Microsoft Dp-700 Fabric data engineer Practice Exam
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Official Name: Microsoft DP-700 Fabric Data Engineer Associate
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Start NowAbout the Microsoft Dp-700 Fabric data engineer Certification
The Microsoft Dp-700 Fabric data engineer certification is designed for professionals who ingest, transform, and orchestrate data using Microsoft Fabric tools. they implement lakehouses, real-time intelligence, and data pipelines while optimizing performance and managing governance. As technology evolves and industry demands grow more complex; this credential validates your ability to apply real-world skills and knowledge using Microsoft tools and frameworks. Earning the certification positions you as a trusted expert, capable of solving high-impact challenges and contributing to secure, scalable, and efficient systems.
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Test Your Knowledge of Microsoft Dp-700 Fabric data engineer
Question #1
A company wants to ingest data from multiple sources into a Lakehouse for analytics.
Which Fabric feature supports scalable data ingestion?
A) Data pipelines
B) Semantic models
C) Notebooks
D) Eventstreams
Solution
Correct answers: A – Explanation:
Data pipelines in Fabric allow scalable ingestion from various sources into Lakehouses or Warehouses for analytics.
Question #2
A data engineer needs to transform raw data using PySpark before loading it into a Warehouse.
Which Fabric component should be used?
A) Notebook
B) Semantic model
C) Dataflow
D) Pipeline
Solution
Correct answers: A – Explanation:
Notebooks support PySpark for advanced data transformation and can be integrated into pipelines for loading.
Question #3
A team wants to implement a medallion architecture in Fabric.
Which layer typically contains curated, cleaned data?
A) Silver layer
B) Bronze layer
C) Gold layer
D) Raw layer
Solution
Correct answers: A – Explanation:
The silver layer in a medallion architecture stores cleaned and enriched data ready for analytics.
Question #4
An organization needs to monitor semantic model refresh failures.
Which Fabric feature provides this capability?
A) Monitoring Hub
B) Notebook logs
C) Pipeline history
D) Lakehouse audit trail
Solution
Correct answers: A – Explanation:
Monitoring Hub tracks semantic model refreshes and alerts users to failures and performance issues.
Question #5
A company wants to automate promotion of Fabric items across dev, test, and prod environments.
Which feature supports this?
A) Deployment pipelines
B) Notebook triggers
C) Lakehouse replication
D) Semantic model export
Solution
Correct answers: A – Explanation:
Deployment pipelines manage lifecycle promotion of Fabric assets across environments with version control.
Question #6
A data engineer needs to optimize query performance in a Fabric Warehouse.
Which strategy should be applied?
A) Use distribution and indexing options
B) Notebook caching
C) Lakehouse partitioning
D) Semantic model filtering
Solution
Correct answers: A – Explanation:
Distribution and indexing improve query performance by organizing data efficiently in Warehouses.
Question #7
A team wants to share a semantic model across multiple workspaces.
Which Fabric feature supports this?
A) Cross-workspace dataset sharing
B) Notebook linking
C) Lakehouse replication
D) Eventstreams
Solution
Correct answers: A – Explanation:
Cross-workspace dataset sharing allows semantic models to be reused across Fabric workspaces for consistency.
Question #8
A business wants to secure access to Lakehouse tables containing sensitive data.
Which feature should be used?
A) Workspace role assignments
B) Notebook encryption
C) Pipeline masking
D) Semantic model permissions
Solution
Correct answers: A – Explanation:
Data pipelines in Fabric allow scalable ingestion from various sources into Lakehouses or Warehouses for analytics.
Question #9
A data engineer needs to ingest streaming data into Fabric for real-time analytics.
Which component supports this?
A) Eventstreams
B) Lakehouse
C) Notebook
D) Semantic model
Solution
Correct answers: A – Explanation:
Eventstreams enable ingestion and processing of real-time data for analytics in Fabric environments.
Question #10
An organization wants to schedule daily transformations and load data into a Warehouse.
Which Fabric feature supports this automation?
A) Pipeline scheduling
B) Notebook triggers
C) Semantic model refresh
D) Lakehouse sync
Solution
Correct answers: A – Explanation:
Pipeline scheduling automates data transformation and loading tasks on a recurring basis.
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