I get it, it's an attractive proposition. Type any technical question into a chat window and get an instant response. Unfortunately (at the moment), it's not quite as simple as that. I think we all know that ChatGPT & other large language models (LLMs) can hallucinate, i.e. confidently giving you answers that: - are wrong - are misleading - were maybe right 6 months ago, but now the answer is irrelevant/ not accurate. With Fabric, they are a few factors that increase the likelihood of hallucinations, that you need to be very aware of: - Fabric is fast moving - things change weekly, monthly. Therefore a feature/ method/ piece of documentation that was used in the last LLM training run 6 months ago, might no longer be relevant, or new features have superseded previous approaches. - Fabric is the evolution of previous Microsoft data products. This is good in some ways, but catastrophic for LLMs (and learners relying on LLMs). There is vastly more training data out on the internet for Azure Data Factory, for example, than Fabric Data Factory. Or Azure Synapse Data Engineering over Fabric Data Engineering. And yes there are similarities for how the old tools work vs the new tools, but you need to be super careful that the LLM generates a response for FABRIC Data Pipelines, rather than Azure Data Factory pipelines, for example. Or generates Fabric Data Warehouse compliant T-SQL code, rather than Azure SQL code. This is very difficult, unless you have knowledge of how both products work (which most learners/ beginners don't!). I'm not saying don't use LLMs for studying, just that you need to be super careful. I can think of two use cases that are lower risk, using LLM+Fabric for Spark syntax & KQL syntax generation. That's because Spark and KQL are very mature ecosystems, with lots of training data on the internet, and their syntax won't change too much over the months and years. Fabric Data Warehouse T-SQL code generation is more tricky/ risky because the way the Fabric Data Warehouse works is quite different to a conventional SQL Server (which is what most of the training data will be based on).

I have passed successfully the certification exam (score ~850/1000). It was "appropriately hard", I´d say. Thank you to Will and the community, you have been indispensable help! Some notes: - Using MS learn saved me a few extra points. - Using MS learn crashed the OnVue application three times (twice during transitions between the exam parts and once when I tried to close the learn window). What a hassle. - Lakehouse & Pyspark is still a thing, no overtly complex questions though

Hi everyone, Is there a place in Fabric where I can view a list of all scheduled jobs? I know I can check the schedule details by opening each pipeline individually, but it's not feasible to open hundreds of pipelines just to see what's scheduled for the next morning. The 'Monitor' feature only shows jobs that have already run. Thanks!

I´m looking for work and got a call from a recruiter. The job is a Azure data engineering job, the client uses Data Factory, Synapse and Databricks among others (apparently Fabric in future too but that was not on req list). I´ve been now studying for DP-600 and preparing for the exam. I have also prior background in other data tools. I filled in a skill assesment form for the job and put most things to "no experience" or "1" for e.g. Synapse, which is true (on scale 1-5). The recruiter called me back saying that "hey, you know very much already and are experienced, just put like 3 or 4 there". I´m a bit hesitant but is the skills from e.g. Fabric Data Factory so transferrable that I can just put 3 or 4 on Azure Datafactory? For context, I´m a Finn and in my culture belittling one´s own skills is seen as a virtue. The recruiter was just laughing and said that if I was from some other culture I would have just put all 4s and 5s across the board :D

Hi everyone, I was thinking for the last couple days about a post on this topic, as I was personally really concerned about the change in the study guide that took place on the 15th of November (I took an exam a week after). Like, what exactly has changed? Does deletion of pyspark mean increase in expected proficiency of SQL, or rather DAX? If Real-Time Intelligence (RTI) is not a part of the examination, why should I know KQL? Since admin governance has been almost completely deleted, what should I still need to know in that regard? To get one thing out of the way: I believe it still doesn't hurt to know a bit about all Fabric tools/experiences, even if it is more of an "overview" (Data Science is probably the exception and can be ommited). I got 1 or 2 pyspark questions in my exam, but they were general. Same with capacities - I got one question there, relatively general as well (not sure even which of these were graded, as MS continuously assess "psychometrics" by including some questions that are still in test phase). I heard some people had RTI-related questions, even though it is out of the study guide for quite some time. So, focus on study guide, but have an overview of all the Fabric infrastructure, even if it means that you know it exists and have basic knowledge how it works. ------ Now the "perspective": I believe that it is important to look from a higher vantage on 3 exams: DP-700 DP-600 and PL-300. While PL-300 remains a PBI-centered exam and will most likely stay constant due to maturity of PBI and a vast userbase, I believe DP-exams are evolving to create a sort of spectrum for "Fabric other than PBI". Evolving is key here: DP-500 and DP-600 (till November 15th) were sort of "Fabric-all" middle-level examinations, that tried to cover pretty much everything apart from data science. Due to the scope, questions had to be relatively general. My (subjective) guess is that with the rollout of DP-700 (still in beta) Microsoft is shifting DP-600 to be more analytical in scope. Admin governance, PySpark and RTI were moved to DP-700.