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Microsoft DP-700 Exam (page: 1)
Microsoft Implementing Data Engineering Solutions Using Fabric
Updated on: 28-Jul-2025

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DP-700 PDF 118 Questions

Case Study:

This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.
To start the case study
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs.
When you are ready to answer a question, click the Question button to return to the question.

Overview:

Litware, Inc. is a publishing company that has an online bookstore and several retail bookstores worldwide. Litware also manages an online advertising business for the authors it represents.

Existing Environment. Fabric Environment
Litware has a Fabric workspace named Workspace1. High concurrency is enabled for Workspace1.
The company has a data engineering team that uses Python for data processing.

Existing Environment. Data Processing
The retail bookstores send sales data at the end of each business day, while the online bookstore constantly provides logs and sales data to a central enterprise resource planning (ERP) system.
Litware implements a medallion architecture by using the following three layers: bronze, silver, and gold. The sales data is ingested from the ERP system as Parquet files that land in the Files folder in a lakehouse. Notebooks are used to transform the files in a Delta table for the bronze and silver layers. The gold layer is in a warehouse that has V-Order disabled.
Litware has image files of book covers in Azure Blob Storage. The files are loaded into the Files folder.

Existing Environment. Sales Data
Month-end sales data is processed on the first calendar day of each month. Data that is older than one month never changes.
In the source system, the sales data refreshes every six hours starting at midnight each day.
The sales data is captured in a Dataflow Gen2 dataflow.
When the dataflow runs, new and historical data is captured. The dataflow captures the following fields of the source:
Sales Date
Author
Price
Units
SKU
A table named AuthorSales stores the sales data that relates to each author. The table contains a column named AuthorEmail. Authors authenticate to a guest Fabric tenant by using their email address.

Existing Environment. Security Groups
Litware has the following security groups:
Sales
Fabric Admins
Streaming Admins

Existing Environment. Performance Issues
Business users perform ad-hoc queries against the warehouse. The business users indicate that reports against the warehouse sometimes run for two hours and fail to load as expected. Upon further investigation, the data engineering team receives the following error message when the reports fail to load: "The SQL query failed while running."
The data engineering team wants to debug the issue and find queries that cause more than one failure.
When the authors have new book releases, there is often an increase in sales activity. This increase slows the data ingestion process.
The company's sales team reports that during the last month, the sales data has NOT been up-to-date when they arrive at work in the morning.

Requirements. Planned Changes
Litware recently signed a contract to receive book reviews. The provider of the reviews exposes the data in Amazon Simple Storage Service (Amazon S3) buckets.
Litware plans to manage Search Engine Optimization (SEO) for the authors. The SEO data will be streamed from a REST API.

Requirements. Version Control
Litware plans to implement a version control solution in Fabric that will use GitHub integration and follow the principle of least privilege.

Requirements. Governance Requirements
To control data platform costs, the data platform must use only Fabric services and items. Additional Azure resources must NOT be provisioned.

Requirements. Data Requirements
Litware identifies the following data requirements:
Process the SEO data in near-real-time (NRT).
Make the book reviews available in the lakehouse without making a copy of the data.
When a new book cover image arrives in the Files folder, process the image as soon as possible.

You need to ensure that processes for the bronze and silver layers run in isolation.
How should you configure the Apache Spark settings?

  1. Disable high concurrency.
  2. Create a custom pool.
  3. Modify the number of executors.
  4. Set the default environment.

Answer(s): B

Explanation:

Isolated Compute
The Isolated Compute option provides more security to Spark compute resources from untrusted services by dedicating the physical compute resource to a single customer. Isolated compute option is best suited for workloads that require a high degree of isolation from other customer's workloads for reasons that include meeting compliance and regulatory requirements. The Isolate Compute option is only available with the XXXLarge (80 vCPU / 504 GB) node size and only available in the following regions. *The isolated compute option can be enabled or disabled after pool creation although the instance might need to be restarted*.
Scenario:

Existing Environment. Data Processing
Litware implements a medallion architecture by using the following three layers: bronze, silver, and gold. The sales data is ingested from the ERP system as Parquet files that land in the Files folder in a lakehouse.
Notebooks are used to transform the files in a Delta table for the bronze and silver layers. The gold layer is in a warehouse that has V-Order disabled.




Case Study:

This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.
To start the case study
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs.
When you are ready to answer a question, click the Question button to return to the question.

Overview:

Litware, Inc. is a publishing company that has an online bookstore and several retail bookstores worldwide. Litware also manages an online advertising business for the authors it represents.

Existing Environment. Fabric Environment
Litware has a Fabric workspace named Workspace1. High concurrency is enabled for Workspace1.
The company has a data engineering team that uses Python for data processing.

Existing Environment. Data Processing
The retail bookstores send sales data at the end of each business day, while the online bookstore constantly provides logs and sales data to a central enterprise resource planning (ERP) system.
Litware implements a medallion architecture by using the following three layers: bronze, silver, and gold. The sales data is ingested from the ERP system as Parquet files that land in the Files folder in a lakehouse. Notebooks are used to transform the files in a Delta table for the bronze and silver layers. The gold layer is in a warehouse that has V-Order disabled.
Litware has image files of book covers in Azure Blob Storage. The files are loaded into the Files folder.

Existing Environment. Sales Data
Month-end sales data is processed on the first calendar day of each month. Data that is older than one month never changes.
In the source system, the sales data refreshes every six hours starting at midnight each day.
The sales data is captured in a Dataflow Gen2 dataflow.
When the dataflow runs, new and historical data is captured. The dataflow captures the following fields of the source:
Sales Date
Author
Price
Units
SKU
A table named AuthorSales stores the sales data that relates to each author. The table contains a column named AuthorEmail. Authors authenticate to a guest Fabric tenant by using their email address.

Existing Environment. Security Groups
Litware has the following security groups:
Sales
Fabric Admins
Streaming Admins

Existing Environment. Performance Issues
Business users perform ad-hoc queries against the warehouse. The business users indicate that reports against the warehouse sometimes run for two hours and fail to load as expected. Upon further investigation, the data engineering team receives the following error message when the reports fail to load: "The SQL query failed while running."
The data engineering team wants to debug the issue and find queries that cause more than one failure.
When the authors have new book releases, there is often an increase in sales activity. This increase slows the data ingestion process.
The company's sales team reports that during the last month, the sales data has NOT been up-to-date when they arrive at work in the morning.

Requirements. Planned Changes
Litware recently signed a contract to receive book reviews. The provider of the reviews exposes the data in Amazon Simple Storage Service (Amazon S3) buckets.
Litware plans to manage Search Engine Optimization (SEO) for the authors. The SEO data will be streamed from a REST API.

Requirements. Version Control
Litware plans to implement a version control solution in Fabric that will use GitHub integration and follow the principle of least privilege.

Requirements. Governance Requirements
To control data platform costs, the data platform must use only Fabric services and items. Additional Azure resources must NOT be provisioned.

Requirements. Data Requirements
Litware identifies the following data requirements:
Process the SEO data in near-real-time (NRT).
Make the book reviews available in the lakehouse without making a copy of the data.
When a new book cover image arrives in the Files folder, process the image as soon as possible.

DRAG DROP (Drag and Drop is not supported)
You need to ensure that the authors can see only their respective sales data.
How should you complete the statement? To answer, drag the appropriate values the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
Note: Each correct selection is worth one point.
Select and Place:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



Box 1: SCHEMABINDING
CREATE FUNCTION (Azure Synapse Analytics and Microsoft Fabric) Creates a user-defined function (UDF) in Azure Synapse Analytics, Analytics Platform System (PDW), or Microsoft Fabric. A user-defined function is a Transact-SQL routine that accepts parameters, performs an action, such as a complex calculation, and returns the result of that action as a value.
In Microsoft Fabric and serverless SQL pools in Azure Synapse Analytics, CREATE FUNCTION can create inline table-value functions but not scalar functions. User-defined table-valued functions (TVFs) return a table data type.
Inline table-valued function syntax
-- Transact-SQL Inline Table-Valued Function Syntax
-- Preview in dedicated SQL pools in Azure Synapse Analytics -- Available in the serverless SQL pools in Azure Synapse Analytics and Microsoft Fabric CREATE FUNCTION [ schema_name. ] function_name
( [ { @parameter_name [ AS ] parameter_data_type
[ = default ] }
[ ,...n ]
]
)
RETURNS TABLE
[ WITH SCHEMABINDING ]
[ AS ]
RETURN [ ( ] select_stmt [ ) ]
[ ; ]
--
SCHEMABINDING
Specifies that the function is bound to the database objects that it references.
When SCHEMABINDING is specified, the base objects cannot be modified in a way that would affect the function definition. The function definition itself must first be modified or dropped to remove dependencies on the object that is to be modified.
Box 2: USER_NAME()
USER_NAME (Transact-SQL)
Returns a database user name from a specified identification number, or the current user name.
Box 3: AuthorSales
We specify the table name, table_schema_name.table_name below.
.
Scenario:
Litware also manages an online advertising business for the authors it represents.

Existing Environment. Sales Data
A table named AuthorSales stores the sales data that relates to each author. The table contains a column named AuthorEmail. Authors authenticate to a guest Fabric tenant by using their email address.
Note: CREATE SECURITY POLICY (Transact-SQL)
Create a security policy
The following syntax creates a security policy with a filter predicate for the dbo.Customer table, and leaves the security policy disabled.
SQL
CREATE SECURITY POLICY [FederatedSecurityPolicy]
ADD FILTER PREDICATE [rls].[fn_securitypredicate]([CustomerId]) ON [dbo].[Customer];
--
table_schema_name.table_name
Is the target table to which the security predicate will be applied. Multiple disabled security policies can target a single table for a particular DML operation, but only one can be enabled at any given time.
Full Syntax:
CREATE SECURITY POLICY [schema_name. ] security_policy_name
{ ADD [ FILTER | BLOCK ] } PREDICATE tvf_schema_name.security_predicate_function_name ( { column_name | expression } [ , ...n] ) ON table_schema_name. table_name [ <block_dml_operation> ] , [ , ...n]
[ WITH ( STATE = { ON | OFF } [,] [ SCHEMABINDING = { ON | OFF } ] ) ] [ NOT FOR REPLICATION ]
[;]




Case Study:

This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.
To start the case study
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs.
When you are ready to answer a question, click the Question button to return to the question.

Overview:
- Company Overview
Contoso, Ltd. is an online retail company that wants to modernize its analytics platform by moving to Fabric. The company plans to begin using Fabric for marketing analytics.

Overview:
- IT Structure
The company's IT department has a team of data analysts and a team of data engineers that use analytics systems.
The data engineers perform the ingestion, transformation, and loading of data. They prefer to use Python or SQL to transform the data.
The data analysts query data and create semantic models and reports. They are qualified to write queries in Power Query and T-SQL.

Existing Environment. Fabric
Contoso has an F64 capacity named Cap1. All Fabric users are allowed to create items.
Contoso has two workspaces named WorkspaceA and WorkspaceB that currently use Pro license mode.

Existing Environment. Source Systems
Contoso has a point of sale (POS) system named POS1 that uses an instance of SQL Server on Azure Virtual Machines in the same Microsoft Entra tenant as Fabric. The host virtual machine is on a private virtual network that has public access blocked. POS1 contains all the sales transactions that were processed on the company's website.
The company has a software as a service (SaaS) online marketing app named MAR1. MAR1 has seven entities. The entities contain data that relates to email open rates and interaction rates, as well as website interactions. The data can be exported from MAR1 by calling REST APIs. Each entity has a different endpoint.
Contoso has been using MAR1 for one year. Data from prior years is stored in Parquet files in an Amazon Simple Storage Service (Amazon S3) bucket. There are 12 files that range in size from 300 MB to 900 MB and relate to email interactions.

Existing Environment. Product Data
POS1 contains a product list and related data. The data comes from the following three tables:
Products
ProductCategories
ProductSubcategories
In the data, products are related to product subcategories, and subcategories are related to product categories.

Existing Environment. Azure
Contoso has a Microsoft Entra tenant that has the following mail-enabled security groups:
DataAnalysts: Contains the data analysts
DataEngineers: Contains the data engineers
Contoso has an Azure subscription.
The company has an existing Azure DevOps organization and creates a new project for repositories that relate to Fabric.

Existing Environment. User Problems
The VP of marketing at Contoso requires analysis on the effectiveness of different types of email content. It typically takes a week to manually compile and analyze the data. Contoso wants to reduce the time to less than one day by using Fabric.
The data engineering team has successfully exported data from MAR1. The team experiences transient connectivity errors, which causes the data exports to fail.

Requirements. Planned Changes
Contoso plans to create the following two lakehouses:
Lakehouse1: Will store both raw and cleansed data from the sources
Lakehouse2: Will serve data in a dimensional model to users for analytical queries
Additional items will be added to facilitate data ingestion and transformation.
Contoso plans to use Azure Repos for source control in Fabric.

Requirements. Technical Requirements
The new lakehouses must follow a medallion architecture by using the following three layers: bronze, silver, and gold. There will be extensive data cleansing required to populate the MAR1 data in the silver layer, including deduplication, the handling of missing values, and the standardizing of capitalization.
Each layer must be fully populated before moving on to the next layer. If any step in populating the lakehouses fails, an email must be sent to the data engineers.
Data imports must run simultaneously, when possible.
The use of email data from the Amazon S3 bucket must meet the following requirements:
Minimize egress costs associated with cross-cloud data access.
Prevent saving a copy of the raw data in the lakehouses.
Items that relate to data ingestion must meet the following requirements:
The items must be source controlled alongside other workspace items.
Ingested data must land in the bronze layer of Lakehouse1 in the Delta format.
No changes other than changes to the file formats must be implemented before the data lands in the bronze layer.
Development effort must be minimized and a built-in connection must be used to import the source data.
In the event of a connectivity error, the ingestion processes must attempt the connection again.
Lakehouses, data pipelines, and notebooks must be stored in WorkspaceA. Semantic models, reports, and dataflows must be stored in WorkspaceB.
Once a week, old files that are no longer referenced by a Delta table log must be removed.

Requirements. Data Transformation
In the POS1 product data, ProductID values are unique. The product dimension in the gold layer must include only active products from product list. Active products are identified by an IsActive value of 1.
Some product categories and subcategories are NOT assigned to any product. They are NOT analytically relevant and must be omitted from the product dimension in the gold layer.

Requirements. Data Security
Security in Fabric must meet the following requirements:
The data engineers must have read and write access to all the lakehouses, including the underlying files.
The data analysts must only have read access to the Delta tables in the gold layer.
The data analysts must NOT have access to the data in the bronze and silver layers.
The data engineers must be able to commit changes to source control in WorkspaceA.

You need to ensure that the data analysts can access the gold layer lakehouse.
What should you do?

  1. Add the DataAnalyst group to the Viewer role for Workspace
  2. Share the lakehouse with the DataAnalysts group and grant the Build reports on the default semantic model permission.
  3. Share the lakehouse with the DataAnalysts group and grant the Read all SQL Endpoint data permission.
  4. Share the lakehouse with the DataAnalysts group and grant the Read all Apache Spark permission.

Answer(s): C

Explanation:

Data Analysts' Access Requirements must only have read access to the Delta tables in the gold layer and not have access to the bronze and silver layers.
The gold layer data is typically queried via SQL Endpoints. Granting the Read all SQL Endpoint data permission allows data analysts to query the data using familiar SQL-based tools while restricting access to the underlying files.




Case Study:

This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.
To start the case study
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs.
When you are ready to answer a question, click the Question button to return to the question.

Overview:
- Company Overview
Contoso, Ltd. is an online retail company that wants to modernize its analytics platform by moving to Fabric. The company plans to begin using Fabric for marketing analytics.

Overview:
- IT Structure
The company's IT department has a team of data analysts and a team of data engineers that use analytics systems.
The data engineers perform the ingestion, transformation, and loading of data. They prefer to use Python or SQL to transform the data.
The data analysts query data and create semantic models and reports. They are qualified to write queries in Power Query and T-SQL.

Existing Environment. Fabric
Contoso has an F64 capacity named Cap1. All Fabric users are allowed to create items.
Contoso has two workspaces named WorkspaceA and WorkspaceB that currently use Pro license mode.

Existing Environment. Source Systems
Contoso has a point of sale (POS) system named POS1 that uses an instance of SQL Server on Azure Virtual Machines in the same Microsoft Entra tenant as Fabric. The host virtual machine is on a private virtual network that has public access blocked. POS1 contains all the sales transactions that were processed on the company's website.
The company has a software as a service (SaaS) online marketing app named MAR1. MAR1 has seven entities. The entities contain data that relates to email open rates and interaction rates, as well as website interactions. The data can be exported from MAR1 by calling REST APIs. Each entity has a different endpoint.
Contoso has been using MAR1 for one year. Data from prior years is stored in Parquet files in an Amazon Simple Storage Service (Amazon S3) bucket. There are 12 files that range in size from 300 MB to 900 MB and relate to email interactions.

Existing Environment. Product Data
POS1 contains a product list and related data. The data comes from the following three tables:
Products
ProductCategories
ProductSubcategories
In the data, products are related to product subcategories, and subcategories are related to product categories.

Existing Environment. Azure
Contoso has a Microsoft Entra tenant that has the following mail-enabled security groups:
DataAnalysts: Contains the data analysts
DataEngineers: Contains the data engineers
Contoso has an Azure subscription.
The company has an existing Azure DevOps organization and creates a new project for repositories that relate to Fabric.

Existing Environment. User Problems
The VP of marketing at Contoso requires analysis on the effectiveness of different types of email content. It typically takes a week to manually compile and analyze the data. Contoso wants to reduce the time to less than one day by using Fabric.
The data engineering team has successfully exported data from MAR1. The team experiences transient connectivity errors, which causes the data exports to fail.

Requirements. Planned Changes
Contoso plans to create the following two lakehouses:
Lakehouse1: Will store both raw and cleansed data from the sources
Lakehouse2: Will serve data in a dimensional model to users for analytical queries
Additional items will be added to facilitate data ingestion and transformation.
Contoso plans to use Azure Repos for source control in Fabric.

Requirements. Technical Requirements
The new lakehouses must follow a medallion architecture by using the following three layers: bronze, silver, and gold. There will be extensive data cleansing required to populate the MAR1 data in the silver layer, including deduplication, the handling of missing values, and the standardizing of capitalization.
Each layer must be fully populated before moving on to the next layer. If any step in populating the lakehouses fails, an email must be sent to the data engineers.
Data imports must run simultaneously, when possible.
The use of email data from the Amazon S3 bucket must meet the following requirements:
Minimize egress costs associated with cross-cloud data access.
Prevent saving a copy of the raw data in the lakehouses.
Items that relate to data ingestion must meet the following requirements:
The items must be source controlled alongside other workspace items.
Ingested data must land in the bronze layer of Lakehouse1 in the Delta format.
No changes other than changes to the file formats must be implemented before the data lands in the bronze layer.
Development effort must be minimized and a built-in connection must be used to import the source data.
In the event of a connectivity error, the ingestion processes must attempt the connection again.
Lakehouses, data pipelines, and notebooks must be stored in WorkspaceA. Semantic models, reports, and dataflows must be stored in WorkspaceB.
Once a week, old files that are no longer referenced by a Delta table log must be removed.

Requirements. Data Transformation
In the POS1 product data, ProductID values are unique. The product dimension in the gold layer must include only active products from product list. Active products are identified by an IsActive value of 1.
Some product categories and subcategories are NOT assigned to any product. They are NOT analytically relevant and must be omitted from the product dimension in the gold layer.

Requirements. Data Security
Security in Fabric must meet the following requirements:
The data engineers must have read and write access to all the lakehouses, including the underlying files.
The data analysts must only have read access to the Delta tables in the gold layer.
The data analysts must NOT have access to the data in the bronze and silver layers.
The data engineers must be able to commit changes to source control in WorkspaceA.

You need to ensure that WorkspaceA can be configured for source control.
Which two actions should you perform? Each correct answer presents part of the solution.
Note: Each correct selection is worth one point.

  1. From Tenant setting, set Users can synchronize workspace items with their Git repositories to Enabled.
  2. From Tenant setting, set Users can sync workspace items with GitHub repositories to Enabled.
  3. Configure WorkspaceA to use a Premium Per User (PPU) license.
  4. Assign WorkspaceA to Cap1.

Answer(s): A,D

Explanation:

A F64 capacity is a Microsoft Fabric Capacity (Pay-as-you-go or Reservation).
Note: Microsoft Fabric, Get started with Git integration
Prerequisites
To integrate Git with your Microsoft Fabric workspace, you need to set up the following prerequisites for both Fabric and Git.
Fabric prerequisites
To access the Git integration feature, you need a Fabric capacity. A Fabric capacity is required to use all supported Fabric items [D]. If you don't have one yet, sign up for a free trial. Customers that already have a Power BI Premium capacity, can use that capacity, but keep in mind that certain Power BI SKUs only support Power BI items.
In addition, the following tenant switches must be enabled from the Admin portal:
* Users can create Fabric items
*-> Users can synchronize workspace items with their Git repositories [A, not B] For GitHub users only: Users can synchronize workspace items with GitHub repositories Scenario:

Existing Environment. Fabric
Contoso has an F64 capacity named Cap1 [D]. All Fabric users are allowed to create items.
Contoso has two workspaces named WorkspaceA and WorkspaceB that currently use Pro license mode.

Requirements. Planned Changes
Contoso plans to use Azure Repos for source control in Fabric.

Requirements. Technical Requirements
Items that relate to data ingestion must meet the following requirements:
*-> The items must be source controlled alongside other workspace items.

Requirements. Data Security
Security in Fabric must meet the following requirements:
*-> The data engineers must be able to commit changes to source control in WorkspaceA.



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?

  1. a lakehouse
  2. an eventhouse
  3. a datamart
  4. a warehouse

Answer(s): A

Explanation:

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.



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Share your comments for Microsoft DP-700 exam with other users:

asad Raza 5/15/2023 5:38:00 AM

please upload this exam
CHINA


Reeta 7/17/2023 5:22:00 PM

please upload the c_activate22 dump questions with answer
SWEDEN


Wong 12/20/2023 11:34:00 AM

q10 - the answer should be a. if its c, the criteria will meet if either the prospect is not part of the suppression lists or if the job title contains vice president
MALAYSIA


david 12/12/2023 12:38:00 PM

this was on the exam as of 1211/2023
Anonymous


Tink 7/24/2023 9:23:00 AM

great for prep
GERMANY


Jaro 12/18/2023 3:12:00 PM

i think in question 7 the first answer should be power bi portal (not power bi)
Anonymous


9eagles 4/7/2023 10:04:00 AM

on question 10 and so far 2 wrong answers as evident in the included reference link.
Anonymous


Tai 8/28/2023 5:28:00 AM

wonderful material
SOUTH AFRICA


VoiceofMidnight 12/29/2023 4:48:00 PM

i passed!! ...but barely! got 728, but needed 720 to pass. the exam hit me with labs right out of the gate! then it went to multiple choice. protip: study the labs!
UNITED STATES


A K 8/3/2023 11:56:00 AM

correct answer for question 92 is c -aws shield
Anonymous


Nitin Mindhe 11/27/2023 6:12:00 AM

great !! it is really good
IRELAND


BailleyOne 11/22/2023 1:45:00 AM

explanations for the answers are to the point.
Anonymous


patel 10/25/2023 8:17:00 AM

how can rea next
INDIA


MortonG 10/19/2023 6:32:00 PM

question: 128 d is the wrong answer...should be c
EUROPEAN UNION


Jayant 11/2/2023 3:15:00 AM

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Anonymous


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UNITED STATES


hello 10/31/2023 12:07:00 PM

good content
Anonymous


Matheus 9/3/2023 2:14:00 PM

just testing if the comments are real
UNITED STATES


yenvti2@gmail.com 8/12/2023 7:56:00 PM

very helpful for exam preparation
Anonymous


Miguel 10/5/2023 12:16:00 PM

question 11: https://help.salesforce.com/s/articleview?id=sf.admin_lead_to_patient_setup_overview.htm&type=5
SPAIN


Noushin 11/28/2023 4:52:00 PM

i think the answer to question 42 is b not c
CANADA


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Anonymous


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Anonymous


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GERMANY


suresh 12/16/2023 10:54:00 PM

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Anonymous


Wali 6/4/2023 10:07:00 PM

thank you for making the questions and answers intractive and selectable.
UNITED STATES


Nawaz 7/18/2023 1:10:00 AM

answers are correct?
UNITED STATES


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INDIA


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INDIA


jaya 12/17/2023 8:36:00 AM

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UNITED STATES


Bsmaind 8/20/2023 9:23:00 AM

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Anonymous


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Anonymous


Vetri 10/3/2023 12:59:00 AM

great content and wonderful to have the answers with explanation
UNITED STATES


Ranjith 8/21/2023 3:39:00 PM

for question #118, the answer is option c. the screen shot is showing the drop down, but the answer is marked incorrectly please update . thanks for sharing such nice questions.
Anonymous