Microsoft Fabric Data Engineer Associate (DP-700) Beta - My Experience & Tips

TLDR

DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric is a challenging exam that covers most aspects of data engineering on Microsoft Fabric. Personally, I consider it a tougher exam than DP-600 and I believe it could do with some rebalancing around examining more spark and pipeline / orchestration topics, but all topics felt relevant and there wasn’t too much variation in question complexity with one or two exceptions.

That said, it’s likely existing Fabric data engineers are more familiar with some experiences than others, so there is probably some learning that’s needed for most people - especially those with limited real time intelligence (or KQL) and error resolution experience.

I expect some of the balancing to be addressed for the more left-field questions when the exam goes live, based on feedback from those undertaking the beta. Even though DP-700 has a large overlap with high level topics to DP-600, I think the exam considers them from different angles or contexts and should have good alignment with those looking to prove their data engineering skills with Fabric.

What is a beta exam?

Exams in beta are assessed for performance and accuracy with feedback on questions being gathered. The beta process is slightly different with AWS exams (described here) than Azure - for this beta exam there isn’t a guaranteed discount (usually 50% with AWS), the beta period duration is not clearlyy defined ahead of time (end date isn’t currently published), and the results aren’t released 5 days after taking the exam but around 10 days after the exam exists its beta period so I don’t yet have my results. Microsoft publish some information on beta exams here.

What is the Fabric DE certification and who is it aimed at?

Aside from the obvious role alignment in the certification title, the Microsoft documentation describes the expected candidate for this exam as someone with “subject matter expertise with data loading patterns, data architectures, and orchestration processes” as well as the below measured skills:

Implement and manage an analytics solution (30–35%)
Ingest and transform data (30–35%)
Monitor and optimize an analytics solution (30–35%)

One thing I would call out around the ingesting and transforming data is that the exam covers all mechanisms of doing so - notebooks, stored procedures, data flows, KQL Querysets - and utilising Spark, SQL, and KQL.

Exam prep

At the time of the beta exam being available, there wasn’t a formal Microsoft Learn training path. Beyond a few blogs from some Microsoft Data Platform MVPs, the only real collateral that exists is a collection of Learn modules. For those who undertook the DP-600 exam before November 15th 2024 (see some blogs about the updates here and here), this collection is mostly similar to the DP-600 learn modules. Additions include “Secure a Microsoft Fabric data warehouse”, three Real Time Intelligence modules (getting started with RTI, using Real Time Eventstreams, and querying data in a KQL database) as well as “Getting started with Data Activator in Microsoft Fabric.” Beyond this, the only real preparation I could suggest is a reasonable amount of hands on development experience across data engineering workloads and development experiences. Though it’s also worth saying that the suggestions from my DP-600 blog still apply.

My experience and recommendations

Most of the reason I wanted to publish this was to cover exam topics, but before doing so there are three key things worth calling out:

Get used to navigating MSLearn - you can open an MSLearn window during the exam. It’s not quite “open book” and it’s definitely trickier to navigate MSLearn using only the search bar rather than search engine optimised results, but effectively navigating MSLearn means not always needing to remember the finest intricate details. It is time consuming, so I aimed to use it sparingly and only when I knew where I could find the answer quickly during the exam. I also forgot that this was possible so missed using it for the first 25 questions
Though it’s somewhat related to time sent navigating MSLearn as above, I did run quite tight on time and only had about 7 minutes remaining when I finished the exam so use your time wisely
Case studies were front-loaded and not timed separately. It’s a delicate balance to be struck, but you can’t go back to the case studies so want to make sure you spend enough effort on them but it’s worth being careful to not waste time for the remaining questions. For reference, I spent about 20 minutes on the 2 case studies

As for the exam topics:

I observed a number of questions focused on error resolution across a range of examples such as access controls (linked to the item below), SQL queries, scheduled refreshes, and more
Be confident around permissions and access control. Though they’re accessible via MSLearn, I’d recommend memorising the workspace roles (admin, contributor, member, admin), but also consider more complex requirements such as row level security, data warehouse object security and dynamic data masking (including evaluating predicate pushdown logic)
Though it could be covered above, I would also suggest having some experience in testing scenarios related to workspace governance and permissions such as configuring various read and write requirements across multiple workspaces via both roles and granular access requirements. I think some questions extend this beyond a simple security question and more into a question of architecture or design
A broad understanding of engineering optimisation techniques is helpful, but I would recommend having a deeper understanding and hands on experience of read optimisation and table maintenance including V-Order, OPTIMIZE, and VACUUM
Deployment processes - pipelines, triggering orchestration based on schedules and processes, and understanding cross workspace deployment, but also azure DevOps integration
Experience selection - at face value, the notebook vs data flow vs KQL and Lakehouse vs warehouse vs event stream seem straightforward but as always the detail is crucial. Be aware of scenarios outlining more specific requirements like a requirement to read data with SQL, KQL, and spark but only write with spark or choosing between methods of orchestration such as job definitions vs. pipelines
Intermediate SQL, PySpark, and KQL Expertise is required - I noted intermediate SQL and beginner PySpark being important for DP-600. Here, both are still true and perhaps more intermediate PySpark is required, but KQL experience is needed too, and I had quite a few code completion exercises across SQL, spark, and KQL with a mix of simple queries, intermediate functions like grouping, sorting, and aggregating, and more advanced queries including complex joins, windowing functions and creation of primary keys. I also had one question that was around evaluating functionality of a few dozens of lines of PySpark code with multiple variables and try/except loops - I felt the complexity of questions was much higher than in DP-600, but it’s hard to know whether this will remain post-beta
I’ve already mentioned a few times above, but Real Time Intelligence was scattered across a number of questions. Alongside understanding various real time assets and KQL logic, a number of scenarios followed a similar pattern around sourcing data from event hub, outputting to lakehouses and implementing filters or aggregations, sometimes with a focus on optimisation techniques for KQL
Understand mechanisms for ingesting external data sources. Though seemingly obvious for a data engineering exam, a couple of things that I would suggest being confident around are PySpark library (or environment) management and shortcuts, including shortcut caching
Capacity monitoring and administration including cross-workspace monitoring, checking running sql query status, and interacting with the Fabric capacity monitoring app