Microsoft DP-700 Exam Practice Questions, practice question pack
MICROSOFT · DP-700

Microsoft DP-700 Exam Practice Questions

139 questionsPDF by emailUpdated September 2026

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Three of the 139 questions in this pack

Question 1

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. Company Overview Contoso, Ltd. is an online retail company that wants to modernize its analytics platform by moving to Fabric. The company plans to begin using Fabric for marketing analytics. Overview. IT Structure The company’s IT department has a team of data analysts and a team of data engineers that use analytics systems. The data engineers perform the ingestion, transformation, and loading of data. They prefer to use Python or SQL to transform the data. The data analysts query data and create semantic models and reports. They are qualified to write queries in Power Query and T-SQL. Existing Environment. Fabric Contoso has an F64 capacity named Cap1. All Fabric users are allowed to create items. Contoso has two workspaces named WorkspaceA and WorkspaceB that currently use Pro license mode. Existing Environment. Source Systems Contoso has a point of sale (POS) system named POS1 that uses an instance of SQL Server on Azure Virtual Machines in the same Microsoft Entra tenant as Fabric. The host virtual machine is on a private virtual network that has public access blocked. POS1 contains all the sales transactions that were processed on the company’s website. The company has a software as a service (SaaS) online marketing app named MAR1. MAR1 has seven entities. The entities contain data that relates to email open rates and interaction rates, as well as website interactions. The data can be exported from MAR1 by calling REST APIs. Each entity has a different endpoint. Contoso has been using MAR1 for one year. Data from prior years is stored in Parquet files in an Amazon Simple Storage Service (Amazon S3) bucket. There are 12 files that range in size from 300 MB to 900 MB and relate to email interactions. Existing Environment. Product Data POS1 contains a product list and related data. The data comes from the following three tables: • Products • ProductCategories • ProductSubcategories In the data, products are related to product subcategories, and subcategories are related to product categories. Existing Environment. Azure Contoso has a Microsoft Entra tenant that has the following mail-enabled security groups: DataAnalysts: Contains the data analysts DataEngineers: Contains the data engineers Contoso has an Azure subscription. The company has an existing Azure DevOps organization and creates a new project for repositories that relate to Fabric. Existing Environment. User Problems The VP of marketing at Contoso requires analysis on the effectiveness of different types of email content. It typically takes a week to manually compile and analyze the data. Contoso wants to reduce the time to less than one day by using Fabric. The data engineering team has successfully exported data from MAR1. The team experiences transient connectivity errors, which causes the data exports to fail. Requirements. Planned Changes Contoso plans to create the following two lakehouses: Lakehouse1: Will store both raw and cleansed data from the sources Lakehouse2: Will serve data in a dimensional model to users for analytical queries Additional items will be added to facilitate data ingestion and transformation. Contoso plans to use Azure Repos for source control in Fabric. Requirements. Technical Requirements The new lakehouses must follow a medallion architecture by using the following three layers: bronze, silver, and gold. There will be extensive data cleansing required to populate the MAR1 data in the silver layer, including deduplication, the handling of missing values, and the standardizing of capitalization. Each layer must be fully populated before moving on to the next layer. If any step in populating the lakehouses fails, an email must be sent to the data engineers. Data imports must run simultaneously, when possible. The use of email data from the Amazon S3 bucket must meet the following requirements: Minimize egress costs associated with cross-cloud data access. Prevent saving a copy of the raw data in the lakehouses. Items that relate to data ingestion must meet the following requirements: The items must be source controlled alongside other workspace items. Ingested data must land in the bronze layer of Lakehouse1 in the Delta format. No changes other than changes to the file formats must be implemented before the data lands in the bronze layer. Development effort must be minimized and a built-in connection must be used to import the source data. In the event of a connectivity error, the ingestion processes must attempt the connection again. Lakehouses, data pipelines, and notebooks must be stored in WorkspaceA. Semantic models, reports, and dataflows must be stored in WorkspaceB. Once a week, old files that are no longer referenced by a Delta table log must be removed. Requirements. Data Transformation In the POS1 product data, ProductID values are unique. The product dimension in the gold layer must include only active products from product list. Active products are identified by an IsActive value of 1. Some product categories and subcategories are NOT assigned to any product. They are NOT analytically relevant and must be omitted from the product dimension in the gold layer. Requirements. Data Security Security in Fabric must meet the following requirements: The data engineers must have read and write access to all the lakehouses, including the underlying files. The data analysts must only have read access to the Delta tables in the gold layer. The data analysts must NOT have access to the data in the bronze and silver layers. The data engineers must be able to commit changes to source control in WorkspaceA. You need to ensure that the data analysts can access the gold layer lakehouse. What should you do?

  1. Add the DataAnalyst group to the Viewer role for WorkspaceA.
  2. Share the lakehouse with the DataAnalysts group and grant the Build reports on the default semantic model permission.
  3. Share the lakehouse with the DataAnalysts group and grant the Read all SQL Endpoint data permission.
  4. Share the lakehouse with the DataAnalysts group and grant the Read all Apache Spark permission.
Show answer and explanation

Correct answer: C. Share the lakehouse with the DataAnalysts group and grant the Read all SQL Endpoint data permission.

grant the Read all SQL Endpoint data permission. The data analysts need read-only access to the Delta tables in the gold layer and nothing else. Sharing the lakehouse and granting the Read all SQL Endpoint data permission lets them query the gold tables through the SQL analytics endpoint without any access to the underlying OneLake files. Because all lakehouses live in WorkspaceA, item level sharing is the only way to expose the gold lakehouse without exposing the bronze and silver data in Lakehouse1.

Why the other options are wrong

  • A. Adding DataAnalysts to the Viewer role for WorkspaceA grants access to every item in that workspace, including Lakehouse1 with its bronze and silver data.
  • B. Build reports on the default semantic model only enables report creation on that model, it does not provide the read access to the gold Delta tables that the analysts require.
  • D. Read all Apache Spark grants access to the lakehouse files and folders through Spark, which goes beyond the read-only Delta table access the analysts are allowed.

Question 2

You have a Fabric workspace. You have semi-structured data. You need to read the data by using T-SQL, KQL, and Apache Spark. The data will only be written by using Spark. What should you use to store the data?

  1. a lakehouse
  2. an eventhouse
  3. a datamart
  4. a warehouse
Show answer and explanation

Correct answer: A. a lakehouse

A lakehouse stores the data as Delta tables in OneLake, which Spark writes to directly. The same tables are readable with T-SQL through the SQL analytics endpoint, and they can be surfaced for KQL queries by creating a OneLake shortcut from a KQL database in an eventhouse. That combination of one Spark write path and three read engines matches the requirement.

Why the other options are wrong

  • B. An eventhouse is built for high volume streaming and telemetry ingestion queried with KQL, so it is not the store for Spark-written semi-structured data.
  • C. A datamart is a Power Query driven SQL store for structured tabular data and offers no Spark write path.
  • D. A warehouse is written and queried with T-SQL, and Spark can only read from it, not write to it.

Question 3

You have a Fabric workspace that contains a warehouse named Warehouse1. You have an on-premises Microsoft SQL Server database named Database1 that is accessed by using an on-premises data gateway. You need to copy data from Database1 to Warehouse1. Which item should you use?

  1. a Dataflow Gen1 dataflow
  2. a data pipeline
  3. a KQL queryset
  4. a notebook
Show answer and explanation

Correct answer: B. a data pipeline

A data pipeline Copy activity supports connections that use an on-premises data gateway and can write directly into a Fabric warehouse. It is the built-in, low code way to move the Database1 tables into Warehouse1 and can be scheduled and monitored.

Why the other options are wrong

  • A. A Dataflow Gen1 dataflow can only output to Power BI dataflow storage, so it cannot load data into a Fabric warehouse at all.
  • C. A KQL queryset only runs queries against KQL databases and cannot copy data from SQL Server.
  • D. A notebook runs on Spark compute that cannot use an on-premises data gateway to reach Database1.

See all 10 free questions Get the full pack, US$39

139 practice questions for Microsoft Fabric Data Engineer (DP-700), with full explanations.

Every question comes with the correct answer, the reasoning behind it, and a short note on why each wrong option is wrong. Work through it once with the answers, then again with the questions-only copy under exam conditions.

  • 139 questions mapped to the DP-700 exam objectives
  • Answers and explanations for every question, including the wrong options
  • A questions-only PDF for timed practice runs
  • Instant delivery by email the moment you check out
  • Free monthly updates for as long as the exam is live
  • Pass or your money back

A DP-700 attempt costs US$165 in the US. This pack is US$39, paid once.

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Last updated September 2026 · 139 questions

What makes the DP-700 hard

The DP-700 tests three languages, and most candidates only work in one. It does not just ask whether you know what a lakehouse is, it requires choosing between a lakehouse, a warehouse and an eventhouse, then writing the SQL, KQL or PySpark to match.

The exam covers ingestion pattern selection, medallion architecture, dataflows versus pipelines versus notebooks, Delta table optimisation, real-time eventstreams, workspace security and deployment pipelines: the decisions a working Fabric data engineer makes every day. Candidates strong in one Fabric workload but untested in the others consistently underperform, because Microsoft tests which tool you would pick and the code you would write in it, not definitions.

It runs to 40 to 60 questions in 100 minutes, with a pass mark of 700 out of 1000, across three domains weighted almost evenly, so the SQL, KQL and PySpark switching, the store-selection trade-offs and the case study format all need to be familiar before exam day.

About the exam

The DP-700 certifies data engineers who build and manage analytics solutions in Microsoft Fabric, covering data ingestion, transformation and administration using lakehouses, warehouses, eventhouses, notebooks and pipelines with SQL, KQL and PySpark. No formal prerequisites. Renews annually via a free Microsoft Learn assessment.

Exam domains

  • Implement and manage an analytics solution: 30 to 35%
  • Ingest and transform data: 30 to 35%
  • Monitor and optimise an analytics solution: 30 to 35%

40 to 60 questions, 100 minutes, passing score 700 out of 1000, US$165 in the US, priced by local currency elsewhere, online proctored and test centres, renews annually.

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Questions before you buy

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139 practice questions as a PDF, each with the correct answer, a full explanation and a note on why the other options are wrong, plus a separate questions-only PDF for timed practice.

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