I haven't taken it, but I've taken self-paced free courses from other platforms and recommend you take those first to make sure that is something you think is viable or want to do before you pay for a course. I really don't think that course would make an impact with DA work, but if you want to look into working in the AI integration field it MAY help. Here's my take on it though, knowing AI integration alone isn't going to get anyone's foot in the door. It's an add-on skill that may distinguish you within your field. More on reddit.com

I did the generative ai in marketing (university of Virginia) and thought it was fairly meh. I didn't learn a whole lot that I didn't already know. Certainly want to take another one but it'll be a bit. More on reddit.com

If you have an ML background and your ACE, it shouldn't be too hard. Read up on Kubeflow, look at Google's ML crash course and their rules of machine learning . If you do that, you'll have a very solid chance of passing. Good luck! More on reddit.com

Google's got some good courses; I've done a few of their learning paths. One caveat, "AI" has virtually come to mean two different things practically speaking: 1) traditional ML and 2) genAI. I'd advise people going in to first understand where genAI fits in the broader AI field, and then decide if you're more interested in traditional ML or genAI, and then choose courses selectively. Many programs conflate these two programs of study for understandable but, imo, somewhat outdated historical reasons. (Combining them still makes total sense if you want to learn all the main parts of AI.) That is, there's overlap in the core tech (deep learning, basically), but for many practical applications, such as building apps based on LLM APIs, studying traditional ML is simply not necessary. And if you want to do ML, then LLMs are similarly a potential distraction. The time to study both is if you want to drill down into the guts of LLMs, such as training your own models (training, not fine tuning), which most people aren't going to do, and if they are, they're probably not using Google to learn it. I spent six months full-time going down the "learn AI" rabbit hole (and loved all the parts), but after six months, I realized that my specific goals would have been better served spending more time above the lower white line in this AI knowledge stack video . Such as learning LangChain and other agentic frameworks. More on reddit.com