If you are preparing for the DP-700 Microsoft Fabric certification exam, understanding practical architecture and implementation scenarios is critical. In this post, we discuss five carefully selected exam-style practice questions that cover important DP-700 topics.
In this article, we covered the following core areas:
- Microsoft Fabric storage options including Eventhouse
- Using Data Pipelines for on premises data ingestion
- Warehouse performance optimization using V-Order
- Direct Lake performance considerations
- Star schema design and dimensional modeling
These are the exact types of scenarios I focused on while preparing for the DP-700 exam. Each question below is structured in an exam style format with a detailed explanation to help you understand the reasoning.
Question 1
You have a Fabric workspace that contains semi structured data. The data must be written only by using Apache Spark. The same data must be queried using T-SQL, KQL, and Apache Spark.
Which storage option should you use?
- Lakehouse
- Eventhouse
- Datamart
- Warehouse
Correct Answer: B. Eventhouse
Explanation:
An Eventhouse is designed for event based and semi structured workloads. It supports multi engine querying including KQL, T-SQL, and Spark. Since the data is written using Spark and must be accessible across different query engines, Eventhouse provides the most suitable architecture.
The other options do not fully support the required combination of Spark ingestion and multi engine query support.
Question 2
You have a Fabric workspace containing a warehouse named Warehouse1. You also have an on premises SQL Server database named Database1 that is connected using an on premises data gateway.
You need to copy data from Database1 to Warehouse1.
Which item should you use?
- Dataflow Gen1
- Data Pipeline
- KQL Queryset
- Notebook
Correct Answer: B. Data Pipeline
Explanation:
Data Pipelines in Microsoft Fabric are designed for orchestrating and automating data movement between sources and destinations. They support on premises connectivity using a data gateway and can load data directly into a warehouse.
This makes Data Pipeline the most appropriate and scalable solution for copying data from SQL Server to a Fabric warehouse.
Question 3
You have a Fabric workspace with a warehouse named Warehouse1. An on premises SQL Server database named Database1 is accessible through a data gateway.
You must copy data from Database1 into Warehouse1.
Which Fabric item should you use?
- Apache Spark job definition
- Data Pipeline
- Dataflow Gen1
- Eventstream
Correct Answer: B. Data Pipeline
Explanation:
Data Pipelines are specifically designed for reliable data transfer and orchestration between systems. They support structured movement of data from on premises sources into Fabric storage components.
Spark jobs focus on processing, Dataflows focus on transformation, and Eventstream is designed for streaming scenarios. Therefore, Data Pipeline is the correct choice.
Question 4
You have a Fabric F32 capacity workspace that contains a warehouse named DW1. The warehouse contains a fact table that has grown from 200 million rows to 500 million rows. Power BI reports use Direct Lake mode and display year over year comparisons.
Users report performance degradation and occasional visual errors. You must improve performance while minimizing operational cost.
What should you do?
- Change MD5 hash to SHA256
- Increase capacity
- Enable V-Order
- Modify surrogate key data type
- Create views
Correct Answer: C. Enable V-Order
Explanation:
V-Order improves data organization and compression for Delta tables, which enhances query performance especially for large datasets used in Direct Lake mode. Enabling V-Order improves performance without increasing capacity, thereby minimizing cost.
Increasing capacity would increase operational cost. Changing hash functions or surrogate key types does not directly address query performance optimization.
Question 5
You have a Fabric workspace containing a lakehouse named Lakehouse1. Data is ingested as a single flat table with the following columns:
| Column Name | Description |
|---|---|
| TransactionID | Unique identifier for each transaction |
| Date | Date of transaction |
| ProductID | Unique identifier for each product |
| ProductColor | Descriptive attribute for product color |
| ProductName | Unique name for each product |
| SalesAmount | Sales value of transaction |
You are implementing a star schema with FactSales and DimProduct tables and tracking changes in DimProduct.
Which three columns should be included in the DimProduct table?
- Date
- ProductName
- ProductColor
- TransactionID
- SalesAmount
- ProductID
Correct Answer: B, C, F
Explanation:
In a star schema, the dimension table contains descriptive attributes and a business key. For DimProduct, the required columns are:
| Column | Reason |
|---|---|
| ProductID | Primary key used to relate to fact table |
| ProductName | Descriptive attribute for reporting |
| ProductColor | Additional descriptive attribute |
FactSales should contain TransactionID, Date, SalesAmount, and ProductID as a foreign key.
Key Takeaways for DP-700 Exam
| Topic | Key Concept |
|---|---|
| Storage | Eventhouse supports multi engine query for semi structured data |
| Ingestion | Use Data Pipeline for on premises to Fabric movement |
| Performance | Enable V-Order for Direct Lake optimization |
| Modeling | Dimension tables contain descriptive attributes and business keys |
Mastering these architecture and implementation concepts will significantly improve your confidence in solving DP-700 case study and scenario based questions.
Continue practicing similar scenarios and focus on understanding why a particular Fabric component is selected in each situation.
