Implementing Data Engineering Solutions Using Microsoft Fabric
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Exam Code
DP-700
Exam Name
Implementing Data Engineering Solutions Using Microsoft Fabric
Questions
137 Questions Answers With Explanation
Update Date
August 03, 2026
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What Is the DP-700 Certification Exam?
The DP-700 certification exam is a standardized assessment designed to measure a candidate's knowledge, competencies, and practical understanding within a defined professional field. It serves as the primary requirement for earning the Microsoft Certified: Fabric Analytics Engineer Associate, a credential that represents a recognized level of proficiency in its respective industry. Depending on the field, this may involve theoretical knowledge, applied problem-solving, regulatory understanding, or hands-on procedural competence.
The exam is typically developed and maintained by an accrediting body or professional organization that sets the standards for the Microsoft Certified: Fabric Analytics Engineer Associate. This ensures that anyone who earns the credential has met a consistent benchmark, regardless of where they studied or gained their experience. For many professionals, the DP-700 Certification Exam represents a formal checkpoint in their career, one that confirms readiness to take on greater responsibility within their chosen field.
Why the Microsoft Certified: Fabric Analytics Engineer Associate Certification Matters?
Certifications like the Microsoft Certified: Fabric Analytics Engineer Associate exist because industries need a reliable way to verify competence beyond a resume or a job title. Earning this credential signals to employers, clients, and colleagues that a professional has invested time in building a structured foundation of knowledge and has been evaluated against an established standard.
Beyond individual recognition, the Microsoft Certified: Fabric Analytics Engineer Associate certification often supports broader professional development. It can influence hiring decisions, contribute to internal advancement, or serve as a prerequisite for more specialized roles within the field. In many industries, certifications also help standardize expectations across organizations, making it easier for professionals to move between employers or sectors while carrying a credential that is widely understood and respected.
Who Should Take the DP-700 Exam?
The DP-700 exam is generally relevant to individuals who are either entering a field or looking to formalize skills they have already developed through experience. This can include early-career professionals seeking a credential to support their first steps into the industry, as well as experienced practitioners who want official recognition of knowledge gained on the job.
Students preparing to enter the workforce may also pursue the DP-700 exam as a way to strengthen their qualifications before graduating or applying for their first roles. In some fields, employers actively encourage or require staff to pursue this certification as part of ongoing professional development, particularly in industries where standards, safety, or compliance play a significant role in daily responsibilities.
Knowledge and Skills Evaluated in the Implementing Data Engineering Solutions Using Microsoft Fabric
The Implementing Data Engineering Solutions Using Microsoft Fabric is built to evaluate both foundational knowledge and the practical judgment needed to apply that knowledge in real situations. Candidates are generally expected to understand core principles and terminology relevant to their field, along with the reasoning behind established procedures, standards, or best practices.
Depending on the industry, this may include understanding regulatory requirements, following established protocols, applying analytical or technical methods, or exercising sound judgment in situations that require careful decision-making. Rather than testing isolated facts in a vacuum, the Implementing Data Engineering Solutions Using Microsoft Fabric tends to reward candidates who can connect concepts to realistic scenarios, reflecting the kind of thinking expected in day-to-day professional practice.
DP-700 Exam Preparation Resources
Preparing for the DP-700 certification exam becomes more effective when using high-quality and up-to-date study materials. MyCertsHub provides resources designed to help candidates build knowledge, practice consistently, and become familiar with the actual exam format.
Effective preparation for the DP-700 certification exam usually begins with a clear understanding of the exam's objectives and structure. Reviewing official guidelines or documentation published by the certifying body provides the most accurate picture of what will be covered and how heavily different areas are weighted.
From there, many candidates benefit from building a structured study plan that breaks preparation into manageable sections over a set period of time. A well-organized DP-700 Study Guide can help sequence this material logically, especially for those approaching a topic for the first time. Consistent review, paired with realistic practice, tends to produce better retention than concentrated last-minute studying.
Practical experience, where applicable to the field, also plays an important role in preparation. Working through DP-700 Practice Questions and a DP-700 practice test can help candidates identify gaps in their understanding and become familiar with the format and pacing of the actual exam. In fields where hands-on skill is assessed, supplementing study with real-world practice or supervised experience often makes the difference between recognizing correct information and genuinely understanding it.
Benefits of Earning the Microsoft Certified: Fabric Analytics Engineer Associate Certification
Successfully earning the Microsoft Certified: Fabric Analytics Engineer Associate certification offers benefits that extend well beyond passing a single exam. It provides documented proof of competence that can be referenced on a resume, professional profile, or internal performance review, offering a clear, third-party validation of skill and knowledge.
The credential can also strengthen professional credibility when working with clients, patients, stakeholders, or colleagues who may not be positioned to evaluate technical or specialized knowledge directly. Over time, this recognition often contributes to expanded career opportunities, whether through new responsibilities, higher-level roles, or eligibility for additional certifications that build on this foundational credential.
Prepare for the DP-700 Exam with MyCertsHub
Preparing for the DP-700 exam is a process that benefits from organized, consistent effort rather than rushed, last-minute review. MyCertsHub is designed to support that process by offering study resources, practice materials, and educational content that help candidates understand what the Implementing Data Engineering Solutions Using Microsoft Fabric covers and how to approach their preparation thoughtfully.
Whether someone is just beginning to explore the Microsoft Certified: Fabric Analytics Engineer Associate or is in the final stages of reviewing material before their exam date, MyCertsHub aims to serve as a dependable resource throughout that journey. Every candidate's path to certification looks a little different, and the goal remains the same: to provide clear, genuinely useful information that supports real understanding of the subject matter.
Microsoft DP-700 Sample Question Answers
Question # 1
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 every15 minutes.You discover that Pipeline1 keeps failing.You need to identify which SQL query was executed when the pipeline failed.What should you do?
A. From Monitoring hub, select the latest failed run of Pipeline1, and then view the output JSON. B. From Monitoring hub, select the latest failed run of Pipeline1, and then view the input JSON. C. From Real-time hub, select Fabric events, and then review the details of
Microsoft.Fabric.ItemReadFailed. D. From Real-time hub, select Fabric events, and then review the details of Microsoft.Fabric.ItemUpdateFailed.
Answer: B
Explanation:
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.
Question # 2
You have a Fabric workspace that contains an eventstream named EventStream1. EventStream1
outputs events to a table in a lakehouse.
You need to remove files that are older than seven days and are no longer in use.
Which command should you run?
A. VACUUM B. COMPUTE C. OPTIMIZE D. CLONE
Answer: A
Question # 3
You have a Fabric workspace that contains a warehouse named Warehouse1. Data is loaded dailyinto 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?
A. sys.dm_exec_connections B. sys.dm_exec_requests C. queryinsights.long_running_queries D. queryinsights.frequently_run_queries E. sys.dm_exec_sessions
Answer: E
Explanation:
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.
Question # 4
You have a Fabric workspace that contains a lakehouse named Lakehouse1. Lakehouse1 contains aDelta table named Table1.You analyze Table1 and discover that Table1 contains 2,000 Parquet files of 1 MB each.You need to minimize how long it takes to query Table1.What should you do?
A. Disable V-Order and run the OPTIMIZE command. B. Disable V-Order and run the VACUUM command. C. Run the OPTIMIZE and VACUUM commands.
Answer: C
Explanation:
Problem Overview:
Table1 has 2,000 small Parquet files (1 MB each).
Query performance suffers when the table contains numerous small files because the query engine
must process each file individually, leading to significant overhead.
Solution:
To improve performance, file compaction is necessary to reduce the number of small files and
create larger, optimized files.
Commands and Their Roles:
OPTIMIZE Command:
- Compacts small Parquet files into larger files to improve query performance.
- It supports optional features like V-Order, which organizes data for efficient scanning.
VACUUM Command:
- Removes old, unreferenced data files and metadata from the Delta table.
- Running VACUUM after OPTIMIZE ensures unnecessary files are cleaned up, reducing storage
overhead and improving performance.
Question # 5
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 solutionmust meet the following requirementsTrigger the process when a new file is added.Provide the highest throughput.Which type of item should you use to ingest the data?
A. Data pipeline B. Environment C. KQL queryset D. Dataflow Gen2
Answer: A
Explanation:
To efficiently ingest large data files (500 GB each) into Lakehouse1 with high throughput and trigger
Question # 6
You have an Azure event hub. Each event contains the following fields:BikepointIDStreetNeighbourhoodLatitudeLongitudeNo_BikesNo_Empty_DocksYou need to ingest the events. The solution must only retain events that have a Neighbourhood valueof Chelsea, and then store the retained events in a Fabric lakehouse.What should you use?
A. a KQL queryset B. an eventstream C. a streaming dataset D. Apache Spark Structured Streaming
Answer: B
Explanation:
An eventstream is the best solution for ingesting data from Azure Event Hub into Fabric, while
applying filtering logic such as retaining only the events that have a Neighbourhood value of
"Chelsea." Eventstreams in Microsoft Fabric are designed for handling real-time data streams and can
apply transformation logic directly on incoming events. In this case, the eventstream can filter events
based on the Neighbourhood field before storing the retained events in a Fabric lakehouse.
Eventstreams are well-suited for stream processing, such as this case where you need to filter out
only specific data (events with a Neighbourhood of "Chelsea") before storing it in the lakehouse.
Question # 7
You have a Fabric workspace that contains an eventstream named Eventstream1. Eventstream1processes data from a thermal sensor by using event stream processing, and then stores the data in alakehouse.You need to modify Eventstream1 to include the standard deviation of the temperature.Which transform operator should you include in the Eventstream1 logic?
A. Expand B. Group by C. Union D. Aggregate
Answer: D
Explanation:
To compute the standard deviation of the temperature from the thermal sensor data, you would use
the Aggregate transform operator in Eventstream1. The Aggregate operator allows you to apply
functions like sum, average, count, and statistical functions like standard deviation across a group of
rows or events. This operator is ideal for operations that require summarizing or computing statistics
over a dataset, such as calculating the standard deviation.
Question # 8
You have a Fabric workspace that contains a semantic model named Model1.
You need to dynamically execute and monitor the refresh progress of Model1.
What should you use?
A. dynamic management views in Microsoft SQL Server Management Studio B. Monitoring hub C. dynamic management views in Azure Data Studio D. a semantic link in a notebook
Answer: D
Explanation:
Semantic models in Microsoft Fabric are part of Power BI datasets and require refreshes to stay
updated with the latest data. Dynamically executing and monitoring the refresh progress requires a tool or approach that
integrates with Fabric's capabilities for semantic models.
Question # 9
You have a Fabric workspace that contains a warehouse named DW1. DW1 is loaded by using anotebook named Notebook1.You need to identify which version of Delta was used when Notebook1 was executed.What should you use
A. Real-Time hub B. OneLake data hub C. the Admin monitoring workspace D. Fabric Monitor E. the Microsoft Fabric Capacity Metrics app
Answer: C
Explanation:
To identify the version of Delta used when Notebook1 was executed, you should use the Admin
monitoring workspace. The Admin monitoring workspace allows you to track and monitor detailed
information about the execution of notebooks and jobs, including the underlying versions of Delta or
other technologies used. It provides insights into execution details, including versions and
configurations used during job runs, making it the most appropriate choice for identifying the Delta
version used during the execution of Notebook1.
Question # 10
You have five Fabric workspaces.
You are monitoring the execution of items by using Monitoring hub.
You need to identify in which workspace a specific item runs.
Which column should you view in Monitoring hub?
A. Start time B. Capacity C. Activity name D. Submitter E. Item type F. Job type G. Location
Answer: G
Explanation:
To identify in which workspace a specific item runs in Monitoring hub, you should view the Location
column. This column indicates the workspace where the item is executed. Since you have multiple
workspaces and need to track the execution of items across them, the Location column will show you
the exact workspace associated with each item or job execution.
Question # 11
You have a Fabric workspace that contains a lakehouse and a notebook named Notebook1.Notebook1 reads data into a DataFrame from a table named Table1 and applies transformation logic.The data from the DataFrame is then written to a new Delta table named Table2 by using a mergeoperation.You need to consolidate the underlying Parquet files in Table1.Which command should you run?
A. VACUUM B. BROADCAST C. OPTIMIZE D. CACHE
Answer: C
Explanation:
To consolidate the underlying Parquet files in Table1 and improve query performance by optimizing
the data layout, you should use the OPTIMIZE command in Delta Lake. The OPTIMIZE command
coalesces smaller files into larger ones and reorganizes the data for more efficient reads. This is
particularly useful when working with large datasets in Delta tables, as it helps reduce the number of
files and improves performance for subsequent queries or operations like MERGE.
Question # 12
You have a Fabric workspace named Workspace1 that contains a data pipeline named Pipeline1 anda lakehouse named Lakehouse1.You have a deployment pipeline named deployPipeline1 that deploys Workspace1 to Workspace2.You restructure Workspace1 by adding a folder named Folder1 and moving Pipeline1 to Folder1.You use deployPipeline1 to deploy Workspace1 to Workspace2.What occurs to Workspace2?
A. Folder1 is created, Pipeline1 moves to Folder1, and Lakehouse1 is deployed. B. Only Pipeline1 and Lakehouse1 are deployed. C. Folder1 is created, and Pipeline1 and Lakehouse1 move to Folder1. D. Only Folder1 is created and Pipeline1 moves to Folder1.
Answer: A
Explanation:
When you restructure Workspace1 by adding a new folder (Folder1) and moving Pipeline1 into it,
deployPipeline1 will deploy the entire structure of Workspace1 to Workspace2, preserving the
changes made in Workspace1. This includes:
Folder1 will be created in Workspace2, mirroring the structure in Workspace1.
Pipeline1 will be moved into Folder1 in Workspace2, maintaining the same folder structure.
Lakehouse1 will be deployed to Workspace2 as it exists in Workspace1.
Question # 13
You have a Fabric capacity that contains a workspace named Workspace1. Workspace1 contains alakehouse named Lakehouse1, a data pipeline, a notebook, and several Microsoft Power BI reports.A user named User1 wants to use SQL to analyze the data in Lakehouse1.You need to configure access for User1. The solution must meet the following requirements:Provide User1 with read access to the table data in Lakehouse1.Prevent User1 from using Apache Spark to query the underlying files in Lakehouse1.Prevent User1 from accessing other items in Workspace1.What should you do?
A. Share Lakehouse1 with User1 directly and select Read all SQL endpoint data. B. Assign User1 the Viewer role for Workspace1. Share Lakehouse1 with User1 and select Read all
SQL endpoint data. C. Share Lakehouse1 with User1 directly and select Build reports on the default semantic model. D. Assign User1 the Member role for Workspace1. Share Lakehouse1 with User1 and select Read all
SQL endpoint data.
Answer: B
Explanation:
To meet the specified requirements for User1, the solution must ensure:
Read access to the table data in Lakehouse1: User1 needs permission to access the data within
Lakehouse1. By sharing Lakehouse1 with User1 and selecting the Read all SQL endpoint data option,
User1 will be able to query the data via SQL endpoints.
Prevent Apache Spark usage: By sharing the lakehouse directly and selecting the SQL endpoint data
option, you specifically enable SQL-based access to the data, preventing User1 from using Apache
Spark to query the data.
Prevent access to other items in Workspace1: Assigning User1 the Viewer role for Workspace1
ensures that User1 can only view the shared items (in this case, Lakehouse1), without accessing
other resources such as notebooks, pipelines, or Power BI reports within Workspace1.
This approach provides the appropriate level of access while restricting User1 to only the required
resources and preventing access to other workspace assets.
Question # 14
You have a Fabric workspace named Workspace1 that contains a warehouse named DW1 and a datapipeline named Pipeline1.You plan to add a user named User3 to Workspace1.You need to ensure that User3 can perform the following actions:View all the items in Workspace1.Update the tables in DW1.The solution must follow the principle of least privilege.You already assigned the appropriate object-level permissions to DW1.Which workspace role should you assign to User3?
A. Admin B. Member C. Viewer D. Contributor
Answer: D
Explanation:
To ensure User3 can view all items in Workspace1 and update the tables in DW1, the most
appropriate workspace role to assign is the Contributor role. This role allows User3 to:
View all items in Workspace1: The Contributor role provides the ability to view all objects within the
workspace, such as data pipelines, warehouses, and other resources.
Update the tables in DW1: The Contributor role allows User3 to modify or update resources within
the workspace, including the tables in DW1, assuming that appropriate object-level permissions are
set for the warehouse.
This role adheres to the principle of least privilege, as it provides the necessary permissions without
granting broader administrative rights.
Question # 15
Your company has a sales department that uses two Fabric workspaces named Workspace1 and Workspace2.The company decides to implement a domain strategy to organize the workspaces.You need to ensure that a user can perform the following tasks:Create a new domain for the sales department.Create two subdomains: one for the east region and one for the west region.Assign Workspace1 to the east region subdomain.Assign Workspace2 to the west region subdomainThe solution must follow the principle of least privilege.Which role should you assign to the user?
A. workspace Admin B. domain admin C. domain contributor D. Fabric admin
Answer: B
Explanation:
To implement a domain strategy and manage subdomains within Fabric, the domain admin role is
the appropriate role for the user. A domain admin has the permissions necessary to:
Create a new domain (for the sales department).
Create subdomains (for the east and west regions).
Assign workspaces (such as Workspace1 and Workspace2) to the appropriate subdomains.
The domain admin role allows for managing the structure and organization of workspaces in the
context of domains and subdomains while maintaining the principle of least privilege by limiting the
user's access to managing the domain structure specifically.
Question # 16
You have a Fabric workspace named Workspace1 that contains an Apache Spark job definition named Job1.You have an Azure SQL database named Source1 that has public internet access disabled.You need to ensure that Job1 can access the data in Source1.What should you create?
A. an on-premises data gateway B. a managed private endpoint C. an integration runtime D. a data management gateway
Answer: B
Explanation:
To allow Job1 in Workspace1 to access an Azure SQL database (Source1) with public internet access
disabled, you need to create a managed private endpoint. A managed private endpoint is a secure,
private connection that enables services like Fabric (or other Azure services) to access resources such
as databases, storage accounts, or other services within a virtual network (VNet) without requiring
public internet access. This approach maintains the security and integrity of your data while enabling
access to the Azure SQL database.
Question # 17
You have a Fabric workspace named Workspace1.You plan to integrate Workspace1 with Azure DevOps.You will use a Fabric deployment pipeline named deployPipeline1 to deploy items from Workspace1to higher environment workspaces as part of a medallion architecture. You will run deployPipeline1by using an API call from an Azure DevOps pipeline.You need to configure API authentication between Azure DevOps and Fabric.Which type of authentication should you use?
A. service principal B. Microsoft Entra username and password C. managed private endpoint D. workspace identity
Answer: A
Explanation:
When integrating Azure DevOps with Fabric (Workspace1), using a service principal is the
recommended authentication method. A service principal provides a way for applications (such as an
Azure DevOps pipeline) to authenticate and interact with resources securely. It allows Azure DevOps
to authenticate API calls to Fabric without requiring direct user credentials. This method is ideal for
automating tasks such as deploying items through a Fabric deployment pipeline.
Question # 18
You have a Fabric workspace that contains a Real-Time Intelligence solution and an eventhouse.Users report that from OneLake file explorer, they cannot see the data from the eventhouse.You enable OneLake availability for the eventhouse.What will be copied to OneLake?
A. only data added to new databases that are added to the eventhouse B. only the existing data in the eventhouse C. no data D. both new data and existing data in the eventhouse E. only new data added to the eventhouse
Answer: D
Explanation:
When you enable OneLake availability for an eventhouse, both new and existing data in the
eventhouse will be copied to OneLake. This feature ensures that data, whether newly ingested or
already present, becomes available for access through OneLake, making it easier for users to interact
with and explore the data directly from OneLake file explorer.
Question # 19
You have a Fabric workspace named Workspace1 that contains a warehouse named Warehouse1.You plan to deploy Warehouse1 to a new workspace named Workspace2.As part of the deployment process, you need to verify whether Warehouse1 contains invalidreferences. The solution must minimize development effort.What should you use?
A. a database project B. a deployment pipeline C. a Python script D. a T-SQL script
Answer: C
Explanation:
A deployment pipeline in Fabric allows you to deploy assets like warehouses, datasets, and reports
between different workspaces (such as from Workspace1 to Workspace2). One of the key features of
a deployment pipeline is the ability to check for invalid references before deployment. This can help
identify issues with assets, such as broken links or dependencies, ensuring the deployment is
successful without introducing errors. This is the most efficient way to verify references and manage
the deployment with minimal development effort.
Question # 20
You have a Fabric deployment pipeline that uses three workspaces named Dev, Test, and Prod.
You need to deploy an eventhouse as part of the deployment process.
What should you use to add the eventhouse to the deployment process?
A. GitHub Actions B. a deployment pipeline C. an Azure DevOps pipeline
Answer: B
Explanation:
A deployment pipeline in Fabric is designed to automate the process of deploying assets (such as
reports, datasets, even houses, and other objects) between environments like Dev, Test, and Prod.
Since you need to deploy an eventhouse as part of the deployment process, a deployment pipeline is
the appropriate tool to move this asset through the different stages of your environment.
Question # 21
You have a Fabric warehouse named DW1. DW1 contains a table that stores sales data and is used bymultiple sales representatives.You plan to implement row-level security (RLS).You need to ensure that the sales representatives can see only their respective data.Which warehouse object do you require to implement RLS?
A. ISTORED PROCEDURE B. CONSTRAINT C. SCHEMA D. FUNCTION
Answer: D
Explanation:
To implement Row-Level Security (RLS) in a Fabric warehouse, you need to use a function that
defines the security logic for filtering the rows of data based on the user's identity or role. This
function can be used in conjunction with a security policy to control access to specific rows in a table.
In the case of sales representatives, the function would define the filtering criteria (e.g., based on a
column such as SalesRepID or SalesRepName), ensuring that each representative can only see their respective data.
Question # 22
You have a Fabric workspace named Workspace1 that contains a lakehouse named Lakehouse1.Lakehouse1 contains the following tables:OrdersCustomerEmployeeThe Employee table contains Personally Identifiable Information (PII).A data engineer is building a workflow that requires writing data to the Customer table, however, theuser does NOT have the elevated permissions required to view the contents of the Employee table.You need to ensure that the data engineer can write data to the Customer table without reading datafrom the Employee table.Which three actions should you perform? Each correct answer presents part of the solution.NOTE: Each correct selection is worth one point.
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 that contains a new lakehouse named Lakehouse2. G. Assign the data engineer the Viewer role for Workspace1.
Answer: A, D, E
Explanation:
To meet the requirements of ensuring that the data engineer can write data to the Customer table
without reading data from the Employee table (which contains Personally Identifiable Information, or
PII), you can implement the following steps:
Share Lakehouse1 with the data engineer.
By sharing Lakehouse1 with the data engineer, you provide the necessary access to the data within
the lakehouse. However, this access should be controlled through roles and permissions, which will
allow writing to the Customer table but prevent reading from the Employee table.
Assign the data engineer the Contributor role for Workspace1.
Assigning the Contributor role for Workspace1 grants the data engineer the ability to perform actions
such as writing to tables (e.g., the Customer table) within the workspace. This role typically allows
users to modify and manage data without necessarily granting them access to view all data (e.g., PII data in the Employee table).
Migrate the Employee table from Lakehouse1 to Lakehouse2.
To prevent the data engineer from accessing the Employee table (which contains PII), you can
migrate the Employee table to a separate lakehouse (Lakehouse2) or workspace (Workspace2).
This separation of sensitive data ensures that the data engineer's access is restricted to the Customer
table in Lakehouse1, while the Employee table can be managed separately and protected under different access controls.
Question # 23
You have a Fabric workspace named Workspace1 that contains a notebook named Notebook1.In Workspace1, you create a new notebook named Notebook2.You need to ensure that you can attach Notebook2 to the same Apache Spark session as 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 ensure that Notebook2 can attach to the same Apache Spark session as Notebook1, you need to
enable high concurrency for notebooks. High concurrency allows multiple notebooks to share a
Spark session, enabling them to run within the same Spark context and thus share resources like
cached data, session state, and compute capabilities. This is particularly useful when you need
notebooks to run in sequence or together while leveraging shared resources.
Question # 24
You have a Fabric F32 capacity that contains a workspace. The workspace contains a warehousenamed DW1 that is modelled by using MD5 hash surrogate keys.DW1 contains a single fact table that has grown from 200 million rows to 500 million rows during the past year.You have Microsoft Power BI reports that are based on Direct Lake. The reports show year-over-year values.Users report that the performance of some of the reports has degraded over time and some visuals show errors.You need to resolve the performance issues. The solution must meet the following requirements:Provide the best query performance.Minimize operational costs.Which 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: D
Explanation:
In this case, the key issue causing performance degradation likely stems from the use of MD5 hash
surrogate keys. MD5 hashes are 128-bit values, which can be inefficient for large datasets like the
500 million rows in your fact table. Using a more efficient data type for surrogate keys (such as
integer or bigint) would reduce the storage and processing overhead, leading to better query
performance. This approach will improve performance while minimizing operational costs because it
reduces the complexity of querying and indexing, as smaller data types are generally faster and more
efficient to process.
Question # 25
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 usingan on-premises data gateway.You need to copy data from Database1 to Warehouse1.Which item should you use?
A. an Apache Spark job definition B. a data pipeline C. a Dataflow Gen1 dataflow D. an eventstream
Answer: B
Explanation:
To copy data from an on-premises Microsoft SQL Server database (Database1) to a warehouse
(Warehouse1) in Fabric, a data pipeline is the most appropriate tool. A data pipeline in Fabric is
designed to move data between various data sources and destinations, including on-premises
databases like SQL Server, and cloud-based storage like Fabric warehouses. The data pipeline can
handle the connection through an on-premises data gateway, which is required to access onpremises
data. This solution facilitates the orchestration of data movement and transformations if
needed.
Feedback That Matters: Reviews of Our Microsoft DP-700 Dumps
Luna TaylorAug 14, 2026
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Solid prep material for DP-700. Although the case studies portion of this course covered similar scenarios to those on the exam, I was anxious about it. Helped a lot.
Luna Taylor
Gemma Phillips
Sophia Johnston