【2026年最新】DP-203: Data Engineering on Microsoft Azure（考场真题)_刷熟必过

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You need to schedule an Azure Data Factory pipeline to execute when a new file arrives in an Azure Data Lake Storage Gen2 container.

Which type of trigger should you use?

Introductory Info
Case study -
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 -
Contoso, Ltd. is a clothing retailer based in Seattle. The company has 2,000 retail stores across the United States and an emerging online presence.
The network contains an Active Directory forest named contoso.com. The forest it integrated with an Azure Active Directory (Azure AD) tenant named contoso.com. Contoso has an Azure subscription associated to the contoso.com Azure AD tenant.
Existing Environment -
Transactional Data -
Contoso has three years of customer, transactional, operational, 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 periods. 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 an Azure logic app that captures trending Twitter feeds referencing the company's products and pushes the products to Azure Event Hubs.
Planned Changes and Requirements
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 Requirements
Contoso identifies the following requirements for customer sentiment analytics:
Allow Contoso users to use PolyBase in an Azure 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 Azure AD 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 that 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 Synapse
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.
Question
You need to implement the surrogate key for the retail store table. The solution must meet the sales transaction dataset requirements.
What should you create?

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are designing an Azure Stream Analytics solution that will analyze Twitter data.
You need to count the tweets in each 10-second window. The solution must ensure that each tweet is counted only once.
Solution: You use a hopping window that uses a hop size of 5 seconds and a window size 10 seconds.
Does this meet the goal?

You need to trigger an Azure Data Factory pipeline when a file arrives in an Azure Data Lake Storage Gen2 container.
Which resource provider should you enable?

You are designing a star schema for a dataset that contains records of online orders. Each record includes an order date, an order due date, and an order ship date.

You need to ensure that the design provides the fastest query times of the records when querying for arbitrary date ranges and aggregating by fiscal calendar attributes.

Which two actions should you perform? 

NOTE: Each correct selection is worth one point.

You plan to create an Azure Synapse Analytics dedicated SQL pool.
You need to minimize the time it takes to identify queries that return confidential information as defined by the company's data privacy regulations and the users who executed the queues.
Which two components should you include in the solution? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

You are designing a streaming data solution that will ingest variable volumes of data.
You need to ensure that you can change the partition count after creation.
Which service should you use to ingest the data?

You have an Azure Synapse Analytics dedicated SQL pool named Pool1 and a database named DB1. DB1 contains a fact table named Table1.
You need to identify the extent of the data skew in Table1.
What should you do in Synapse Studio?

You have an Azure Data Factory version 2 (V2) resource named Df1. Df1 contains a linked service.
You have an Azure Key vault named vault1 that contains an encryption key named key1.
You need to encrypt Df1 by using key1.
What should you do first?

You plan to create an Azure Databricks workspace that has a tiered structure. The workspace will contain the following three workloads:
✑ A workload for data engineers who will use Python and SQL.
✑ A workload for jobs that will run notebooks that use Python, Scala, and SQL.
✑ A workload that data scientists will use to perform ad hoc analysis in Scala and R.
The enterprise architecture team at your company identifies the following standards for Databricks environments:
✑ The data engineers must share a cluster.
✑ The job cluster will be managed by using a request process whereby data scientists and data engineers provide packaged notebooks for deployment to the cluster.
✑ All the data scientists must be assigned their own cluster that terminates automatically after 120 minutes of inactivity. Currently, there are three data scientists.
You need to create the Databricks clusters for the workloads.
Solution: You create a High Concurrency cluster for each data scientist, a High Concurrency cluster for the data engineers, and a Standard cluster for the jobs.
Does this meet the goal?

You have an Azure data factory.
You need to examine the pipeline failures from the last 60 days.
What should you use?

You are designing an Azure Stream Analytics solution that will analyze Twitter data.
You need to count the tweets in each 10-second window. The solution must ensure that each tweet is counted only once.
Solution: You use a hopping window that uses a hop size of 10 seconds and a window size of 10 seconds.
Does this meet the goal?

You have two Azure Data Factory instances named ADFdev and ADFprod. ADFdev connects to an Azure DevOps Git repository.
You publish changes from the main branch of the Git repository to ADFdev.
You need to deploy the artifacts from ADFdev to ADFprod.
What should you do first?

You are monitoring an Azure Stream Analytics job.
You discover that the Backlogged Input Events metric is increasing slowly and is consistently non-zero.
You need to ensure that the job can handle all the events.
What should you do?

You are designing an inventory updates table in an Azure Synapse Analytics dedicated SQL pool. The table will have a clustered columnstore index and will include the following columns:
（含图）You identify the following usage patterns:
✑ Analysts will most commonly analyze transactions for a warehouse.
✑ Queries will summarize by product category type, date, and/or inventory event type.
You need to recommend a partition strategy for the table to minimize query times.
On which column should you partition the table?

You have files and folders in Azure Data Lake Storage Gen2 for an Azure Synapse workspace as shown in the following exhibit.
（含图）
You create an external table named ExtTable that has LOCATION='/topfolder/'.
When you query ExtTable by using an Azure Synapse Analytics serverless SQL pool, which files are returned?

You need to implement a Type 3 slowly changing dimension (SCD) for product category data in an Azure Synapse Analytics dedicated SQL pool.
You have a table that was created by using the following Transact-SQL statement.
（含图）
Which two columns should you add to the table? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

You are developing a solution that will stream to Azure Stream Analytics. The solution will have both streaming data and reference data.
Which input type should you use for the reference data?

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are designing an Azure Stream Analytics solution that will analyze Twitter data.
You need to count the tweets in each 10-second window. The solution must ensure that each tweet is counted only once.
Solution: You use a session window that uses a timeout size of 10 seconds.
Does this meet the goal?

You have an Azure Synapse Analytics dedicated SQL pool that contains a large fact table. The table contains 50 columns and 5 billion rows and is a heap.

Most queries against the table aggregate values from approximately 100 million rows and return only two columns.

You discover that the queries against the fact table are very slow.

Which type of index should you add to provide the fastest query times?