Struggling to prep for the Google Generative AI Leader exam — anyone else?
Okay so I've been putting off scheduling my Google Generative AI Leader exam for like three weeks now because every time I sit down to study I just end up more confused than when I started. It's not that the material is impossible, it's that it's SO broad — one minute you're reading about grounding models in first-party data, the next you're supposed to know the difference between a workflow agent and a conversational agent, and then boom, PII and data anonymization show up too. It doesn't feel like a "leader" level exam, it feels like they crammed five certifications into one.
A few things I genuinely can't get straight in my head:
How deep do you actually need to go on the Vertex AI vs. BigQuery vs. Cloud Storage distinctions? I keep mixing up which tool does what when a question frames it as a "company deciding which platform to use" scenario.
Anyone have a clean way to remember the different agent types (workflow, customer service, conversational, employee productivity)? I can explain them in theory but under exam pressure I second-guess myself every time.
For those who passed — was it mostly conceptual/definition-style questions, or did you get a lot of scenario-based ones where you have to pick the "most beneficial" or "best" option out of four decent-sounding answers?
For what it's worth, going through actual generative AI Leader practice questions on pass4success actually helped me a ton with the scenario-based stuff — seeing how the "best answer" logic works (like why grounding a chatbot in first-party data beats just using general model knowledge) made the pattern click way faster than just reading theory ever did.
Anyway, would love to hear how everyone else tackled this one. Did you focus more on Responsible AI/security concepts or the Google Cloud product mapping? Any topics that caught you off guard on exam day? Drop your experience below, trying to build a game plan before I book my test date
.png?_a=BAVMn6E80){kind=small}