NEW! Get It Done Certified Practice Exam Questions for the Microsoft Certified: Fabric Data Engineer Associate (DP-700) Exam
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Practice Exam Questions
Question 1
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. FUNCTION
📌 Row-level security in Fabric warehouses is implemented by using inline table-valued functions that define filtering logic based on the executing user context, which are then applied as security predicates on tables.
Incorrect answers:
❌ A. STORED PROCEDURE – Stored procedures encapsulate logic but do not enforce row-level filtering automatically on table access and cannot be used as RLS predicates.
❌ B. CONSTRAINT – Constraints enforce data integrity rules, not user-based access filtering.
❌ C. SCHEMA – Schemas organize database objects but do not provide row-level security enforcement.
Question 2
You have an Azure event hub. Each event contains the following fields:
BikepointID, Street, Neighbourhood, Latitude, Longitude, No_Bikes, No_Empty_Docks.
You need to ingest the events. The solution must only retain events that have a Neighbourhood value of Chelsea, and then store the retained events in a Fabric lakehouse.
Data retention in case of failure is required to be two days.
What should you use?
A. an eventstream
B. Apache Spark Structured Streaming
C. a streaming dataset
D. a KQL queryset
✅ Correct Answer: A. an eventstream
📌 Eventstreams in Fabric provide built-in filtering, fault tolerance, and retention while directly integrating with Event Hubs and lakehouses, making them ideal for near-real-time ingestion with minimal development effort.
Incorrect answers:
❌ B. Apache Spark Structured Streaming – This would require custom code and additional configuration, increasing development and operational overhead.
❌ C. a streaming dataset – Streaming datasets are designed for Power BI visuals, not durable lakehouse storage.
❌ D. a KQL queryset – KQL querysets are used for querying and analysis, not ingestion and persistence into lakehouses.
Question 3
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
✅ Correct Answer: B. a deployment pipeline
📌 Fabric deployment pipelines natively support deploying Fabric items, including eventhouses, across environments with built-in comparison and promotion capabilities.
Incorrect answers:
❌ A. GitHub Actions – GitHub Actions can orchestrate workflows but do not directly manage Fabric item promotion.
❌ C. an Azure DevOps pipeline – Azure DevOps can trigger deployments but does not replace Fabric’s native deployment pipeline functionality.
Question 4
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 invalid references.
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
✅ Correct Answer: B. a deployment pipeline
📌 Fabric deployment pipelines automatically validate dependencies and detect invalid references during deployment with minimal configuration effort.
Incorrect answers:
❌ A. a database project – Requires additional setup and manual management outside Fabric.
❌ C. a Python script – Custom scripting increases complexity and maintenance overhead.
❌ D. a T-SQL script – Manual validation does not scale well and lacks deployment context.
Question 5
You have a Fabric workspace that contains a lakehouse named Lakehouse1.
You plan to create a data pipeline named Pipeline1 to ingest data into Lakehouse1.
You will use a parameter named param1 to pass an external value into Pipeline1.
The param1 parameter has a data type of int.
You need to ensure that the pipeline expression returns param1 as an integer value.
How should you specify the parameter value?
A. "@pipeline().parameters.param1"
B. "@{pipeline().parameters.param1}"
C. "@{pipeline().parameters.[param1]}"
D. "@@{pipeline().parameters.param1}"
✅ Correct Answer: B. "@{pipeline().parameters.param1}"
📌 The @{} expression syntax ensures proper evaluation and type preservation when referencing pipeline parameters in Fabric data pipelines.
Incorrect answers:
❌ A. "@pipeline().parameters.param1" – This returns a string literal instead of evaluating the expression.
❌ C. "@{pipeline().parameters.[param1]}" – Incorrect syntax for parameter referencing.
❌ D. "@@{pipeline().parameters.param1}" – Invalid expression syntax.
Question 6
You have a Fabric workspace named Workspace1 that contains a lakehouse named Lakehouse1.
Workspace1 contains the following items:
- A Dataflow Gen2 dataflow that copies data from an on-premises SQL Server database
- A notebook that transforms files
- A data pipeline that loads a CSV file
You need to develop an orchestration solution that will load each item one after the other and run every 15 minutes.
Which type of item should you use?
A. notebook
B. warehouse
C. Dataflow Gen2 dataflow
D. data pipeline
✅ Correct Answer: D. data pipeline
📌 Data pipelines provide built-in orchestration, dependency control, and scheduling capabilities, making them the appropriate choice for sequential execution.
Incorrect answers:
❌ A. notebook – Notebooks do not provide native orchestration across heterogeneous Fabric items.
❌ B. warehouse – Warehouses store data but do not orchestrate workflows.
❌ C. Dataflow Gen2 dataflow – Dataflows handle transformations but cannot orchestrate notebooks and pipelines together.
Question 7
You have a Fabric workspace that contains a lakehouse and a notebook named Notebook1.
Notebook1 reads data from a table named Table1 and writes data to a new Delta table named Table2 using a merge operation.
You need to consolidate the underlying Parquet files in Table1.
Which command should you run?
A. VACUUM
B. BROADCAST
C. OPTIMIZE
D. CACHE
✅ Correct Answer: C. OPTIMIZE
📌 The OPTIMIZE command compacts small Parquet files into larger ones, improving read performance and query efficiency.
Incorrect answers:
❌ A. VACUUM – Removes obsolete files but does not consolidate active files.
❌ B. BROADCAST – Used for query execution optimization, not file compaction.
❌ D. CACHE – Caches data in memory temporarily without modifying file layout.
Question 8
You are monitoring execution of items in Fabric by using Monitoring hub.
You need to identify in which workspace a specific item runs.
Which column should you view?
A. Start time
B. Capacity
C. Activity name
D. Submitter
E. Item type
F. Job type
G. Location
✅ Correct Answer: G. Location
📌 The Location column identifies the workspace where the item execution occurred, enabling precise tracking across environments.
Incorrect answers:
❌ A. Start time – Indicates when execution began, not where it ran.
❌ B. Capacity – Shows compute allocation, not workspace identity.
❌ C. Activity name – Identifies the action, not its location.
❌ D. Submitter – Identifies the user who initiated execution.
❌ E. Item type – Describes the type of object executed.
❌ F. Job type – Categorizes execution workload, not workspace.
Question 9
You have an on-premises SQL Server database accessed via an on-premises data gateway.
You need to copy data from the database to a Fabric warehouse.
Which item should you use?
A. a Dataflow Gen1 dataflow
B. a data pipeline
C. a KQL queryset
D. a notebook
✅ Correct Answer: B. a data pipeline
📌 Data pipelines support gateway-based connectivity and provide optimized data movement into Fabric warehouses.
Incorrect answers:
❌ A. a Dataflow Gen1 dataflow – Legacy option with limited orchestration and scalability.
❌ C. a KQL queryset – Used for querying, not ingestion.
❌ D. a notebook – Requires custom code and does not natively manage gateway ingestion.
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Question 10
You have a Fabric workspace that contains a Real-Time Intelligence solution and an eventhouse.
Users report that they cannot see the eventhouse data from OneLake File Explorer.
You enable OneLake availability for the eventhouse.
What will be copied to OneLake?
A. only data added to new databases
B. only the existing data
C. no data
D. both new data and existing data
E. only new data
✅ Correct Answer: D. both new data and existing data
📌 Enabling OneLake availability exposes both historical and newly ingested eventhouse data to OneLake without duplicating storage.
Incorrect answers:
❌ A. only data added to new databases – Existing data is also exposed.
❌ B. only the existing data – New incoming data is included as well.
❌ C. no data – Data becomes accessible once enabled.
❌ E. only new data – Existing data is not excluded.
Question 11
You have a Fabric workspace named Workspace1.
You plan to integrate Workspace1 with Azure DevOps. You will use a Fabric deployment pipeline to deploy items by triggering an API call from an Azure DevOps pipeline.
Which type of authentication should you use?
A. service principal
B. Microsoft Entra username and password
C. managed private endpoint
D. workspace identity
✅ Correct Answer: A. service principal
📌 Service principals provide secure, non-interactive authentication for automated CI/CD workflows and are the recommended approach for API-based Fabric deployments. They align with the principle of least privilege and allow fine-grained permission assignment scoped to Fabric workspaces and deployment pipelines. Using service principals avoids credential rotation issues associated with user accounts. This approach is consistent with enterprise-grade DevOps automation patterns.
Incorrect answers:
❌ B. Microsoft Entra username and password – This method is insecure for automation, does not scale, and violates modern identity best practices by relying on interactive credentials.
❌ C. managed private endpoint – Managed private endpoints enable private network connectivity but do not provide authentication or authorization capabilities.
❌ D. workspace identity – Workspace identities are designed for resource access from Fabric items, not for external CI/CD pipeline authentication.
Question 12
You have a Fabric workspace that contains an Apache Spark job definition named Job1.
You have an Azure SQL database that has public internet access disabled.
You need to ensure that Job1 can access the database.
What should you create?
A. an on-premises data gateway
B. a managed private endpoint
C. an integration runtime
D. a data management gateway
✅ Correct Answer: B. a managed private endpoint
📌 Managed private endpoints allow Fabric Spark workloads to securely access Azure services over the Microsoft backbone network without exposing public endpoints. This approach ensures compliance with network isolation and security requirements while maintaining low latency. It is the recommended solution for accessing Azure SQL databases with public access disabled. No additional infrastructure is required.
Incorrect answers:
❌ A. an on-premises data gateway – This is used only for on-premises data sources and is unnecessary for Azure-native services.
❌ C. an integration runtime – Integration runtimes are part of Azure Data Factory architecture and are not used by Fabric Spark jobs.
❌ D. a data management gateway – This is a deprecated concept and not applicable to Microsoft Fabric.
Question 13
You have a Fabric workspace that contains a lakehouse named Lakehouse1.
External data files are 500 GB each, and a new file arrives daily.
You must ingest the data with no transformations, trigger ingestion when a new file arrives, and achieve the highest throughput.
Which item should you use?
A. Data pipeline
B. Environment
C. KQL queryset
D. Dataflow Gen2
✅ Correct Answer: A. Data pipeline
📌 Fabric data pipelines support event-based triggers, high-throughput ingestion, and direct loading into lakehouses without transformation overhead. They are optimized for large-scale file movement and can automatically respond to new file arrivals. This minimizes development effort while maximizing performance. Pipelines are the preferred ingestion mechanism for raw data scenarios.
Incorrect answers:
❌ B. Environment – Environments are used for dependency and configuration management, not data ingestion.
❌ C. KQL queryset – KQL querysets are designed for querying and analysis, not for ingesting large files.
❌ D. Dataflow Gen2 – Dataflows are optimized for transformations and shaping data, which introduces unnecessary overhead in this scenario.
Question 14
You have a Fabric workspace that contains an eventstream named EventStream1.
EventStream1 outputs streaming sensor data to a lakehouse table.
You need to compute the average speed of cars using non-overlapping, contiguous one-minute intervals, ensuring each event belongs to exactly one interval.
Which windowing function should you use?
A. sliding
B. hopping
C. tumbling
D. session
✅ Correct Answer: C. tumbling
📌 Tumbling windows divide streaming data into fixed-size, non-overlapping intervals, ensuring that each event is assigned to exactly one window. This behavior matches the requirement for contiguous one-minute aggregation without duplication. Tumbling windows are commonly used for time-based KPIs such as averages and counts. They provide predictable and deterministic aggregation results.
Incorrect answers:
❌ A. sliding – Sliding windows overlap, causing events to appear in multiple windows, which violates the requirement.
❌ B. hopping – Hopping windows also overlap and allow events to be counted multiple times.
❌ D. session – Session windows are based on activity gaps, not fixed time intervals, and are unsuitable for this use case.
Question 15
You have a Fabric workspace that contains a Real-Time Intelligence solution and an eventhouse.
Users report that they cannot see eventhouse data from OneLake File Explorer.
You enable OneLake availability for the eventhouse.
What data becomes available in OneLake?
A. Only data added to new databases
B. Only existing data
C. No data
D. Both existing and new data
E. Only new data
✅ Correct Answer: D. Both existing and new data
📌 Enabling OneLake availability exposes the eventhouse storage to OneLake without duplicating data, making both historical and newly ingested data immediately accessible through OneLake File Explorer.
Incorrect answers:
❌ A. Only data added to new databases – Existing databases are also exposed.
❌ B. Only existing data – New data continues to be exposed after enablement.
❌ C. No data – Data is exposed once availability is enabled.
❌ E. Only new data – Existing data is not excluded.
Question 16
You have a Fabric workspace that contains a lakehouse and a notebook named Notebook1.
Notebook1 reads from a Delta table and performs transformations.
You need to compact the underlying Parquet files to improve query performance.
Which command should you run?
A. VACUUM
B. CACHE
C. OPTIMIZE
D. BROADCAST
✅ Correct Answer: C. OPTIMIZE
📌 OPTIMIZE consolidates small Parquet files into larger files, reducing file fragmentation and improving read performance for analytical workloads.
Incorrect answers:
❌ A. VACUUM – Removes obsolete files but does not compact active ones.
❌ B. CACHE – Caches data in memory temporarily without changing file layout.
❌ D. BROADCAST – A query execution hint, not a storage operation.
Question 17
You have five Fabric workspaces.
You are monitoring executions by using Monitoring hub and want to identify which workspace an item ran in.
Which column should you view?
A. Activity name
B. Job type
C. Capacity
D. Location
✅ Correct Answer: D. Location
📌 The Location column identifies the workspace where the execution occurred, enabling precise tracking across environments.
Incorrect answers:
❌ A. Activity name – Identifies the operation, not the workspace.
❌ B. Job type – Indicates workload classification only.
❌ C. Capacity – Shows compute allocation, not workspace identity.
Question 18
You have an on-premises SQL Server database accessed through an on-premises data gateway.
You need to copy data into a Fabric warehouse.
Which item should you use?
A. a notebook
B. a KQL queryset
C. a data pipeline
D. a streaming dataset
✅ Correct Answer: C. a data pipeline
📌 Data pipelines support gateway-based connectivity and optimized ingestion into Fabric warehouses with scheduling and retry capabilities.
Incorrect answers:
❌ A. a notebook – Requires custom code and manual orchestration.
❌ B. a KQL queryset – Used for querying, not ingestion.
❌ D. a streaming dataset – Intended for Power BI visuals, not warehousing.
Question 19
You have a Fabric workspace that contains a warehouse named DW1.
DW1 is loaded by a notebook.
You need to identify which Delta version was used when the notebook executed.
What should you use?
A. OneLake data hub
B. Real-Time hub
C. Fabric Monitor
D. Admin monitoring workspace
✅ Correct Answer: D. Admin monitoring workspace
📌 The Admin monitoring workspace provides detailed execution metadata, including runtime versions and engine details used during execution.
Incorrect answers:
❌ A. OneLake data hub – Focuses on data discovery, not execution metadata.
❌ B. Real-Time hub – Used for streaming analytics monitoring.
❌ C. Fabric Monitor – Provides execution status but not detailed engine versioning.
Question 20
You have a Fabric workspace that contains a warehouse named Warehouse1.
You want to set a primary key on a dimension table.
Which object type supports primary keys in Fabric warehouses?
A. Delta table constraint
B. Warehouse table constraint
C. Notebook metadata
D. Semantic model relationship
✅ Correct Answer: B. Warehouse table constraint
📌 Fabric warehouses support primary keys through table-level constraints defined directly on warehouse tables.
Incorrect answers:
❌ A. Delta table constraint – Delta constraints apply to lakehouse tables, not warehouses.
❌ C. Notebook metadata – Metadata does not enforce relational constraints.
❌ D. Semantic model relationship – Logical relationships do not enforce physical constraints.
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.
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(last question to follow)
Question 320
You want to confirm which users are currently connected to a Fabric warehouse.
Which DMV should you query?
A. sys.dm_exec_connections
B. sys.dm_exec_sessions
C. sys.dm_exec_requests
D. queryinsights.failed_queries
✅ Correct Answer: B. sys.dm_exec_sessions
📌 sys.dm_exec_sessions exposes active user sessions and connection context.
Incorrect answers:
❌ A. dm_exec_connections – Connection-level only.
❌ C. dm_exec_requests – Query execution, not sessions.
❌ D. failed_queries – Historical failures.
(END OF PRACTICE EXAM QUESTIONS)
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