DP-700 Microsoft Fabric Data Engineering Associate – Skills Measured Resources

New certification time! At FabCon Europe, a new certification was announced…the Microsoft Fabric Data Engineering Associate. This joins the DP-600 as the next Fabric-based cert and focuses more on data engineering. As this cert is based mostly on Fabric workloads, there won’t be much overlap with the Azure Data Engineer cert DP-203. If you have passed DP-203 or never taken it, it won’t matter here.

I think this is a great step in the right direction, and we’re seeing the different areas of Fabric being dived deeper into as 1 exam doesn’t do all workloads justice. Hopefully we get to see further exams in the future…perhaps even an MSCE-style process where several Fabric exams get rolled up into a larger certification.

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Certification Links

• Exam DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric (beta) – Certifications | Microsoft Learn
• Prove your data engineering skills and join the AI transformation – Microsoft Community Hub
• https://learn.microsoft.com/en-gb/credentials/certifications/resources/study-guides/dp-700
• https://learn.microsoft.com/en-gb/collections/p34pu1ex4y4r2z

How am I going to prepare for it? Well, there’s no magic here…just like the DP600 Fabric Analytics Engineering cert, I look at each skill being measured and go find the relevant Microsoft documentation and hopefully find a Learn module. I enjoy doing this as it gives me chance to go over and over the list of skills until I can pretty much recite them all from memory. I also like to see how all these skills map together and how I’d use them in building a solution.

There is a Learn Collection here https://learn.microsoft.com/en-gb/collections/p34pu1ex4y4r2z which has a variety of modules, however there is no direct link to each skill being measured.

This isn’t an exhaustive list nor is it designed to hold your hand through the exam, but it’s what I’ll be making sure I concentrate on when studying for the exam. The study guide is here and I have worked through each item and linked to what I think is the relevant area https://learn.microsoft.com/en-gb/credentials/certifications/resources/study-guides/dp-700 but of course it may not exactly fit, here’s where you come in to do your due diligence. I will be revisiting this blog to update links.

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Community

There are new blogs and videos about the DP-700 as well which I urge you to read/watch. Kevin Chant has a great blog about all the different ways you can learn/study for the exam. Nikola Ilic (Data Mozart) has a video on DP-700, check it out.

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Implement and manage an analytics solution (30–35%)

Configure Microsoft Fabric workspace settings

Configure Spark workspace settings	Docs
Configure domain workspace settings	Docs
Configure OneLake workspace settings	Docs, Docs (Shortcut Caching)
Configure data workflow workspace settings	Docs

Implement lifecycle management in Fabric

Configure version control	Docs, Docs, Docs
Implement database projects	Docs, Docs
Create and configure deployment pipelines	Docs

Configure Security & Governance

Implement workspace-level access controls	Docs
Implement item-level access controls	Docs
Implement row-level, column-level, object-level, and file-level access controls	Docs, Docs, Docs
Implement dynamic data masking	Docs
Apply sensitivity labels to items	Docs
Endorse items	Docs

Orchestrate Processes

Choose between a pipeline and a notebook	Learn (Pipelines), Docs (Notebooks)
Design and implement schedules and event-based triggers	Docs, Docs
Implement orchestration patterns with notebooks and pipelines, including parameters and dynamic expressions	Docs (Expressions), Docs (Parameters), Docs (Notebook Parameters)

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Ingest and transform data (30–35%)

Design and implement loading patterns

Design and implement full and incremental data loads	Docs, Docs, Docs
Prepare data for loading into a dimensional model	Docs
Design and implement a loading pattern for streaming data	Learn, Docs

Ingest and transform batch data

Choose an appropriate data store	Docs
Choose between dataflows, notebooks, and T-SQL for data transformation	Learn (Dataflows), Docs (Notebooks), Docs (SQL)
Create and manage shortcuts to data	Docs
Implement mirroring	Docs
Ingest data by using pipelines	Docs
Transform data by using PySpark, SQL, and KQL	Learn (PySpark), Docs (SQL), Learn (KQL), Docs (KQL)
Denormalize data	Docs
Group and aggregate data	tbc
Handle duplicate, missing, and late-arriving data	tbc

Ingest and transform streaming data

Choose an appropriate streaming engine	Docs, Learn
Process data by using eventstreams	Learn, Docs
Process data by using Spark structured streaming	Docs
Process data by using KQL	Docs, Learn (KQL)
Create windowing functions	Docs

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Monitor and optimize an analytics solution (30–35%)

Monitor Fabric items

Monitor data ingestion	Docs
Monitor data transformation	Docs
Monitor semantic model refresh	Docs
Configure alerts	Learn

Identify and resolve errors

Identify and resolve pipeline errors	Docs, Docs (Troubleshooting)
Identify and resolve dataflow errors	Docs
Identify and resolve notebook errors	Docs
Identify and resolve eventhouse errors	Docs
Identify and resolve eventstream errors	Docs
Identify and resolve T-SQL errors	Docs, Docs

Optimize performance

Optimize a lakehouse table	Docs, Docs
Optimize a pipeline	Docs (SQL Database specific)
Optimize a data warehouse	Docs
Optimize eventstreams and eventhouses	Docs (Capacity)
Optimize Spark performance	Docs (Delta)
Optimize query performance	tbc