You are designing a Fabric Data Pipeline that automates data ingestion using Dataflow Gen 2. The pipeline needs to:Extract customer transaction data from an external SQL database.Transform the data using Dataflow Gen 2.Load the transformed data into a Lakehouse table for downstream analytics.Ensure the process automatically reruns if any failures occur.After configuring the pipeline, you notice that the Dataflow Gen 2 step executes successfully, but the pipeline does not retry upon failure, causing data freshness issues.Which of the following changes would best ensure that the pipeline retries automatically on failure?

B- B) Configure a Retry Policy in the pipeline settings with an appropriate retry count and interval

You are designing a Dataflow Gen 2 in Microsoft Fabric to ingest and transform sales data from an Azure SQL Database before loading it into a Lakehouse table. The Dataflow needs to:Connect securely to the Azure SQL database.Transform sales records by filtering transactions from the last 12 months.Load the transformed data into the Lakehouse table with incremental refresh enabled.Optimize data refresh performance by minimizing unnecessary data loads.After configuring the dataflow, you notice that full data reloads occur during each refresh cycle, increasing processing time.Which of the following steps would best ensure that only new and updated records are processed during each refresh?

B- B) Configure an Incremental Refresh Policy in the Dataflow settings to only process new and changed records.

You are designing a Fabric Data Pipeline to orchestrate the movement of customer order data from an Azure Data Lake Storage (ADLS Gen2) container to a Fabric Lakehouse table. The pipeline consists of the following steps:Ingest data from a CSV file stored in ADLS Gen2.Transform the data using Dataflow Gen 2, applying schema validation and removing duplicate records.Load the transformed data into a Lakehouse table.Trigger the pipeline execution automatically whenever a new file arrives in the ADLS container.After deploying the pipeline, you observe that new files are uploaded to ADLS, but the pipeline does not trigger automatically.Which of the following modifications would best resolve this issue?

B- B) Configure an Event-based trigger in the pipeline settings that listens to the ADLS Gen2 storage event for file creation.

You are a data engineer at Contoso Ltd., responsible for setting up an ingestion pipeline in Microsoft Fabric. The company wants to automate the data ingestion process using Data Factory pipelines.You need to ensure that data is ingested into the bronze layer of a Fabric lakehouse using a no-code approach while also enabling source control for the pipeline. The ingestion must support Delta format, and if a failure occurs, the pipeline must retry the operation automatically.What should you do?

B- B. Create a Data Pipeline with a built-in Copy activity, store the pipeline in Azure Repos, and configure retries.

You are working on a data processing pipeline using Apache Spark and have a large dataset stored in a distributed file system. Your task involves performing a series of transformations and actions to clean, aggregate, and analyze the data. Considering the need for efficient execution, fault tolerance, and the ability to handle iterative algorithms, Which of the following approaches would be most appropriate?

A- A) Use DataFrames and perform transformations using the DataFrame API, leveraging Catalyst optimizer for query optimization and Tungsten for efficient code generation.

You are developing a Spark-based data pipeline that processes a large dataset stored in a Delta Lake table. The pipeline must handle incremental updates efficiently, ensure ACID transactions, and allow schema evolution without manual intervention. Additionally, certain records need to be updated based on business rules before being written back to the Delta table. Considering performance, reliability, and maintainability, which approach is most appropriate for handling updates in Spark?

B- B) Load the dataset as a DataFrame, use Delta Lake’s MERGE INTO operation to update existing records efficiently, and enable autoMerge to handle schema changes.

You are tasked with processing a large dataset containing user activity logs stored in a distributed file system. Each log entry includes a timestamp, user ID, action type, and additional metadata. To analyze user behavior over time, you need to calculate the total number of distinct actions performed by each user per day. Considering performance optimization and efficient resource utilization in Spark, which approach would be most appropriate?

B- B) Load the data directly into a DataFrame, use the groupBy method with user_id and date columns, and apply the countDistinct function on the action_type column to get the desired aggregation.

You have a Spark SQL table named `employees` with columns: `employee_id`, `first_name`, `last_name`, `department`, and `salary`. To improve query performance, you decide to create a persistent view that shows only employees from the 'Sales' department with salaries above $50,000. Which of the following commands would you use to create this view?

A- A)CREATE VIEW sales_high_earners ASSELECT employee_id, first_name, last_name, salaryFROM employeesWHERE department = 'Sales' AND salary > 50000;

In a Microsoft Fabric environment, an organization wants to enforce strict security controls so that only specific users can access datasets based on their assigned roles. The organization uses multiple workspaces and requires role-based access to govern data visibility efficiently. Which of the following security mechanisms should be implemented to achieve this while maintaining scalability across different teams?

A- A) Assigning permissions at the workspace level and enabling row-level security (RLS) on datasets.

A multinational corporation is implementing a data access strategy in Microsoft Fabric to ensure that employees can access only the data relevant to their roles. The IT team needs to configure security at a granular level to prevent unauthorized access to certain tables and columns while allowing appropriate users to query the data efficiently. They must also ensure that these security policies scale across multiple workspaces and datasets. Which of the following approaches best aligns with their requirements?

B- B) Implementing Object-Level Security (OLS) to hide specific tables and columns from unauthorized users

An enterprise wants to ensure that data access policies in Microsoft Fabric are enforced consistently while minimizing administrative overhead. They need a scalable approach to implement fine-grained security controls across datasets and workspaces, ensuring that employees see only the relevant data based on their roles. Additionally, they want to leverage security groups and minimize the number of individual permissions assignments to maintain manageability. Which method should they prioritize?

A- A) Defining Row-Level Security (RLS) rules based on Microsoft Entra ID (Azure AD) security groups.

A data security administrator is tasked with securing access to a Fabric Lakehouse table containing sensitive customer records. The administrator must ensure that only employees in the “Finance” security group can view the Revenue column while all other users, including “Sales” and “Support” teams, should be restricted from accessing this column. Additionally, all users must be able to access the rest of the table for reporting purposes. What is the best way to achieve this requirement in Microsoft Fabric?

B- B) Implement Object-Level Security (OLS) to restrict access to the Revenue column for unauthorized users

A data engineering team is setting up a CI/CD pipeline in Microsoft Fabric to automate deployment across multiple environments. They want to ensure that changes made in the development workspace are validated and automatically deployed to the test and production environments. Additionally, they need to maintain version control and rollback capabilities. Which approach should they follow to achieve a structured and automated deployment process?

B- B) Use Git integration with Microsoft Fabric to manage source control and configure deployment pipelines.

Microsoft Certified: Fabric Data Engineer Associate (DP-700) Exam Questions

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2026

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Preparing for the Microsoft Certified: Fabric Data Engineer Associate (DP-700) certification can be a critical step in advancing your career as a data engineering professional. This practice exam offers you the opportunity to engage with 133 real questions that mirror the structure and content of the actual certification test. By taking this practice exam, you gain invaluable exposure to the kinds of questions you can expect on the day of the certification, allowing you to familiarize yourself with the exam's format and focus areas.

Additionally, this practice exam serves as a self-assessment tool, enabling you to gauge your readiness for the certification. You can identify your strengths and weaknesses across various topics, allowing you to tailor your study efforts more effectively. Whether you’re looking to reinforce your knowledge or pinpoint areas that need further review, this practice exam is designed to help you develop a clear and actionable study plan. Taking the time to work through these questions will enhance your confidence and improve your performance on the actual exam.

Per page:

Welcome to the questions-and-answers listing page for the Microsoft Certified: Fabric Data Engineer Associate (DP-700) exam. Here, you will find a comprehensive collection of 133 real practice questions designed to help you prepare effectively for the certification exam. Each question is an opportunity to familiarize yourself with the types of topics and challenges you may encounter, enhancing both your knowledge and confidence as you approach the test.

To make the most of this resource, read through each question carefully and try to answer it without looking at the provided answers initially. This will help you assess your understanding and identify areas where you may need further study. Afterward, review the correct answers and explanations to deepen your comprehension and remediate any gaps in your knowledge.

As you prepare for the DP-700 exam, consider these study tips:

1. **Create a Study Schedule**: Break down your study materials into manageable sections and allocate specific times for review. This structured approach can help prevent last-minute cramming and ensure you cover all necessary topics.

2. **Utilize a Variety of Resources**: Mix your study methods by incorporating online courses, textbooks, hands-on labs, and community forums. Different formats can provide unique insights and reinforce your understanding.

3. **Practice Exams**: Regularly take full-length practice exams under timed conditions. This will not only help you become familiar with the exam format but also improve your time management skills during the actual test.

By integrating these strategies and leveraging the questions on this page, you'll be better positioned to pass the Microsoft Certified: Fabric Data Engineer Associate (DP-700) exam. Good luck!

Question 1 Single Choice

You are working on a Microsoft Fabric project where you need to create a Dataflow Gen 2 that ingests data from an external SQL database and applies transformation steps before loading it into a Lakehouse table. During testing, you notice that the ingestion pipeline runs successfully, but the transformed data is not appearing in the destination table.

Upon investigation, you realize that the dataflow execution completes without errors, but the output mode configuration might be the reason for missing data.

Which of the following configurations would best ensure that transformed data is loaded into the Lakehouse table correctly?

Question 2 Single Choice

You are designing a Fabric Data Pipeline that automates data ingestion using Dataflow Gen 2. The pipeline needs to:

Extract customer transaction data from an external SQL database.
Transform the data using Dataflow Gen 2.
Load the transformed data into a Lakehouse table for downstream analytics.
Ensure the process automatically reruns if any failures occur.

After configuring the pipeline, you notice that the Dataflow Gen 2 step executes successfully, but the pipeline does not retry upon failure, causing data freshness issues.

Which of the following changes would best ensure that the pipeline retries automatically on failure?

Question 3 Multiple Choice

You have a KQL database with a table TransformedData that records bike station information, including the number of available bikes (No_Bikes), station identifiers (BikepointID), and timestamps (Timestamp). You aim to create a materialized view that always provides the most recent number of bikes at each station. Which of the following KQL commands would achieve this? (Select all that apply)

Question 4 Multiple Choice

You are tasked with creating a Real-Time Dashboard in Microsoft Fabric to monitor live data from a KQL database. Which of the following steps are necessary to successfully create and visualize data on this dashboard? (Select all that apply)

Options:

Question 5 Single Choice

You are designing a Dataflow Gen 2 in Microsoft Fabric to ingest and transform sales data from an Azure SQL Database before loading it into a Lakehouse table. The Dataflow needs to:

Connect securely to the Azure SQL database.
Transform sales records by filtering transactions from the last 12 months.
Load the transformed data into the Lakehouse table with incremental refresh enabled.
Optimize data refresh performance by minimizing unnecessary data loads.

After configuring the dataflow, you notice that full data reloads occur during each refresh cycle, increasing processing time.

Which of the following steps would best ensure that only new and updated records are processed during each refresh?

Question 6 Single Choice

You are designing a Fabric Data Pipeline to orchestrate the movement of customer order data from an Azure Data Lake Storage (ADLS Gen2) container to a Fabric Lakehouse table. The pipeline consists of the following steps:

Ingest data from a CSV file stored in ADLS Gen2.
Transform the data using Dataflow Gen 2, applying schema validation and removing duplicate records.
Load the transformed data into a Lakehouse table.
Trigger the pipeline execution automatically whenever a new file arrives in the ADLS container.

After deploying the pipeline, you observe that new files are uploaded to ADLS, but the pipeline does not trigger automatically.

Which of the following modifications would best resolve this issue?

Question 7 Single Choice

You are tasked with setting up a Fabric Data Pipeline that automatically ingests sales transaction data from an Azure Data Lake Storage Gen2 (ADLS Gen2) container into a Microsoft Fabric Lakehouse.

The pipeline should:

Trigger automatically when a new sales data file arrives in ADLS Gen2.
Process and clean the data using Dataflow Gen 2 (removing duplicates, schema validation).
Load the transformed data into a Lakehouse table for reporting.

Which of the following is the correct sequence of steps to configure this pipeline?

A-

1️⃣ Create an event-based trigger on ADLS Gen2.
2️⃣ Build a Dataflow Gen 2 transformation.
3️⃣ Add a Copy Data activity to move data to staging.
4️⃣ Configure the Lakehouse table load activity.

B-

1️⃣ Create a Fabric Data Pipeline and define activities (Copy Data, Dataflow Gen 2, and Lakehouse Load).
2️⃣ Configure an event-based trigger on ADLS Gen2.
3️⃣ Activate the trigger and test by uploading a new file.
4️⃣ Validate that transformed data appears in the Lakehouse.

C-

1️⃣ Upload a new file to ADLS Gen2.
2️⃣ Define a Copy Data activity in Fabric Data Pipeline.
3️⃣ Build a Dataflow Gen 2 transformation.
4️⃣ Set up an event-based trigger to automate pipeline execution.

D-

1️⃣ Define a Copy Data activity in Fabric Data Pipeline.
2️⃣ Build a Dataflow Gen 2 transformation.
3️⃣ Configure an event-based trigger on ADLS Gen2.
4️⃣ Load the data into the Lakehouse table.

Question 8 Single Choice

You are using Copy Data activity in Fabric Data Pipelines to move data from an Azure SQL Database to a Lakehouse table. The pipeline should:

1️⃣ Automatically detect schema changes in the source.
2️⃣ Ensure new columns are included without breaking existing workflows.
3️⃣ Maintain high performance for large datasets.

After deploying the pipeline, you notice that when new columns are added in the source, the pipeline fails instead of dynamically adjusting to the changes.

What is the best way to handle this issue?

Question 9 Single Choice

You are designing a Fabric Data Pipeline to automate the ingestion and transformation of customer order data from an Azure SQL Database into a Fabric Lakehouse.

Your company requires the pipeline to:

1️⃣ Standardize pipeline creation across multiple teams by using pre-built templates.
2️⃣ Ensure consistency in data processing steps such as data ingestion, transformation, and loading.
3️⃣ Reduce development time by leveraging reusable pipeline components.
4️⃣ Enable customization where necessary to adjust for different data sources.

After setting up the pipeline using a pre-built template, you notice that the transformation logic does not match your specific business requirements.

Which of the following actions would best allow you to modify the template while still leveraging its benefits?

Question 10 Single Choice

You are responsible for monitoring and troubleshooting a Fabric Data Pipeline that loads customer transactions from an Azure Blob Storage container into a Fabric Warehouse.

Your team has reported that some pipeline runs complete with warnings, but not all data appears in the Warehouse table.

To diagnose the issue, you must:
1️⃣ Identify failed or partially successful runs using Fabric's monitoring tools.
2️⃣ Pinpoint the exact step where issues occurred and review logs.
3️⃣ Ensure pipeline execution continues even if minor errors occur.

Which of the following steps should you take first to investigate the issue?

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Frequently Asked Questions

How realistic are the practice questions for the DP-700 exam?

The practice questions closely mimic the style and difficulty of the actual DP-700 exam, providing a valuable opportunity to familiarize yourself with the types of questions you will encounter.

How should I use the practice questions to prepare for the DP-700 exam?

Incorporate the practice questions into your study routine by taking timed quizzes and reviewing explanations for both correct and incorrect answers to solidify your understanding of the material.

How many practice questions should I complete before the real DP-700 exam?

While there's no exact number, aim to complete a significant portion of the practice questions, focusing on areas where you feel less confident, to ensure comprehensive readiness before the real exam.

Can I retake the practice questions multiple times?

Yes, you can retake the practice questions as many times as you like, which can help reinforce learning and track improvement over time.

Are there any recommended study strategies while using these practice questions?

Consider using a mix of study strategies, such as spaced repetition, taking notes on challenging concepts, and discussing questions with peers to enhance your understanding and retention.

Microsoft Certified: Fabric Data Engineer Associate (DP-700) Exam Questions

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