Free DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric (beta) Exam Questions and Answers
80 verified practice questions for DP-700.
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Last updated: September 19, 2026
- Exam code
- DP-700
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Question #1
You need to populate the MAR1 data in the bronze layer. Which two types of activities should you include in the pipeline? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
Select 2 answers.
Please select an optionIncorrectCorrect answer: A, B
MAR1 has seven entities, each accessible via a different API endpoint. A ForEach activity is required to iterate over these endpoints to fetch data from each one. It enables dynamic execution of API calls for each entity. The Copy data activity is the primary mechanism to extract data from REST APIs and load it into the bronze layer in Delta format. It supports native connectors for REST APIs and Delta, minimizing development effort. You need to schedule the population of the medallion layers to meet the technical requirements. What should you do? * A. Schedule a data pipeline that calls other data pipelines. * B. Schedule a notebook. * C. Schedule an Apache Spark job. * D. Schedule multiple data pipelines. * Answer: A The technical requirements specify that: Medallion layers must be fully populated sequentially (bronze silver gold). Each layer must be populated before the next. If any step fails, the process must notify the data engineers. Data imports should run simultaneously when possible. Why Use a Data Pipeline That Calls Other Data Pipelines? A data pipeline provides a modular and reusable approach to orchestrating the sequential population of medallion layers. By calling other pipelines, each pipeline can focus on populating a specific layer (bronze, silver, or gold), simplifying development and maintenance. A parent pipeline can handle: - Sequential execution of child pipelines. - Error handling to send email notifications upon failures. - Parallel execution of tasks where possible (e.g., simultaneous imports into the bronze layer).
Was this answer correct?Question #2
You have a Fabric workspace that contains a warehouse named Warehouse1. While monitoring Warehouse1, you discover that query performance has degraded during the last 60 minutes. You need to isolate all the queries that were run during the last 60 minutes. The results must include the username of the users that submitted the queries and the query statements. What should you use?
Please select an optionIncorrectCorrect answer: B
The queryinsights views, such as exec_requests_history, list every submitted statement with the submitting user and the query text for the chosen period.
Was this answer correct?Question #3
You have a Fabric warehouse named DW1 that loads data by using a data pipeline named Pipeline1. Pipeline1 uses a Copy data activity with a dynamic SQL source. Pipeline1 is scheduled to run every 15 minutes. You discover that Pipeline1 keeps failing. You need to identify which SQL query was executed when the pipeline failed. What should you do?
Please select an optionIncorrectCorrect answer: B
The input JSON contains the configuration details and parameters passed to the Copy data activity during execution, including the dynamically generated SQL query. Viewing the input JSON for the failed pipeline run provides direct insight into what query was executed at the time of failure.
Was this answer correct?Question #4
You have a Fabric workspace that contains a lakehouse named Lakehouse1. Data is ingested into Lakehouse1 as one flat table. The table contains the following columns. You plan to load the data into a dimensional model and implement a star schema. From the original flat table, you create two tables named FactSales and DimProduct. You will track changes in DimProduct. You need to prepare the data. Which three columns should you include in the DimProduct table? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
Select 3 answers.
Please select an optionIncorrectCorrect answer: B, C, F
In a star schema, the DimProduct table serves as a dimension table that contains descriptive attributes about products. It will provide context for the FactSales table, which contains transactional data. The following columns should be included in the DimProduct table: • ProductName: The ProductName is an important descriptive attribute of the product, which is needed for analysis and reporting in a dimensional model. • ProductColor: ProductColor is another descriptive attribute of the product. In a star schema, it makes sense to include attributes like color in the dimension table to help categorize products in the analysis. • ProductID: ProductID is the primary key for the DimProduct table, which will be used to join the FactSales table to the product dimension. It's essential for uniquely identifying each product in the model.
Was this answer correct?Question #5
You have three users named User1, User2, and User3. You have the Fabric workspaces shown in the following table. You have a security group named Group1 that contains User1 and User3. The Fabric admin creates the domains shown in the following table. User1 creates a new workspace named Workspace3. You add Group1 to the default domain of Domain1. For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Answer Yes or No for each statement in the exhibit.
Please select an optionIncorrectCorrect answer: Yes; No; No
Question #6
You have a Fabric workspace that contains a warehouse named Warehouse1. Data is loaded daily into Warehouse1 by using data pipelines and stored procedures. You discover that the daily data load takes longer than expected. You need to monitor Warehouse1 to identify the names of users that are actively running queries. Which view should you use?
Please select an optionIncorrectCorrect answer: E
sys.dm_exec_sessions provides real-time information about all active sessions, including the user, session ID, and status of the session. You can filter on session status to see users actively running queries.
Was this answer correct?Question #7
You have a Fabric workspace that contains a lakehouse named Lakehouse1. In an external data source, you have data files that are 500 GB each. A new file is added every day. You need to ingest the data into Lakehouse1 without applying any transformations. The solution must meet the following requirements Trigger the process when a new file is added. Provide the highest throughput. Which type of item should you use to ingest the data?
Please select an optionIncorrectCorrect answer: D
To ingest large files (500 GB each) from an external data source into Lakehouse1 with high throughput and to trigger the process when a new file is added, an Eventstream is the best solution. An Eventstream in Fabric is designed for handling real-time data streams and can efficiently ingest large files as soon as they are added to an external source. It is optimized for high throughput and can be configured to trigger upon detecting new files, allowing for fast and continuous ingestion of data with minimal delay.
Was this answer correct?Question #8
You have a Fabric workspace that contains a warehouse named Warehouse1. Warehouse! contains a table named Customer. Customer contains the following data. You have an internal Microsoft Entra user named User1 that has an email address of user1@contoso.com. You need to provide User1 with access to the Customer table. The solution must prevent User1 from accessing the CreditCard column. How should you complete the statement? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Match each item to its target.
Please select an optionIncorrectCorrect answer: GRANT = SELECT; TO = [user1@contoso.com]
Question #9
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?
Please select an optionIncorrectCorrect answer: A
A lakehouse is the best option for storing semi-structured data when you need to read it using T-SQL, KQL, and Apache Spark. A lakehouse combines the flexibility of a data lake (which can handle semi-structured and unstructured data) with the performance features of a data warehouse. It allows data to be written using Apache Spark and can be queried using different technologies such as T-SQL (for SQL-based querying), KQL (Kusto Query Language for querying), and Apache Spark (for distributed processing). This solution is ideal when dealing with semi-structured data and requiring a versatile querying approach.
Was this answer correct?Question #10
You are implementing a medallion architecture in a Fabric lakehouse. You plan to create a dimension table that will contain the following columns: • ID • CustomerCode • CustomerName • CustomerAddress • CustomerLocation • ValidFrom • ValidTo You need to ensure that the table supports the analysis of historical sales data by customer location at the time of each sale Which type of slowly changing dimension (SCD) should you use?
Please select an optionIncorrectCorrect answer: A
A Type 2 dimension keeps a new row with ValidFrom and ValidTo for every attribute change, so each sale joins to the customer location valid at that time.
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