Certified Data Cloud Consultant

The “Year of Certification” rolls on into its second month. And I passed my second Certification exam of the year! I’ve been doing a lot of learning and exploring with Data Cloud so I expected to be in a good place to pass this exam with a little extra study. So how did it go?

I’m getting pretty familiar and comfortable with the exam process now. I know it’ll take a few minutes to do the biometric authentication and get the exam up. So I don’t worry about how long that’s taking. I know the Launch button will appear 10 minutes before. So I launch 10 minutes early. And I know to not get worried no matter how it feels as I am answering the questions - more on that in a bit

I had studied up for this exam. To start with I completed the Partner Learning Camp curriculum. The non-hands on parts of this have now been migrated to the preparatory Trail Mix so anyone can access them. If you have access to the PLC definitely go and do the hands on available there as this will teach you so much more. I had also built my own little study guide as I went pulling in material from Salesforce help. I used that in the last few days running up to the exam.

In the end I used about my normal time: a little under 30 seconds per question. I would recommend sitting these exams at your own pace. They are not a sprint! You don’t get extra marks for being quick. However I generally know the answer or have a good educated guess and spending longer won’t change that. I do make sure I read the question twice to make sure I’ve fully understood it. This exam was also the first time in a Salesforce Certification exam that I’ve used the Review feature to come back to a question later.

The exam transcript shows quite a wide variation in scores for each topic. The pass mark for the exam is 62%. So I’m happy I scored at least that in each section. Given the section weightings the above equates to about 78%. Not bad (and comfortably above the pass rate) but not that amazing. The 100% section is good though!

That said at the end of the exam I was mostly just happy (and a bit relieved) to have passed. I was not feeling particularly happy about quite a few questions and more often than I’d like I felt like I was taking educated guesses. Not completely blind random guesses but I was definitely not sure.

In terms of areas for me to improve and areas I feel the Trail Mix does not cover well:

• Right to Be Forgotten

• B2C Integration

• Marketing Cloud Integration

• S3/Cloud Activation

If you are thinking of sitting this study on on those areas! For me the “Year of Certification” rolls on. I think my next exam (maybe a couple of months away) will be an Architect one.

Data Cloud DX (September 2023)

As I said in my Dreamforce 2023 post I’ve been working on Data Cloud producing a demo. This has been my first real “in anger” exposure to Data Cloud (I’ve been on a couple of Data Cloud workshops previously).. So how has Data Cloud been to develop on? Read on to find out.

Before I describe the DX a (very) short overview of Data Cloud might be in order. This is still pretty new to the Salesforce ecosystem and most developers will not yet have played with it. Data Cloud is a Data Lake/Data Warehouse product (Salesforce like the term Data Lakehouse) that enables the ingestion of data from multiple sources on and off platform, the harmonisation of this data and actions to be taken based on insights pulled from the data. All at masive scale (trillions of records) and near-real-time performance.

Raw data ingested from any source will be stored in a Data Lake Object (DLO). These can be mapped to Data Model Objects (multiple DLOs can map to one DMO) and Data Transforms can manipulate the DLOs to map in additional fields, aggregate, filter etc. DLOs represent the Data Lake. DMOs are more like a Data Warehouse layer that is the business facing model of the data. Calculated Insights can run on the DMOs to produce windowed insights with dimensions and measures. Data Actions allow integration back to Salesforce orgs posting Platfrom Events whenever a DMO or CI records is written.

Now that we understand Data Cloud at a very high level how is it for a developer? Well the first thing to note is that its currently pretty hard for your average developer to get a Data Cloud enabled org. Certainly, as of today, you can’t just spin up a scratch org with a feature enabled.

The second general thing to note is that for iterative development Data Cloud is not great. Developers are used to writing a bit of code, pusing it to an org, trying it out, changing the code and repeating this loop multiple times. In a normal Salesforce org this works reasonably well (although could be faster here too). In Data Cloud this cycle is slow. Really slow. There is no real way to do something right now. Pretty much everything is actually scheduled and run in batches. So when you click a “Refresh Now” button what you are really saying is “schedule this for a Refresh soon”. So if you need to ingest some data, run a data transform, then a Calculated Insight you could be waiting around for half an hour.

This is all made worse by the lack of dependency based scheduling. There is no way of saying “when any of these data streams are completed run this data transform” for instance. Most processes can be scheduled to run at specific times. Which might suffice for production (although I have my doubts) but in development this means you start so,e ingests, refresh the screen until done, then run the transform, refresh that screen until done etc. This is a very frustrating way to work!

In the next post I’ll talk about the specific frustrations with the Data Transform editor/process…

Dreamforce 2023

Dreamforce is fast approaching. As much as I’d love to be there I won’t be. Not this year. Maybe next year. But some things I have been working on will be. In future posts I’ll write a bit more about them, and maybe I’ll be able to share some videos. For now I’ll just say a little about the demos I’ve been working on building and where you can see them if you are lucky enough to be at Dreamforce

The first demo I’ve been working on is a Data Cloud demo. This demo showcases how our CS Cloud product can work with Data Cloud ingesting data from multiple sources that can be on platform or off, work with that data at scale and then use that data to drive intelligent action within our product running on platform. This demo also showcases our ability to use Data Cloud to drive AI models to predict future states.

My part of this demo is the Data Cloud part. It’s been fun working in a novel environment although it’s fair to say that Data Cloud still has some rough edges from a developer experience point of view. I’ll write about the various parts of building a DC solution in future

The second demo is part of a very small closed pilot group that we as a company and I personally have been lucky enough to be part of. This is a demo of how we could incorporate Einstein GPT into our products. Again we are using CS Cloud as our demo product. The demo shows how we can use EGPT to summarise text coming from records inside or org to drive generation of application specific objects in a a number of ways.

This demo is very exciting and shows how easy it is to incorporate GPT into your apps using the Salesforce APIs. And this is with some very early APIs that will be totally re-built for wider usage after Dreamforce. Even with these early APIs I was able to achieve good results in a very short period.

You will be able to see the EGPT demo at the App Exchange landing in Dreamforce. Our Data Cloud demo will be presented twice on Thursday at 1:30 and again at 2:00. I hope you can catch our demos and see the exciting work we’ve done with new parts of the platofrm