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Microsoft DP-700

Implementing Data Engineering Solutions Using Microsoft Fabric

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Question 1 of 161

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?

Answer choices
Show answer

Correct answer: C

Sharing the lakehouse with the DataAnalysts group and granting Read all SQL Endpoint data lets analysts query the Delta tables through the lakehouse SQL analytics endpoint. This provides read-only access to the gold-layer tables without granting access to the underlying OneLake files. Viewer access to WorkspaceA would expose workspace items and potentially data outside the gold layer, while Apache Spark permissions are intended for Spark-based access rather than analyst SQL queries.

Community votes

33 votes

CSuggested

100%

Question 2 of 161

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?

Answer choices
Show answer

Correct answer: A

A lakehouse stores data in OneLake using open formats that Apache Spark can write efficiently. Its tables can then be queried through the SQL analytics endpoint with T-SQL and can also be accessed by KQL when appropriate Fabric experiences are used. This makes it suitable for semi-structured data that requires multi-engine access. A warehouse is primarily optimized for T-SQL workloads, while an eventhouse is designed mainly for real-time event and KQL scenarios.

Community votes

175 votes

ASuggested

53%

B

47%

Question 3 of 161

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 using an on-premises data gateway.

You need to copy data from Database1 to Warehouse1.

Which item should you use?

Answer choices
Show answer

Correct answer: B

A data pipeline copies data from an on-premises SQL Server source to a Fabric warehouse by using the configured on-premises data gateway. Its Copy Data activity supports connecting to Database1, selecting source tables or queries, and loading the results into Warehouse1. Data pipelines are designed for orchestrated data movement and can also schedule, monitor, and retry the transfer. A KQL queryset is for querying KQL databases, while a notebook is primarily for code-based transformation and analysis rather than the standard gateway-based copy workflow.

Community votes

41 votes

BSuggested

100%

Question 4 of 161

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 using an on-premises data gateway.

You need to copy data from Database1 to Warehouse1.

Which item should you use?

Answer choices
Show answer

Correct answer: B

A data pipeline is used to orchestrate and run copy activities between data sources and destinations in Microsoft Fabric. It can connect to an on-premises SQL Server database through an on-premises data gateway and load the extracted data into a Fabric warehouse. Spark job definitions are intended for executing Spark code, while eventstreams process real-time streaming events rather than performing scheduled or batch database copies.

Community votes

24 votes

BSuggested

100%

Question 5 of 161

You have a Fabric F32 capacity that contains a workspace. The workspace contains a warehouse named 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?

Answer choices
Show answer

Correct answer: B

Increasing the Fabric capacity provides more compute and memory resources for the warehouse and Direct Lake semantic model workload. As the fact table grows substantially, year-over-year calculations require more data to be scanned and processed, which can cause slower visuals and capacity-related errors on an F32 SKU. A higher capacity delivers the best query performance while avoiding redesign of the existing surrogate keys or creation of additional objects. Changing hash algorithms, key data types, or views does not address the underlying resource limitation.

Community votes

197 votes

BSuggested

40%

C

39%

D

19%

E

2%

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