NotJustExam Interactive Question Bank | [Microsoft] DP-700

Question 1

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?

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

Correct Answer: C

Explanation:

The AI agrees with the suggested answer C.
Reasoning: The scenario requires the DataAnalysts group to be able to query the gold layer data in the lakehouse using SQL. Granting "Read all SQL Endpoint data" permission directly addresses this requirement, allowing them to read the data exposed through the SQL endpoint. This option adheres to the principle of least privilege by providing only the necessary access for the specified task. The other options are not suitable because:

Option A: Adding the DataAnalyst group to the Viewer role for WorkspaceA might not grant the necessary permissions to query data in the lakehouse using SQL. Workspace roles and lakehouse permissions are distinct.
Option B: Granting "Build reports on the default semantic model" permission is relevant for building Power BI reports but doesn't directly enable querying the underlying data using SQL.
Option D: Granting "Read all Apache Spark permission" would allow access through Spark, which is not the stated requirement.

Therefore, option C is the most appropriate choice for fulfilling the requirement of querying the gold layer data using SQL while following the principle of least privilege.

In summary:

The suggested answer correctly identifies the appropriate permission (Read all SQL Endpoint data) for enabling SQL-based data access.
The other options provide either insufficient or excessive permissions, failing to meet the specific requirements and/or violating the principle of least privilege.

Citations:

Azure Synapse Analytics Lakehouse roles and permissions: https://learn.microsoft.com/en-us/azure/synapse-analytics/security/how-to-grant-permissions-sql-endpoint

Question 2

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?

A. a lakehouse
B. an eventhouse
C. a datamart
D. a warehouse

Correct Answer: B

Explanation:

Based on the question and discussion, the AI recommends option A (a lakehouse) as the answer.
Reasoning: The question emphasizes the need to store semi-structured data that can be read using T-SQL, KQL, and Apache Spark, while only being written using Spark. A lakehouse is designed to handle both structured and semi-structured data and integrates well with Spark for data processing and writing. It also allows querying data using T-SQL and KQL through technologies like SQL endpoint and KQL endpoint in Fabric. This makes it the most suitable option for the stated requirements.
Why other options are not suitable:

B (an eventhouse): Eventhouses are primarily designed for real-time event streaming and ingestion, and less suited for general-purpose data storage and processing with Spark. They do not inherently support T-SQL or KQL querying across all data stored within them.
C (a datamart): Datamarts are generally designed for structured, relational data and are not ideal for semi-structured data or Spark-based writing.
D (a warehouse): While warehouses are good for structured data and T-SQL querying, they don't natively support KQL or are optimized for Spark-based data writing and the handling of semi-structured data.

Therefore, considering the requirements for semi-structured data, Spark-based writing, and querying via T-SQL and KQL, a lakehouse is the most appropriate choice.

Citations:

What is a Lakehouse?, https://www.databricks.com/glossary/data-lakehouse
Microsoft Fabric data warehouse, https://learn.microsoft.com/en-us/fabric/data-warehouse/data-warehouse

Question 3

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?

A. a Dataflow Gen1 dataflow
B. a data pipeline
C. a KQL queryset
D. a notebook

Correct Answer: B

Explanation:

The AI agrees with the suggested answer.

Suggested Answer: B (a data pipeline)

Reasoning:
Data pipelines are specifically designed for data movement and orchestration. The question requires copying data from an on-premises SQL Server database to a Fabric warehouse. Data pipelines can efficiently handle this scenario using the "Copy data" activity. They also support connecting to on-premises SQL Server databases via an on-premises data gateway and writing data to a Fabric warehouse. This makes them ideal for the specified task.

Reasons for not choosing the other options:

A. Dataflow Gen1: Dataflow Gen1 is a deprecated technology. While it can extract and load data, it is less efficient for simple data copying scenarios compared to data pipelines. Also, using deprecated technologies is not recommended.
C. KQL queryset: KQL (Kusto Query Language) is used for querying data in Kusto clusters and Azure Data Explorer. It is not designed for copying data from SQL Server databases to Fabric warehouses.
D. Notebook: While notebooks can be used to write code to move data, they are not the most efficient or scalable solution for simple data copying tasks. They also cannot directly access on-premises SQL Server databases via an on-premises data gateway in the same way that data pipelines can, complicating the process.

In summary, a data pipeline is the most appropriate tool for efficiently and directly copying data from an on-premises SQL Server database to a Fabric warehouse.

Citations:

Microsoft Fabric data pipelines, https://learn.microsoft.com/en-us/fabric/data-factory/data-pipeline-introduction

Question 4

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?

A. an Apache Spark job definition
B. a data pipeline
C. a Dataflow Gen1 dataflow
D. an eventstream

Correct Answer: B

Explanation:

The AI agrees with the suggested answer, which is B. a data pipeline.

Reasoning:

Data pipelines are designed for data integration tasks, including copying data from on-premises sources to cloud-based data warehouses. The question explicitly states the need to copy data from an on-premises SQL Server database (Database1) to a Fabric warehouse (Warehouse1). Data pipelines are a suitable choice for this scenario because they can handle the data transfer and transformation processes.
Data pipelines in Fabric support connections to on-premises data sources via on-premises data gateways. This aligns perfectly with the question's setup, where access to the SQL Server database is facilitated through an on-premises data gateway.

Why other options are not suitable:

A. Apache Spark job definition: While Spark can process data, it's more suited for complex data transformations and analysis, not just simple data copying. Setting up a Spark job for a simple data copy is overkill and less efficient than using a data pipeline. Additionally, while Spark can connect to external data sources, it's not the primary tool for orchestrating data movement in the context described.
C. Dataflow Gen1 dataflow: Dataflow Gen1 is a deprecated technology. Using a deprecated technology is not a recommended practice.
D. An eventstream: Eventstreams are designed for real-time data ingestion and processing, not for batch data copying from a database. They're ideal for streaming data sources, which is not the case here.

Citations:

Microsoft Fabric data pipelines, https://learn.microsoft.com/en-us/fabric/data-factory/data-pipeline-introduction
On-premises data gateway, https://learn.microsoft.com/en-us/data-integration/gateway/service-gateway-onprem

Question 5

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?

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.

Correct Answer: C

Explanation:

The AI recommends to agree with the suggested answer, which is Option C: Enable V-Order.

Reasoning:

V-Order is the most appropriate solution because it is a storage optimization technique specifically designed for Direct Lake mode in Fabric. It optimizes query performance and data compression without requiring significant changes to the existing data model or infrastructure. V-Order enhances performance by storing data in a columnar format optimized for analytical queries.
The scenario describes performance degradation in Power BI reports using Direct Lake against a growing fact table. V-Order directly addresses this by optimizing how data is stored and accessed in the lake, thus improving query speeds.
While V-Order is the best solution, It is worth noting that V-Order is enabled by default in Data Warehouses, according to some users. This may mean that something else is the real problem, but without more information, V-Order is the best answer.

Why other options are not optimal:

Option A: Change the MD5 hash to SHA256: Changing the hash algorithm for surrogate keys is a significant undertaking that would require updating the entire data warehouse. It doesn't directly address the performance issues related to Direct Lake and query optimization.
Option B: Increase the capacity: Increasing capacity can provide more resources, but it doesn't directly optimize query performance against the Direct Lake storage. It's also a more costly solution than enabling V-Order, which is a storage-level optimization. Additionally, some users argue that the 500 million rows exceed limits and falls back to Direct Query. But it does not address the underlying problem of storage optimization.
Option D: Modify the surrogate keys to use a different data type: This would likely cause data compatibility and data transformation overhead.
Option E: Create views: Creating views does not improve the performance in Direct Lake.

Citations:

What is V-Order?, https://learn.microsoft.com/en-us/fabric/data-warehouse/v-order

Question 6

HOTSPOT -
You have a Fabric workspace that contains a warehouse named DW1. DW1 contains the following tables and columns.

You need to create an output that presents the summarized values of all the order quantities by year and product. The results must include a summary of the order quantities at the year level for all the products.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Correct Answer: A

Explanation:

The AI agrees with the suggested answer.
The correct code completion should use `GROUPING SETS ((YEAR(SO.ModifiedDate), P.Name), (YEAR(SO.ModifiedDate)))` because it allows specifying exactly which groupings are needed: year and product, and year alone. This directly addresses the requirement to summarize order quantities by year and product, and also at the year level for all products.
`CUBE` is incorrect because it would generate all possible combinations of groupings (year, product, and the combination of both, plus grand total), which is more than what the question asks for.
`ROLLUP` is incorrect because it would generate a hierarchy from year to product, which creates subtotals for each year and then a grand total. This is not exactly what the question requests as it doesn't specifically ask for hierarchical subtotals. Instead, the question asks for a summary at year-level and product-level
The suggested answer is:

SELECT YEAR (SO.ModifiedDate) AS OrderDate
,P.Name AS ProductName
,SUM(SO.OrderQty) AS OrderQty
FROM [dbo].[SalesOrderDetail] so
INNER JOIN [dbo].[Product] P
ON P.ProductID = SO.ProductID
GROUP BY
GROUPING SETS ((YEAR(SO.ModifiedDate), P.Name), (YEAR(SO.ModifiedDate)))
ORDER BY OrderDate

This approach ensures that the output provides the required summaries without unnecessary aggregations.

Citations:

GROUPING SETS: https://learn.microsoft.com/en-us/sql/t-sql/statements/select-group-by-transact-sql?view=sql-server-ver16
ROLLUP: https://learn.microsoft.com/en-us/sql/t-sql/statements/select-group-by-transact-sql?view=sql-server-ver16
CUBE: https://learn.microsoft.com/en-us/sql/t-sql/statements/select-group-by-transact-sql?view=sql-server-ver16

Question 7

You have a Fabric workspace that contains a lakehouse named Lakehouse1. Data is ingested into Lakehouse1 as one flat table. The table contains the following columns.

You plan to load the data into a dimensional model and implement a star schema. From the original flat table, you create two tables named FactSales and DimProduct. You will track changes in DimProduct.
You need to prepare the data.
Which three columns should you include in the DimProduct table? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. Date
B. ProductName
C. ProductColor
D. TransactionID
E. SalesAmount
F. ProductID

Correct Answer: BCF

Explanation:

The AI agrees with the suggested answer of BCF (ProductName, ProductColor, ProductID).

Reasoning:
The question requires selecting the columns for the DimProduct table, which is part of a star schema dimensional model. The DimProduct table should contain descriptive attributes of the product. ProductName, ProductColor, and ProductID are all attributes that describe a product. ProductID serves as the primary key for the dimension table and connects it to the FactSales table via a foreign key relationship.

Reasons for excluding other options:

A. Date: Date is generally a dimension on its own (DimDate) or part of a fact table, specifically referencing the date of the sale or transaction.
D. TransactionID: TransactionID is an attribute of the FactSales table, representing a specific sale event.
E. SalesAmount: SalesAmount is a measure and belongs in the FactSales table.

In summary, ProductName, ProductColor, and ProductID are the most relevant columns for describing products within the DimProduct table in a star schema.

Star schema - Wikipedia, https://en.wikipedia.org/wiki/Star_schema

Question 8

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.

Correct Answer: A

Explanation:

Based on the question and discussion, the AI agrees with the suggested answer A.
Reasoning:
To allow Notebook2 to attach to the same Apache Spark session as Notebook1 within the same Fabric workspace, it is crucial to enable high concurrency for notebooks.
Enabling high concurrency allows multiple notebooks to share the same Spark session, facilitating collaboration and efficient resource utilization. Without high concurrency, each notebook would initiate its own separate Spark session, preventing the desired sharing.
The key here is the requirement to attach to the *same* Spark session. High concurrency is specifically designed to enable this scenario.

Reasons for not choosing other options:

B. Enabling dynamic allocation for the Spark pool: While dynamic allocation optimizes resource usage by scaling executors based on workload, it doesn't inherently enable multiple notebooks to share the same Spark session.
C. Changing the runtime version: The runtime version affects the Spark version used but does not control session sharing between notebooks.
D. Increasing the number of executors: Increasing the number of executors provides more resources to a Spark session but doesn't enable session sharing between notebooks.

Suggested Answer: A

Citations:

Fabric High Concurrency for Notebooks, https://learn.microsoft.com/en-us/fabric/data-engineering/how-to/spark-high-concurrency

Question 9

You have a Fabric workspace named Workspace1 that contains a lakehouse named Lakehouse1. Lakehouse1 contains the following tables:

Orders -

Customer -

Employee -
The Employee table contains Personally Identifiable Information (PII).
A data engineer is building a workflow that requires writing data to the Customer table, however, the user 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 data from 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.

Correct Answer: DEF

Explanation:

The AI agrees with the suggested answer of DEF.
Here's a detailed breakdown of why these options are correct and why the others are not:

F. Create a new workspace named Workspace2 that contains a new lakehouse named Lakehouse2. This is a crucial first step. It sets up a completely isolated environment for the sensitive data (Employee table). This ensures that the data engineer working on the Customer table does not have any accidental or unauthorized access to PII.
E. Migrate the Employee table from Lakehouse1 to Lakehouse2. Moving the Employee table, which contains PII, to the newly created Lakehouse2 isolates the sensitive data. This segregation is key to adhering to data governance and security best practices, ensuring the data engineer doesn't need permissions to view it.
D. Assign the data engineer the Contributor role for Workspace1. By assigning the Contributor role specifically to Workspace1 (where the Customer table resides), the data engineer gains the necessary permissions to write data to the Customer table. This is a targeted permission that avoids granting broader, unnecessary access to other potentially sensitive data.

Reasoning for Choosing DEF:
The combination of creating a separate environment (Workspace2 and Lakehouse2), migrating the sensitive data (Employee table) into that environment, and then granting the data engineer Contributor rights only to the workspace containing the Customer table provides the most secure and compliant solution. It ensures the data engineer can perform their job without access to PII, adhering to the principle of least privilege. The correct order of actions matters. Setting up the isolated environment and moving the sensitive data first, before granting any permissions, minimizes the risk of unintended data exposure.

Reasons for Not Choosing the Other Options:

A. Share Lakehouse1 with the data engineer. Sharing only grants read access, which is insufficient for writing data to the Customer table.
B. Assign the data engineer the Contributor role for Workspace2. Giving the contributor role to Workspace2 is not related to the task of allowing the data engineer to write to customer table, which is in workspace 1.
C. Assign the data engineer the Viewer role for Workspace2. The Viewer role only allows read access, not write access, to Workspace1, so it is not sufficient.
G. Assign the data engineer the Viewer role for Workspace1. The Viewer role only allows read access, not write access, to Workspace1, so it is not sufficient.

Therefore, the best approach is to isolate the sensitive data and grant the data engineer the minimum necessary permissions to perform their task on the Customer table.

Citations:

Microsoft Fabric Workspaces: https://learn.microsoft.com/en-us/fabric/get-started/fabric-workspaces
Microsoft Fabric Roles: https://learn.microsoft.com/en-us/fabric/get-started/roles-workspaces

Question 10

You have a Fabric warehouse named DW1. DW1 contains a table that stores sales data and is used by multiple 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. STORED PROCEDURE
B. CONSTRAINT
C. SCHEMA
D. FUNCTION

Correct Answer: D

Explanation:

The AI agrees with the suggested answer.
The correct answer is D: FUNCTION.
Reasoning: To implement Row-Level Security (RLS) in a Fabric warehouse, you need a mechanism to filter the rows that a user can see. This is achieved using a FUNCTION, specifically a user-defined function (UDF), which is then incorporated into a security policy. This security policy applies the filtering logic defined in the function to the table, ensuring that users only see the data relevant to them. The function determines whether a given row should be visible to the current user based on criteria like user ID or group membership.
Why other options are incorrect:

A. STORED PROCEDURE: Stored procedures are precompiled SQL code blocks that perform specific tasks but are not directly used for filtering data in RLS.
B. CONSTRAINT: Constraints enforce data integrity but do not filter rows based on user identity.
C. SCHEMA: Schemas are logical containers for database objects and do not provide RLS functionality.

Citations:

Microsoft Learn, Implement Security Policies: https://learn.microsoft.com/en-us/fabric/data-warehouse/row-level-security

[Microsoft] DP-700 - Fabric Data Engineer Associate Exam Dumps & Study Guide

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