最新のDP-203問題集PDFでDP-203リアルで有効な正確な問題集は352問題と解答が待ってます！
100%無料DP-203試験問題集リアルMicrosoft Certified: Azure Data Engineer Associate問題集を試そう
Microsoft DP-203 認定試験の出題範囲：
| トピック | 出題範囲 |
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| トピック 2 |
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| トピック 3 |
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DP-203試験を受けるには、候補者はデータエンジニアリングの概念とAzure Servicesに協力した経験を十分に理解する必要があります。候補者は、データストレージソリューション、データ処理、およびデータセキュリティの経験を持つことをお勧めします。候補者は、公式のMicrosoftトレーニングコース、DP-203T00-A：Microsoft Azureのデータエンジニアリング、またはオンラインで入手可能な学習資料を使用することにより、試験の準備をすることができます。
質問 # 33
You have a self-hosted integration runtime in Azure Data Factory.
The current status of the integration runtime has the following configurations:
Status: Running
Type: Self-Hosted
Running / Registered Node(s): 1/1
High Availability Enabled: False
Linked Count: 0
Queue Length: 0
Average Queue Duration. 0.00s
The integration runtime has the following node details:
Name: X-M
Status: Running
Available Memory: 7697MB
CPU Utilization: 6%
Network (In/Out): 1.21KBps/0.83KBps
Concurrent Jobs (Running/Limit): 2/14
Role: Dispatcher/Worker
Credential Status: In Sync
Use the drop-down menus to select the answer choice that completes each statement based on the information presented.
NOTE: Each correct selection is worth one point.
正解：
解説：
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/create-self-hosted-integration-runtime
質問 # 34
You have an Azure Synapse Analytics dedicated SQL pool named Pool1 that contains an external table named Sales. Sales contains sales data. Each row in Sales contains data on a single sale, including the name of the salesperson.
You need to implement row-level security (RLS). The solution must ensure that the salespeople can access only their respective sales.
What should you do? 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: A security policy for sale
Here are the steps to create a security policy for Sales:
* Create a user-defined function that returns the name of the current user:
* CREATE FUNCTION dbo.GetCurrentUser()
* RETURNS NVARCHAR(128)
* AS
* BEGIN
* RETURN SUSER_SNAME();
* END;
* Create a security predicate function that filters the Sales table based on the current user:
* CREATE FUNCTION dbo.SalesPredicate(@salesperson NVARCHAR(128))
* RETURNS TABLE
* WITH SCHEMABINDING
* AS
* RETURN SELECT 1 AS access_result
* WHERE @salesperson = SalespersonName;
* Create a security policy on the Sales table that uses the SalesPredicate function to filter the data:
* CREATE SECURITY POLICY SalesFilter
* ADD FILTER PREDICATE dbo.SalesPredicate(dbo.GetCurrentUser()) ON dbo.Sales
* WITH (STATE = ON);
By creating a security policy for the Sales table, you ensure that each salesperson can only access their own sales data. The security policy uses a user-defined function to get the name of the current user and a security predicate function to filter the Sales table based on the current user.
Box 2: table-value function
to restrict row access by using row-level security, you need to create a table-valued function that returns a table of values that represent the rows that a user can access. You then use this function in a security policy that applies a predicate on the table.
質問 # 35
What should you recommend using to secure sensitive customer contact information?
- A. Transparent Data Encryption (TDE)
- B. row-level security
- C. data labels
- D. column-level security
正解：D
解説：
Scenario: All cloud data must be encrypted at rest and in transit.
Always Encrypted is a feature designed to protect sensitive data stored in specific database columns from access (for example, credit card numbers, national identification numbers, or data on a need to know basis). This includes database administrators or other privileged users who are authorized to access the database to perform management tasks, but have no business need to access the particular data in the encrypted columns. The data is always encrypted, which means the encrypted data is decrypted only for processing by client applications with access to the encryption key.
References:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-security-overview
質問 # 36
You have a data model that you plan to implement in a data warehouse in Azure Synapse Analytics as shown in the following exhibit.
All the dimension tables will be less than 2 GB after compression, and the fact table will be approximately 6 TB.
Which type of table should you use for each table? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
質問 # 37
You have an Azure Active Directory (Azure AD) tenant that contains a security group named Group1. You have an Azure Synapse Analytics dedicated SQL pool named dw1 that contains a schema named schema1.
You need to grant Group1 read-only permissions to all the tables and views in schema1. The solution must use the principle of least privilege.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.
正解：
解説：
Reference:
https://docs.microsoft.com/en-us/azure/data-share/how-to-share-from-sql
質問 # 38
You have an Azure data factory that has the Git repository settings shown in the following exhibit.
Use the drop-down menus to select the answer choose that completes each statement based on the information presented in the graphic.
NOTE: Each correct answer is worth one point.
正解：
解説：
Explanation
質問 # 39
You have the following table named Employees.
You need to calculate the employee_type value based on the hire_date value.
How should you complete the Transact-SQL statement? 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://docs.microsoft.com/en-us/sql/t-sql/language-elements/case-transact-sql
質問 # 40
You are designing an Azure Stream Analytics job to process incoming events from sensors in retail environments.
You need to process the events to produce a running average of shopper counts during the previous 15 minutes, calculated at five-minute intervals.
Which type of window should you use?
- A. hopping
- B. sliding
- C. snapshot
- D. tumbling
正解：D
解説：
Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time intervals. The following diagram illustrates a stream with a series of events and how they are mapped into 10-second tumbling windows.
Reference:
https://docs.microsoft.com/en-us/stream-analytics-query/tumbling-window-azure-stream-analytics
質問 # 41
You plan to create an Azure Data Lake Storage Gen2 account
You need to recommend a storage solution that meets the following requirements:
* Provides the highest degree of data resiliency
* Ensures that content remains available for writes if a primary data center fails What should you include in the recommendation? To answer, select the appropriate options in the answer area.
正解：
解説：
質問 # 42
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：
1 - Create an empty table named SalesFact_work that has the same schema as SalesFact.
2 - Switch the partition containing the stale data from SalesFact to SalesFact_Work.
3 - Drop the SalesFact_Work table.
Reference:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables-partition
質問 # 43
You have an Azure SQL database named Database1 and two Azure event hubs named HubA and HubB. The data consumed from each source is shown in the following table.
You need to implement Azure Stream Analytics to calculate the average fare per mile by driver.
How should you configure the Stream Analytics input for each source? 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/stream-analytics/stream-analytics-use-reference-data
質問 # 44
You are designing an Azure Synapse Analytics dedicated SQL pool.
Groups will have access to sensitive data in the pool as shown in the following table.
You have policies for the sensitive data. The policies vary be region as shown in the following table.
You have a table of patients for each region. The tables contain the following potentially sensitive columns.
You are designing dynamic data masking to maintain compliance.
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
Text Description automatically generated
Reference:
https://docs.microsoft.com/en-us/azure/azure-sql/database/dynamic-data-masking-overview
Topic 2, Contoso Case StudyTransactional Date
Contoso has three years of customer, transactional, operation, sourcing, and supplier data comprised of 10 billion records stored across multiple on-premises Microsoft SQL Server servers. The SQL server instances contain data from various operational systems. The data is loaded into the instances by using SQL server integration Services (SSIS) packages.
You estimate that combining all product sales transactions into a company-wide sales transactions dataset will result in a single table that contains 5 billion rows, with one row per transaction.
Most queries targeting the sales transactions data will be used to identify which products were sold in retail stores and which products were sold online during different time period. Sales transaction data that is older than three years will be removed monthly.
You plan to create a retail store table that will contain the address of each retail store. The table will be approximately 2 MB. Queries for retail store sales will include the retail store addresses.
You plan to create a promotional table that will contain a promotion ID. The promotion ID will be associated to a specific product. The product will be identified by a product ID. The table will be approximately 5 GB.
Streaming Twitter Data
The ecommerce department at Contoso develops and Azure logic app that captures trending Twitter feeds referencing the company's products and pushes the products to Azure Event Hubs.
Planned Changes
Contoso plans to implement the following changes:
* Load the sales transaction dataset to Azure Synapse Analytics.
* Integrate on-premises data stores with Azure Synapse Analytics by using SSIS packages.
* Use Azure Synapse Analytics to analyze Twitter feeds to assess customer sentiments about products.
Sales Transaction Dataset Requirements
Contoso identifies the following requirements for the sales transaction dataset:
* Partition data that contains sales transaction records. Partitions must be designed to provide efficient loads by month. Boundary values must belong: to the partition on the right.
* Ensure that queries joining and filtering sales transaction records based on product ID complete as quickly as possible.
* Implement a surrogate key to account for changes to the retail store addresses.
* Ensure that data storage costs and performance are predictable.
* Minimize how long it takes to remove old records.
Customer Sentiment Analytics Requirement
Contoso identifies the following requirements for customer sentiment analytics:
* Allow Contoso users to use PolyBase in an A/ure Synapse Analytics dedicated SQL pool to query the content of the data records that host the Twitter feeds. Data must be protected by using row-level security (RLS). The users must be authenticated by using their own A/ureAD credentials.
* Maximize the throughput of ingesting Twitter feeds from Event Hubs to Azure Storage without purchasing additional throughput or capacity units.
* Store Twitter feeds in Azure Storage by using Event Hubs Capture. The feeds will be converted into Parquet files.
* Ensure that the data store supports Azure AD-based access control down to the object level.
* Minimize administrative effort to maintain the Twitter feed data records.
* Purge Twitter feed data records;itftaitJ are older than two years.
Data Integration Requirements
Contoso identifies the following requirements for data integration:
Use an Azure service that leverages the existing SSIS packages to ingest on-premises data into datasets stored in a dedicated SQL pool of Azure Synaps Analytics and transform the data.
Identify a process to ensure that changes to the ingestion and transformation activities can be version controlled and developed independently by multiple data engineers.
質問 # 45
You need to design a data retention solution for the Twitter feed data records. The solution must meet the customer sentiment analytics requirements.
Which Azure Storage functionality should you include in the solution?
- A. soft delete
- B. change feed
- C. lifecycle management
- D. time-based retention
正解：A
解説：
Scenario: Purge Twitter feed data records that are older than two years.
Data sets have unique lifecycles. Early in the lifecycle, people access some data often. But the need for access often drops drastically as the data ages. Some data remains idle in the cloud and is rarely accessed once stored. Some data sets expire days or months after creation, while other data sets are actively read and modified throughout their lifetimes. Azure Storage lifecycle management offers a rule-based policy that you can use to transition blob data to the appropriate access tiers or to expire data at the end of the data lifecycle.
Reference:
https://docs.microsoft.com/en-us/azure/storage/blobs/lifecycle-management-overview This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.
To start the case study
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.
Overview
Litware, Inc. owns and operates 300 convenience stores across the US. The company sells a variety of packaged foods and drinks, as well as a variety of prepared foods, such as sandwiches and pizzas.
Litware has a loyalty club whereby members can get daily discounts on specific items by providing their membership number at checkout.
Litware employs business analysts who prefer to analyze data by using Microsoft Power BI, and data scientists who prefer analyzing data in Azure Databricks notebooks.
質問 # 46
You are building an Azure Data Factory solution to process data received from Azure Event Hubs, and then ingested into an Azure Data Lake Storage Gen2 container.
The data will be ingested every five minutes from devices into JSON files. The files have the following naming pattern.
/{deviceType}/in/{YYYY}/{MM}/{DD}/{HH}/{deviceID}_{YYYY}{MM}{DD}HH}{mm}.json You need to prepare the data for batch data processing so that there is one dataset per hour per deviceType.
The solution must minimize read times.
How should you configure the sink for the copy activity? 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: @trigger().startTime
startTime: A date-time value. For basic schedules, the value of the startTime property applies to the first occurrence. For complex schedules, the trigger starts no sooner than the specified startTime value.
Box 2: /{YYYY}/{MM}/{DD}/{HH}_{deviceType}.json
One dataset per hour per deviceType.
Box 3: Flatten hierarchy
- FlattenHierarchy: All files from the source folder are in the first level of the target folder. The target files have autogenerated names.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/concepts-pipeline-execution-triggers
https://docs.microsoft.com/en-us/azure/data-factory/connector-file-system
質問 # 47
You have an Azure subscription that contains the resources shown in the following table.
You need to ensure that you can Spark notebooks in ws1. The solution must ensure secrets from kv1 by using UAMI1. What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
質問 # 48
You have an Azure subscription.
You plan to build a data warehouse in an Azure Synapse Analytics dedicated SQL pool named pool1 that will contain staging tables and a dimensional model Pool1 will contain the following tables.
正解：
解説：
質問 # 49
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Microsoft DP-203は、Microsoft Azureプラットフォーム上でデータエンジニアになりたい個人向けに設計された試験です。この試験は、Azureサービスを使用してデータストレージソリューション、データ処理ソリューション、およびデータ監視ソリューションを設計および実装する能力を評価することを目的としています。この試験は、データエンジニア向けの人気のある認定試験の1つであり、Microsoft Azureデータエンジニアリングにおける個人の専門知識を測定する標準として、世界的に認められています。
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