product ID, customer loyalty number, price, and discount amount, from the point of sale (POS) system and output the data to data storage in Azure.
Customer data, including name, contact information, and loyalty number, comes from Salesforce, a SaaS application, and can be imported into Azure once every eight hours. Row modified dates are not trusted in the source table.
Product data, including product ID, name, and category, comes from Salesforce and can be imported into Azure once every eight hours. Row modified dates are not trusted in the source table.
Daily inventory data comes from a Microsoft SQL server located on a private network.
Litware currently has 5 TB of historical sales data and 100 GB of customer data. The company expects approximately 100 GB of new data per month for the next year.
Litware will build a custom application named FoodPrep to provide store employees with the calculation results of how many prepared food items to produce every four hours.
Litware does not plan to implement Azure ExpressRoute or a VPN between the on-premises network and Azure.
質問 # 189
You have an Azure Synapse Analytics serverless SQL pool, an Azure Synapse Analytics dedicated SQL pool, an Apache Spark pool, and an Azure Data Lake Storage Gen2 account.
You need to create a table in a lake database. The table must be available to both the serverless SQL pool and the Spark pool.
Where should you create the table, and Which file format should you use for data in the table? TO answer, select the appropriate options in the answer are a.
NOTE: Each correct selection is worth one point.
正解：
解説：
質問 # 190
You have an Azure Data Factory pipeline that performs an incremental load of source data to an Azure Data Lake Storage Gen2 account.
Data to be loaded is identified by a column named LastUpdatedDate in the source table.
You plan to execute the pipeline every four hours.
You need to ensure that the pipeline execution meets the following requirements:
Automatically retries the execution when the pipeline run fails due to concurrency or throttling limits.
Supports backfilling existing data in the table.
Which type of trigger should you use?
- A. schedule
- B. on-demand
- C. tumbling window
- D. event
正解：C
解説：
In case of pipeline failures, tumbling window trigger can retry the execution of the referenced pipeline automatically, using the same input parameters, without the user intervention. This can be specified using the property "retryPolicy" in the trigger definition.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/how-to-create-tumbling-window-trigger
質問 # 191
You are implementing an Azure Stream Analytics solution to process event data from devices.
The devices output events when there is a fault and emit a repeat of the event every five seconds until the fault is resolved. The devices output a heartbeat event every five seconds after a previous event if there are no faults present.
A sample of the events is shown in the following table.
You need to calculate the uptime between the faults.
How should you complete the Stream Analytics SQL query? To answer, select the appropriate options in the answer are a.
NOTE: Each correct selection is worth one point.
正解：
解説：
Reference:
https://docs.microsoft.com/en-us/stream-analytics-query/session-window-azure-stream-analytics
https://docs.microsoft.com/en-us/stream-analytics-query/tumbling-window-azure-stream-analytics
質問 # 192
You have a table named SalesFact in an enterprise data warehouse in Azure Synapse Analytics. SalesFact contains sales data from the past 36 months and has the following characteristics:
Is partitioned by month
Contains one billion rows
Has clustered columnstore indexes
At the beginning of each month, you need to remove data from SalesFact that is older than 36 months as quickly as possible.
Which three actions should you perform in sequence in a stored procedure? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
正解：
解説：
Reference:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables-partition
質問 # 193
You plan to create a dimension table in Azure Synapse Analytics that will be less than 1 GB.
You need to create the table to meet the following requirements:
* Provide the fastest Query time.
* Minimize data movement during queries.
Which type of table should you use?
- A. round-robin
- B. heap
- C. hash distributed
- D. replicated
正解：D
解説：
Explanation
A replicated table has a full copy of the table accessible on each Compute node. Replicating a table removes the need to transfer data among Compute nodes before a join or aggregation. Since the table has multiple copies, replicated tables work best when the table size is less than 2 GB compressed. 2 GB is not a hard limit.
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/design-guidance-for-replicated-tab
質問 # 194
You are building an Azure Stream Analytics job that queries reference data from a product catalog file. The file is updated daily.
The reference data input details for the file are shown in the Input exhibit. (Click the Input tab.)
The storage account container view is shown in the Refdata exhibit. (Click the Refdata tab.)
You need to configure the Stream Analytics job to pick up the new reference data.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation
Graphical user interface, application, table Description automatically generated
Box 1: {date}/product.csv
In the 2nd exhibit we see: Location: refdata / 2020-03-20
Note: Path Pattern: This is a required property that is used to locate your blobs within the specified container.
Within the path, you may choose to specify one or more instances of the following 2 variables:
{date}, {time}
Example 1: products/{date}/{time}/product-list.csv
Example 2: products/{date}/product-list.csv
Example 3: product-list.csv
Box 2: YYYY-MM-DD
Note: Date Format [optional]: If you have used {date} within the Path Pattern that you specified, then you can select the date format in which your blobs are organized from the drop-down of supported formats.
Example: YYYY/MM/DD, MM/DD/YYYY, etc.
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-use-reference-data
質問 # 195
You have an Azure Data Factory pipeline that has the activity shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
正解：
解説：
Explanation
質問 # 196
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リアルMicrosoft DP-203試験問題集には正解318問題と解答があります：https://www.goshiken.com/Microsoft/DP-203-mondaishu.html
Microsoft DP-203認定のリアル2024年最新の模擬試験：https://drive.google.com/open?id=1yhGQTbTUit6_YvAZC90edsw-Cewu3XBQ