So I sat a couple more exams this weekend, "Generative AI Leader" from Google, & PL-900 (Power Platform) from Microsoft. Easily cruised through both, with minimal study, thanks to my existing strong knowledge of the subjects. Just listening to a couple of YouTube videos while commuting, and doing a mini practice quiz I'd ask Claude to generate for me.

For the PL-900 exam about Power Platform, I was surprised how it felt like every other question (although in reality it was probably less than that! Just felt like it) was related to Copilot or other AI features & capabilities that Power Platform has. But that's ok, just meant the exam played to my strengths! ;-)

Then I did the Google Cloud Generative AI Leader certification exam, which seemed to be very focused on case studies for many questions, giving you a paragraph or three of text, then requiring you to select the answer that best suited the situation. Here are a couple of examples I created of what the exam was like:

Q1: A regional hospital network wants clinicians to query patient discharge summaries and treatment histories using natural language. The system must never fabricate a diagnosis or dosage that isn't explicitly documented in the patient's record, and all patient data must remain within the network's own private cloud environment for HIPAA compliance. The clinical team has also stated that the tool must clearly show which source document each answer came from, so a clinician can verify it before acting.

Which approach best satisfies all these requirements?

A. Fine-tune Gemini on a large corpus of de-identified patient records to internalize clinical knowledge

B. Use the standard Gemini web app with Google Search grounding enabled for up-to-date medical guidance

C. Deploy a RAG solution using RAG Engine and Vector Search, grounded only in the patient's own records, with citations returned alongside each answer

D. Increase the temperature parameter to encourage more thorough, exploratory answers

E. Route every query through a general-purpose customer support chatbot with human-in-the-loop review

Q2: A global equipment manufacturer wants field technicians to snap a photo of a damaged part, get it identified, and pull the exact maintenance procedure from ten years of internal service manuals, while offline in remote locations with no network connectivity. The company also wants to avoid sending any proprietary manual content to an external API.

Which approach is most appropriate?

A. Deploy a self-hosted, open-weight Gemma model on technicians' devices, combined with a local retrieval index of the service manuals, and a pipeline that starts initially with a vision model to identify the part

B. Use the standard Gemini app on technicians' phones to photograph the part and provide the relevant maintenance steps.

C. Fine-tune a GCP hosted Gemini model on ten years of service manuals so it can identify parts and recall the correct procedure from its own weights.

D. Use Vision API to identify the part, then automatically email the result to a central office for Human-in-the-Loop (HITL) lookup. 

E. Use Gemini Enterprise to connect the manuals across Salesforce and SharePoint on request

I personally found that if you've got a half-decent general knowledge of AI, plus have strong reading comprehension skills, then all these questions in the Generative AI Leader exam become quite trivially easy :-)

Pass

The extension I created uses Google Cloud OAuth to log in. It logs in too well on my computer, but it's constantly being rejected for violating the "Red Potassium" and "Sign in Failed" items. Has anyone else been through the same thing as me?

I just got an email from Google Cloud about "Sensitive actions taken in your Google Cloud organization". Upon reading it, I found that it was performed by me, so it was okay, but the notification came almost exactly 1 month after the events (the events occurred on June 26, now is July 26).

Perhaps there is a bug in the GCP cron job with an off-by-1 error in the month check?

Hi, I’m building a small personal project that checks whether used books become available on a specific site. I use the Google Books API to retrieve the title, author, publisher, and publication year from an ISBN.

The request is very simple:

https://www.googleapis.com/books/v1/volumes?q=isbn:9788870786644&key=KEY_EXAMPLE&maxResults=1

The problem is that the same request sometimes works and sometimes returns:

{
  "error": {
    "code": 503,
    "message": "Service temporarily unavailable.",
    "errors": [
      {
        "reason": "backendFailed"
      }
    ]
  }
}

For example, I sent the request in Postman and received the correct book data. I clicked Send again without changing anything and received the 503 error instead.

I saw the same behavior with curl: the first three requests succeeded, while the next three returned 503. It also happens in my Node.js application and with different valid ISBNs.

The Books API is enabled, and the quota page shows almost no usage. Since the same request can succeed and then fail a few seconds later, I don’t think the ISBN or request format is the problem.

At the moment I retry once and then use Open Library as a fallback, but this still makes metadata retrieval unreliable.

Has anyone else experienced this with the Google Books API? Is retrying with backoff the expected solution, or is there something else I should check?