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Exam Information
Official specifications published by Microsoft
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Skills this practice test covers
- Configure Spark, domain, OneLake and data workflow settings for a Fabric workspace.
- Choose between a pipeline and a notebook, and set schedules and event-based triggers.
- Design full and incremental loads, and prepare data for a dimensional model.
- Transform batch data with PySpark, SQL and KQL, including duplicate and late-arriving rows.
- Process streaming data with eventstreams, Spark structured streaming and KQL windowing functions.
- Resolve pipeline, dataflow and notebook errors, then optimize lakehouse tables and Spark jobs.
Explore related topics
DP-700 exam content
3 domains · 38 topics · 156 of 302 questions are written for DP-700
Implement and manage an analytics solution53 questions
This domain covers the Fabric environment that data work runs in. Candidates configure workspace settings, manage the lifecycle with version control, database projects and deployment pipelines, secure items at every level, and orchestrate pipelines and notebooks.
Skills tested
- Set workspace options for Spark, domains, OneLake and data workflows.
- Configure version control, implement database projects, and create deployment pipelines.
- Implement access controls from workspace level down to rows, columns, objects, folders and files.
- Apply dynamic data masking and sensitivity labels, endorse items, and use workspace logging.
- Orchestrate notebooks and pipelines with parameters, dynamic expressions and event-based triggers.
Questions by topic
- Configure OneLake workspace settings4
- Implement orchestration patterns (e.g., parent-child, looping, branching)5
- Implement lifecycle management4
- Configure security and governance9
- Deploy items across workspaces by using deployment pipelines4
- Configure Spark workspace settings4
- Apply sensitivity labels to Fabric items3
- Implement dynamic data masking3
- Configure item-level access2
- Implement version control for Fabric items6
- Configure domain workspace settings5
- Design a pipeline or a notebook based on requirements3
- Configure alerts1
Ingest and transform data56 questions
This domain is the core loading work, for batch and streaming data alike. Candidates design loading patterns, choose a data store and a transformation tool, use shortcuts and mirroring, and process streams with eventstreams, Spark structured streaming or KQL.
Skills tested
- Design full and incremental loads, and a loading pattern for streaming data.
- Choose between dataflows, notebooks, KQL and T-SQL for a transformation.
- Create and manage shortcuts to data, and implement mirroring.
- Group, aggregate and denormalize data, and handle duplicate, missing and late-arriving data.
- Choose a streaming engine and create windowing functions over a stream.
Questions by topic
- Process data by using eventstreams7
- Process data by using KQL5
- Configure and use mirroring5
- Design and implement data loading patterns6
- Configure and use Fabric shortcuts4
- Process data by using Spark structured streaming3
- Design and implement data loading that supports the creation of a dimensional model5
- Choose an appropriate data store4
- Ingest data by using pipelines2
- Transform data by using Spark notebooks9
- Copy data by using SQL3
- Choose data loading and transformation methods2
- Ingest and transform batch data1
Monitor and optimize an analytics solution47 questions
This domain covers keeping a running solution healthy and fast. Candidates monitor ingestion, transformation and semantic model refresh, configure alerts, resolve errors across Fabric items, and optimize tables, pipelines, warehouses, eventstreams, eventhouses and Spark.
Skills tested
- Monitor data ingestion, data transformation and semantic model refresh, and configure alerts.
- Identify and resolve pipeline, dataflow, notebook, eventhouse, eventstream and T-SQL errors.
- Optimize a lakehouse table, a pipeline and a data warehouse.
- Improve Spark performance and query performance.
Questions by topic
- Optimize Data Warehouse performance7
- Optimize Spark performance6
- Identify and resolve pipeline errors3
- Optimize Lakehouse table performance6
- Monitor refreshes of semantic models4
- Identify and resolve notebook errors7
- Configure alerts3
- Identify and resolve dataflow errors4
- Optimize pipeline performance3
- Optimize performance1
- Monitor analytics solution components2
- Identify and resolve performance issues1
Requirements
- No formal prerequisites: anyone can book the exam.
- Recommended: subject matter expertise in data loading patterns, data architectures and orchestration processes.
- Recommended: skill in manipulating and transforming data with SQL, PySpark and KQL.
About the DP-700 exam
With DP-700, Microsoft certifies data engineers who build batch and streaming data solutions in Microsoft Fabric. The exam has three domains of similar weight: setting up and securing workspaces, loading and transforming data, and monitoring and tuning what runs. Microsoft expects candidates to be skilled with SQL, PySpark and Kusto Query Language (KQL). Passing earns Microsoft Certified: Fabric Data Engineer Associate.
The PlanetCert practice test spreads its questions across all three domains, with many on Spark notebooks, eventstreams and warehouse tuning. The explanations show why one tool fits a load better than another, such as a pipeline Copy activity for a plain copy, or a notebook when the data needs PySpark logic. Study Mode suits the error questions, where the fix depends on reading a failed run's details. Brainy AI Assistant can walk through a KQL windowing query, or explain how a shortcut differs from mirroring.
How to earn the certification
- Earns
- Microsoft Certified: Fabric Data Engineer AssociateAssociate
The certification is valid for one year and is renewed with a free online assessment on Microsoft Learn.
- Before
- DP-900 Azure Data Fundamentals
Optional. Core data concepts, such as relational and analytical data, before associate-level data engineering.
- DP-900 Azure Data Fundamentals
How to prepare for DP-700
- 1
Set up the workspace like production
Connect a Fabric workspace to Git, review its Spark settings and assign it to a domain. Add a deployment pipeline with development, test and production stages.
- 2
Load one table twice
Build a full load and an incremental load of the same source table, once with a pipeline and once with a notebook. Compare how each run handles duplicate and late-arriving rows.
- 3
Push a live stream through
Send sample events into an eventstream, land them in an eventhouse, and aggregate them with a KQL windowing function. Then process the same events with Spark structured streaming.
- 4
Read every failed run
The exam asks how to resolve pipeline, dataflow, notebook, eventhouse, eventstream and T-SQL errors. Cause one failure of each kind, then trace it from the Monitoring hub to its cause.
- 5
Compact a table, then sit timed runs
Compact a lakehouse table and compare query times before and after. Then work through the DP-700 practice test by domain in Study Mode, and finish with full runs in Timed Mode.
Start a timed DP-700run
Microsoft's course is Microsoft Fabric Data Engineer (DP-700T00). Its learning paths include Ingest data with Microsoft Fabric, Implement a Lakehouse with Microsoft Fabric, Implement Real-Time Intelligence with Microsoft Fabric, and Implement a data warehouse with Microsoft Fabric.
Who DP-700 is for
- Data engineers who build batch and streaming pipelines in Microsoft Fabric.
- Azure data engineers who are moving their loading and transformation work into Fabric.
- Analytics engineers who also own ingestion, orchestration and monitoring in a workspace.
Typical job titles: Data engineer · Cloud data engineer · Analytics engineer · Data architect · BI developer
DP-700 glossary
19 terms from the exam objectives and the vendor's official material, each defined in a sentence.
- Change data capture
- A method that records inserts, updates and deletes in a source, so an incremental load copies only the changes.
- Compaction
- Merging a table's many small files into fewer large ones, so that queries open fewer files.
- Copy activity
- A pipeline step that copies data from a source to a destination.
- Database projects
- SQL scripts that define a warehouse's schema, kept in source control and deployed together.
- Dataflow Gen2
- A low-code Fabric tool that ingests and transforms data with Power Query.
- Delta Lake
- The open table format behind Fabric lakehouse tables, which adds transactions and version history to data lake files.
- Deployment pipelines
- A Fabric feature that promotes items through development, test and production workspaces.
- Dynamic data masking
- A feature that hides part of a column's values, such as most digits of a card number, from users without permission.
- Event-based triggers
- Triggers that start a pipeline when an event happens, such as a file arriving, instead of on a schedule.
- Eventhouse
- A Fabric store for large volumes of event and time-series data, queried with KQL.
- Eventstreams
- A Fabric item that captures streaming events, can transform them, and routes them to destinations such as an eventhouse.
- Late-arriving data
- Records that arrive after the period they belong to has already been loaded.
- Mirroring
- A Fabric feature that keeps a near real-time copy of an external database in OneLake.
- Monitoring hub
- The Fabric page that lists runs of pipelines, notebooks and other items, with their status and details.
- OneLake
- The one data lake that every Microsoft Fabric workspace in a tenant stores its data in.
- Real-Time Intelligence
- The Fabric workload for streaming and event data, which includes eventstreams and eventhouses.
- Shortcuts
- OneLake references to data kept elsewhere, so that Fabric can use the data without copying it.
- Spark structured streaming
- A Spark engine that processes a data stream as a table that keeps growing.
- Windowing functions
- Functions that group stream events into time windows, such as tumbling windows, before aggregating them.