Microsoft  DP-700 Exam Dumps & Practice Test Questions

Question 1:

How can you configure access to ensure that data analysts are permitted to view and analyze data only from the gold layer in a lakehouse environment?

A. Add the DataAnalyst group to the Viewer role for WorkspaceA
B. Share the lakehouse with the DataAnalysts group and assign the “Build reports on the default semantic model” permission
C. Share the lakehouse with the DataAnalysts group and assign the “Read all SQL Endpoint data” permission
D. Share the lakehouse with the DataAnalysts group and assign the “Read all Apache Spark” permission

Correct Answer: B

Explanation:

In environments structured around a medallion architecture, the gold layer represents the most refined, cleaned, and trusted data, ready for use in business intelligence and analytics. Data analysts typically work with this layer, and strict data governance policies often restrict their access to only this final layer to protect raw (bronze) or intermediate (silver) data from unauthorized access or misuse.

The objective in this case is to allow analysts to generate insights from the gold layer using semantic models while restricting access to the rest of the environment. The best way to accomplish this is by sharing the lakehouse with the DataAnalysts group and assigning them the “Build reports on the default semantic model” permission. This enables the analysts to build reports using the structured data in the semantic model without allowing broader access to other resources.

Let’s review why the other options are not ideal:

• Option A: Granting the DataAnalyst group Viewer access to the entire WorkspaceA provides read permissions to all objects, not just the gold layer. This violates the principle of least privilege.
  
  

• Option C: Granting “Read all SQL Endpoint data” might expose analysts to additional data across different layers or components not intended for them.
  
  

• Option D: The “Read all Apache Spark” permission grants broad read access across Spark jobs, potentially exposing underlying datasets in bronze or silver layers.

By contrast, Option B ensures controlled access with a narrow scope. It empowers analysts to do their job effectively—building reports on trusted data—while maintaining data security and governance standards. This permission model supports operational clarity, avoids data leakage, and ensures compliance with internal policies. Therefore, Option B is the most suitable and secure approach.

Question 2:

You need to store semi-structured data in a Fabric workspace where it will be written using Apache Spark and accessed using T-SQL, KQL, and Spark. Which storage solution should you choose?

A. A lakehouse
B. An eventhouse
C. A datamart
D. A warehouse

Correct Answer: A

Explanation:

In modern data architectures, managing semi-structured data while supporting diverse access mechanisms—such as T-SQL, Kusto Query Language (KQL), and Apache Spark—requires a flexible and scalable solution. The lakehouse model is uniquely designed to fulfill this role, combining features of a data lake and a data warehouse to support both unstructured/semi-structured storage and structured, analytical querying.

A lakehouse allows semi-structured data to be stored in open formats like Parquet or Delta Lake, which are optimal for both read and write operations across different engines. Since the requirement specifies that data will be written using Apache Spark, this aligns perfectly with lakehouse functionality, as Spark is commonly used for both batch and streaming writes to this type of storage. Additionally, lakehouses are accessible via T-SQL for SQL-style queries, KQL for telemetry/log analytics, and Spark for advanced big data processing—making it a comprehensive and flexible solution.

Let’s examine why the other options don’t fit:

• Option B: "Eventhouse" is not a standard data storage term or technology, and therefore it’s not a recognized or supported solution in this context.

• Option C: A datamart is typically built for structured data, often with a focus on departmental analytics. It lacks native support for writing data via Apache Spark or managing semi-structured formats effectively.

• Option D: A warehouse is optimized for structured, relational data and is not well-suited for semi-structured formats or Spark-based writes. It also lacks the flexibility needed for KQL and other unstructured data tools.

In summary, the lakehouse architecture offers the flexibility of a data lake with the performance and schema management of a data warehouse. It supports all the necessary access interfaces (T-SQL, KQL, Spark), while also being well-suited for the ingestion and processing needs of Apache Spark. As such, it is the optimal storage choice for this scenario.

Question 3:

You are working in a Fabric workspace that contains a warehouse named Warehouse1. Your environment includes an on-premises Microsoft SQL Server database called Database1, which is accessible through an on-premises data gateway. You need to copy data from Database1 into Warehouse1. 

Which of the following tools should you use?

A. A Dataflow Gen1 dataflow
B. A data pipeline
C. A KQL query set
D. A notebook

Answer: B

Explanation:

When transferring data from an on-premises SQL Server database into a data warehouse hosted in Microsoft Fabric, the most efficient and practical approach is to use a data pipeline. Data pipelines are designed specifically to manage data movement and transformation tasks. In this case, they are ideal for extracting data from an on-premises environment using a gateway and loading it into Warehouse1 efficiently.

A data pipeline can be scheduled and configured to pull data on a regular basis or as needed, and it supports integration with the on-premises data gateway, which ensures secure communication between on-premises data sources and the Fabric service. Pipelines allow for orchestrating data flows, handling both transformation and loading logic, and provide monitoring and retry mechanisms to ensure data reliability.

Let’s consider why the other options are less suitable:

• A. Dataflow Gen1 dataflow: While dataflows can perform transformations and data ingestion, they are more limited in scope and best suited for self-service or lightweight ETL scenarios. They aren’t as scalable or efficient as pipelines for large or operational data transfer jobs, especially across hybrid environments.

• C. KQL query set: KQL (Kusto Query Language) is used with Azure Data Explorer for log analytics and telemetry, not for moving data from SQL Server into a Fabric warehouse. It does not fulfill the requirement to extract and load data.

• D. Notebook: Notebooks are mainly used for interactive data exploration, analytics, and running code in languages like Python or Spark SQL. While technically possible to use notebooks for data movement, they are not built for production-grade data ingestion and orchestration.

Thus, a data pipeline is the most appropriate and scalable tool for copying data from Database1 to Warehouse1 using the on-premises gateway.

Question 4:

Within your Fabric workspace, you manage a data warehouse named Warehouse1. You also have an on-premises SQL Server database (Database1) connected via an on-premises data gateway. 

You need to copy data from Database1 into Warehouse1. Which feature should you use?

A. An Apache Spark job definition
B. A data pipeline
C. A Dataflow Gen1 dataflow
D. An event stream

Answer: B

Explanation:

To move data efficiently from an on-premises SQL Server instance to a Fabric-hosted warehouse, the most suitable and robust method is to use a data pipeline. This tool is built to orchestrate the flow of data across environments and supports the integration of on-premises resources through a data gateway.

A data pipeline allows you to build an ETL (Extract, Transform, Load) process that can handle large volumes of data reliably and repeatably. It enables seamless data ingestion into Warehouse1 and supports batch scheduling, transformation steps, and monitoring—all critical for production environments.

Now, here’s why the other options are less ideal:

• A. Apache Spark job definition: Spark jobs are typically used for large-scale, distributed data processing and analytics, such as machine learning or data transformations. However, using Spark just to move data from one system to another is overkill and less efficient for this specific use case.
  
  

• C. Dataflow Gen1 dataflow: Although dataflows are often used for ingesting and transforming data in Microsoft platforms, they are more aligned with self-service BI and smaller data loads. Pipelines offer a more scalable and manageable approach, especially for integration with on-premises systems.
  
  

• D. Event stream: Event streams are used for handling real-time data, such as telemetry or live sensor feeds. They are not appropriate for bulk or scheduled data loads from a relational database like SQL Server to a data warehouse.

Given the nature of the task—copying structured data from an on-premises source into a cloud-based data warehouse—a data pipeline offers the right combination of reliability, scale, and integration features to accomplish the goal efficiently.

Question 5:

You manage a Fabric F32 capacity that includes a warehouse named DW1. Over the past year, DW1 has grown significantly, expanding from 200 million to 500 million rows. The warehouse uses MD5 hash surrogate keys. Reports built with Direct Lake are showing slower performance and errors in some visuals. 

You want to resolve these issues by optimizing performance while keeping operational costs low. What should you do?

A. Change the MD5 hash to SHA256
B. Increase the capacity
C. Enable V-Order
D. Modify the surrogate keys to use a different data type
E. Create views

Answer: C

Explanation:

The primary concern here is improving the performance of Power BI reports using Direct Lake mode on a warehouse that has rapidly increased in size. The solution must also be cost-effective. The best approach in this scenario is to enable V-Order.

V-Order is a storage optimization technique designed specifically to improve performance in Direct Lake scenarios. It physically organizes data within the storage engine to align with commonly queried columns and reduces the volume of data scanned during queries. As a result, query execution becomes significantly faster, which directly benefits Power BI reports using Direct Lake, where performance is tightly coupled to how efficiently data can be accessed.

Let’s assess why the other options are less effective:

• A. Change the MD5 hash to SHA256: This change would not yield performance improvements. In fact, SHA256 produces longer hash outputs, which can increase storage requirements and reduce efficiency during joins and lookups, potentially making performance worse.

• B. Increase the capacity: While upgrading capacity could provide more resources, it would also increase costs. It does not address the root cause of poor performance, which is likely inefficient data storage or access patterns.

• D. Modify the surrogate keys: Changing the data type of surrogate keys could reduce row size slightly, but unless the keys are contributing significantly to performance degradation (which is unlikely), this won’t yield major improvements.

• E. Create views: Views can simplify query syntax but don't inherently boost performance. They still rely on the underlying data structure and may add complexity to query plans if not properly optimized.

Therefore, enabling V-Order directly tackles the performance problem by improving data layout and query efficiency without adding extra operational cost, making it the most effective and economical solution.

Question 6:

You are working in a Fabric workspace named Workspace1, which already contains a notebook called Notebook1. You create another notebook named Notebook2 in the same workspace. 

To allow Notebook2 to connect to the same Apache Spark session currently used by Notebook1, what should you do?

A. Enable high concurrency for notebooks
B. Enable dynamic allocation for the Spark pool
C. Change the runtime version
D. Increase the number of executors

Answer: A

Explanation:

To enable two notebooks in the same Fabric workspace to share the same Apache Spark session, high concurrency must be activated. High concurrency mode allows multiple users or notebooks to connect to and utilize the same Spark session without spawning individual sessions. This capability is essential when a shared context is needed, such as for real-time collaboration or when working with shared state or cached data.

By default, Spark sessions are isolated for each notebook, which can cause duplicated computations, resource inefficiencies, and inconsistent data states. Enabling high concurrency ensures that sessions are reused, making interactions between notebooks more efficient and consistent.

Option B, enabling dynamic allocation, allows Spark to automatically scale resources based on workload. While helpful for optimizing performance, it does not influence whether multiple notebooks can share a session.
Option C, changing the runtime version, affects compatibility with certain APIs or libraries but does not affect session sharing capabilities.
Option D, increasing the number of executors, simply allocates more parallel processing units for Spark jobs and also doesn’t impact session sharing between notebooks.

In summary, high concurrency is the only setting that directly controls and enables shared Spark sessions between multiple notebooks. This setting is critical in collaborative environments and helps optimize resource usage, streamline development, and improve efficiency across the workspace.

Question 7:

Within your Fabric workspace Workspace1, you manage a lakehouse named Lakehouse1. It contains three tables: Orders, Customer, and Employee. The Employee table includes personally identifiable information (PII). 

A data engineer needs to write to the Customer table but should not have access to view the Employee table. What combination of actions will allow this?

A. Share Lakehouse1 with the data engineer
B. Assign the data engineer the Contributor role for Workspace2
C. Assign the data engineer the Viewer role for Workspace2
D. Assign the data engineer the Contributor role for Workspace1
E. Migrate the Employee table from Lakehouse1 to Lakehouse2
F. Create a new workspace named Workspace2 with a new lakehouse
G. Assign the data engineer the Viewer role for Workspace1

Answer: A, D, E

Explanation:

To allow the data engineer to write to the Customer table without having access to the sensitive Employee table, you must apply least privilege access principles and data separation.

First, Option A, sharing Lakehouse1, is essential because the engineer cannot access or interact with any lakehouse data unless it's explicitly shared. This establishes the initial access.

Option D, assigning the Contributor role for Workspace1, grants the engineer permissions to write or modify data. Unlike the Viewer role, the Contributor role allows creating or editing tables, but not necessarily reading from every table. However, the engineer would still see all tables unless further action is taken.

That’s where Option E comes in—migrating the Employee table to a separate lakehouse. This physical data separation ensures that even with Contributor access, the engineer cannot interact with the Employee table unless granted access to the new lakehouse as well. It effectively safeguards PII from unauthorized exposure while enabling legitimate work.

The other options are either irrelevant (like Option B, which assigns permissions to a different workspace), unnecessary (Option F, creating a new workspace is excessive), or too permissive (like Option G, which grants read-only access but still allows visibility into sensitive data).

This combination—access control, appropriate role assignment, and data isolation—ensures secure, compliant collaboration.

Question 8:

You manage a Fabric data warehouse named DW1, which stores sales information accessed by various sales representatives. You want to enforce row-level security (RLS) so that each representative only sees their own data. 

What type of warehouse object is needed to define the logic for this filtering?

A. STORED PROCEDURE
B. CONSTRAINT
C. SCHEMA
D. FUNCTION

Answer:  D

Explanation:

Implementing Row-Level Security (RLS) requires the use of a function that determines which rows a user is authorized to access. This function evaluates the current user’s identity—typically using system variables like USER_NAME()—and applies filtering logic accordingly.

The RLS function is then bound to the target table through a security policy that enforces this filtering every time a query is executed. For example, a function might return a predicate that matches the user’s region or employee ID against each row, ensuring only relevant records are shown.

Option A, a stored procedure, is used to encapsulate logic for procedural operations (e.g., ETL tasks), but does not apply to query-time row filtering.
Option B, constraints, are used for enforcing data validity (e.g., check constraints, foreign keys), not for access control.
Option C, schemas, are structural containers for grouping database objects, not security enforcement tools.

Using a function for RLS is a best practice in modern data platforms, including Microsoft Fabric, Synapse, and Azure SQL. It offers granular control and automates filtering without requiring users to write security-aware queries. The filtering is transparent and enforced at the engine level, helping maintain data privacy and compliance across shared environments.

Question 9:

You are working with a Power BI dataset that is connected to a lakehouse in Microsoft Fabric. Your organization wants to centralize data governance and reuse the semantic model across different departments without duplicating datasets. 

What is the best way to achieve this requirement?

A. Enable field parameters for each report to control access
B. Use a shared semantic model across multiple workspaces
C. Export the semantic model to Excel and distribute manually
D. Create separate datasets for each department in their own workspace

Correct Answer: B

Explanation:

In enterprise-level data modeling, centralized governance and semantic model reuse are crucial for ensuring data consistency and reducing duplication. The best way to enable this in Microsoft Fabric and Power BI is by using a shared semantic model.

A semantic model is a Power BI dataset that defines relationships, measures, hierarchies, and business logic. By default, semantic models are only accessible within the workspace in which they are created. However, when shared as a certified or promoted dataset, they can be reused across different workspaces.

By sharing the semantic model from a central workspace, departments across the organization can build their own reports and dashboards without creating duplicate datasets. This approach ensures:

• Centralized data logic – Measures and transformations are created once and reused.

• Improved performance – Less duplication leads to better resource utilization.

• Governance and security – Access can be controlled using Power BI permissions and roles.

• Scalability – Reduces management overhead when changes are required.

Let’s review the incorrect options:

• A (Enable field parameters) only changes the report view but doesn’t help with model reuse or governance.

• C (Exporting to Excel) breaks the connection to the live data and removes governance controls.

• D (Separate datasets) create redundant models, increasing maintenance complexity and risk of inconsistencies.

Therefore, Option B is the most scalable, efficient, and governance-aligned solution for sharing semantic models in a Microsoft Fabric environment.

Question 10:

You are designing a data solution using Microsoft Fabric. Your source data is JSON-based IoT telemetry, and you need to ingest, transform, and analyze it using Apache Spark, T-SQL, and KQL. 

The data should be written only by Spark. What is the most suitable storage option?

A. Data warehouse
B. Eventstream
C. Lakehouse
D. Datamart

Correct Answer: C

Explanation:

In Microsoft Fabric, selecting the appropriate storage layer depends on your data format, query requirements, and data ingestion tools. For scenarios involving semi-structured data like JSON, with write operations performed via Apache Spark, and access needed through T-SQL and KQL, the best fit is a lakehouse.

A lakehouse combines features of both a data lake (flexible, schema-less storage) and a data warehouse (structured querying). It is ideal for storing raw or transformed semi-structured data (such as JSON or Parquet), and it supports a wide range of compute engines:

• Apache Spark is tightly integrated with the lakehouse for scalable, distributed processing, making it perfect for writing and transforming data.
  
  

• T-SQL can be used to query structured views over this data in the lakehouse.
  
  

• KQL can be used for log and telemetry-style querying, especially useful for IoT scenarios.

Let’s consider the other options:

• A (Data warehouse) is optimized for structured, tabular data and doesn't support JSON or semi-structured formats well. It also doesn’t allow direct Spark writes.

• B (Eventstream) is used for real-time data ingestion but isn’t meant for long-term storage or multi-language querying.

• D (Datamart) is a self-service, SQL-based analytics tool in Power BI, but it’s not designed for semi-structured data or Spark-based ingestion.

Thus, the lakehouse provides the flexibility and compatibility required for your scenario. It supports Spark for writing, and T-SQL/KQL for querying, making it the optimal storage choice for semi-structured IoT telemetry data in Microsoft Fabric.