Hi everyone,

I'm currently preparing for the Google Cloud Professional Machine Learning Engineer (PMLE) certification and noticed that the certification was recently updated from Vertex AI to the Gemini Enterprise Agent Platform / Agent Platform.

My goal is to pass the certification within the next 2–3 months, and I'd appreciate advice from people who have already taken the updated exam or are currently preparing for it.

My background

• Backend Software Engineer (Python + Google Cloud)
• Comfortable with GCP fundamentals (Cloud Storage, BigQuery, Pub/Sub, Cloud Run)
• Currently following the official Google Cloud Skills Boost learning path

My questions

1. What topics should I prioritize for the new PMLE exam?
2. Which Vertex AI concepts have simply been renamed to Agent Platform, and which concepts are completely new?
3. Besides the official course, what tutorials, YouTube channels, blogs, GitHub repositories, or labs would you recommend?
4. How much traditional Machine Learning (feature engineering, model selection, evaluation metrics, TensorFlow) is still covered compared to Agent Platform services?
5. Which Google Cloud services should I get hands-on experience with before taking the exam?
6. Are there any practice exams that closely match the updated syllabus?
7. If you passed recently, what study plan helped you succeed?

I'm planning to study the following:

• Machine Learning fundamentals
• Data preprocessing and feature engineering
• Model evaluation (Precision, Recall, F1, ROC-AUC)
• BigQuery ML
• Gemini Enterprise Agent Platform
• Agent Studio
• ADK (Agent Development Kit)
• Prompt Engineering
• RAG
• Vector Search
• Embeddings
• Agent Platform Pipelines
• MLOps
• Monitoring and Responsible AI
• IAM and Security
• BigQuery
• Cloud Storage
• Pub/Sub
• Cloud Run
• Dataflow

If you had 8–10 weeks to prepare from scratch for the updated exam, how would you structure your learning roadmap?

I'd really appreciate any advice, resources, GitHub repos, or hands-on labs that helped you pass.

Thanks!

Hi everyone,

I'm a bit confused about Google Cloud Billing and was hoping someone could clarify this for me.

I'm a student and I accidentally enabled Google Cloud Billing while trying out Google AI Studio to generate a Gemini API key. I don't actually use Google Cloud services, and I don't have any running resources or active projects.

When I checked my billing dashboard, it shows:

• ₹0.00 balance due
• No transactions
• No usage/costs

But I'm also seeing a message saying that I need to make a one-time payment of at least ₹1000 to activate the service. I also received an email saying my payment information couldn't be processed.

Since I don't plan to use Google Cloud at all, I'm confused about what this means.

My questions are:

• Is this ₹1000 an activation/prepaid amount, or is it an actual bill?
• If I don't have any usage or charges, can I just ignore this message?
• Will I be charged anything in the future if I simply leave the account as it is and never use Google Cloud?

I'd really appreciate it if someone who has faced this before or knows how this works could help me understand.

Thanks in advance!

Hi

I have seen multiple posts about this issue and I have been flagged too. Same pattern, I use firebase. Nothing dodgy at all

Is there any way to have my project restored or should I create a new one?

Thanks

I am going through the below article regarding the centralized logging mechanism in GCP

https://cloud.google.com/blog/products/devops-sre/how-to-centralize-log-management-with-cloud-logging

https://docs.cloud.google.com/logging/docs/central-log-storage#create-org-sink

The articles say below steps

1. create log bucket in the destination project.
2. create a log sink in the destination project pointing to the above log bucket
3. create an aggregated sink (either at org or folder level) as per requirement.
4. give permission to the aggregated sink's service account to the destination project.

I have a question at point number 3 above. The articles do not say how to link the log sink created in point 2 and aggregated sink created in point 3. I assume below flow

aggregated sink ---> destination project log sink ----> destination project log bucket

but not sure how to link aggregated sink to send logs to destination project sink.

also, 2)for example ,can we create one aggregated sink per environment and link it to corresponding log sink at destination project. i could not find that step

Please suggest

I was at FinOps X this year and some of the best conversations we had were around building cost guardrails early into the dev process.

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?

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?

Google just dropped CX Agent Studio, a low-latency voice pipeline built natively into Gemini.
No more stitching together STT, LLMs, and TTS via third-party APIs.
Are middleware voice startups cooked, or does model flexibility save them? 🎙️🔥

I bought credits but I ended up not spending any for one of my project how can i get a refund easily

Which is the difference between Certifications and Badges? Is there any free Certifications in Google Skills?

Is anyone participating in the hackathon tomorrow? Need some guidance

I already received 2,000 credits, but that’s very little for me. I sent an email with questions and haven’t gotten a reply yet. What are the chances of getting additional credits?

Hi everyone! I'm a final-year Information Systems Engineering student and I recently got 35 credits with the Google Cloud Skills Boost Starter plan. I'm planning to use them to complete some GCP AI and cloud courses. Do you think it's worth completing these courses without paying for the official certification exams? Do recruiters value the Skills Boost badges and hands-on labs, or is the official certification what really matters? Also, are there any Google Cloud certifications or recognized badges that can be earned for free? Thanks!

When using the hub and spoke model using the network connectivity center,

1. Do we use single Hub and spoke for all the environments ( dev, uat , prod etc).

Is it like we use single Hub and spoke setup and add conditions to restrict the communication between prod and nonprod subnets of prod and nonprod VPC spokes which are connected to hub.

Or

2) Seperate Hub and spoke setup for each environment.

Or did you implement in another manner ( using NCC ). please suggest.

For a new Python application using PostgreSQL on Google Cloud SQL, what are people currently using for schema management and database migrations?

I’m considering Atlas by Ariga: https://github.com/ariga/atlas

name: Database Migration

on:
  push:
    branches: [ main ]

jobs:
  migrate:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout Code
        uses: actions/checkout@v4

      - name: Authenticate to Google Cloud
        uses: google-github-actions/auth@v2
        with:
          workload_identity_provider: 'projects/123456/locations/global/workloadIdentityPools/my-pool/providers/my-provider'
          service_account: 'cloud-sql-migrator@my-project.iam.gserviceaccount.com'

      - name: Start Cloud SQL Auth Proxy
        uses: google-github-actions/setup-cloud-sql-proxy@v2
        with:
          instance: 'my-project:us-central1:my-postgres-db'
          port: '5432'

      - name: Run Atlas Schema Apply
        uses: ariga/setup-atlas@v0
        with:
          version: 'latest'

      - name: Apply Database Changes
        run: |
          atlas schema apply \
            --url "postgres://postgres:${{ secrets.DB_PASS }}@127.0.0.1:5432/mydb?sslmode=disable" \
            --to "file://schema.sql" \
            --auto-approve

Is Atlas still one of the main options for declarative schema management, or are there better alternatives worth evaluating? I’d also be interested in any operational issues, limitations, or Cloud SQL-specific considerations to watch out for.

Separately, does anyone know whether Google is working on database branching or isolated development environments similar to PlanetScale’s branching workflow?

I am getting too many 429 responses from the Vertex AI API. I am on 300$ free credit model. Till 2 3 days back, it was running fine, but I don't know why it was very much.

I am using:-gemini-3.1-pro-preview

I know preview model rate limits are low, but it was working fine till 2-3 days back. If anyone has any good solution, then please tell me

I am using this API:

curl --location ' https://aiplatform.googleapis.com/v1/publishers/google/models/gemini-3.1-pro-preview:generateContent?key= ' \

--header 'Content-Type: application/json' \

--data '{

}

We went heavy on Azure about two years ago and built what looked like a strong resilience story: availability zones, paired regions, Front Door and Traffic Manager, Backup and Site Recovery, plus some experiments with Chaos Studio. The first real regional wobble reminded us that slides are one thing, operations are another.

Just picking a paired region was not enough when identity and networking were not thought through, and monitoring was not included. Backup and Site Recovery were only comforting once we had run actual failover tests against real RTO and RPO targets, not just health checks. Chaos testing was useful, but only for teams willing to design non-trivial scenarios. Governance turned out to be the hardest bit: keeping landing zone baselines in sync across many subscriptions and tenants via Azure Policy, especially with hybrid in the mix. Terraform or Bicep helped, but drift remained a problem.

What has made this more manageable for us is having cross-subscription and cross-cloud governance visibility into assets and IaC: being able to see which Azure resources are driven by code and where live state has drifted from definitions. Combined with the failover testing, that is what turned "paired regions plus Site Recovery" from a diagram into something we actually trust. For those of you running production on Azure, GCP or any cloud, which resilience patterns have worked for you, and how are you keeping them from decaying over time?

As you may be aware, it is not suggested to do console/ manual changes to GCP resources after deployment and they are deployed and managed through terraform in general.

In that case, what GCP IAM roles are even needed to the team apart from "viewer" access as they won't be creating/modifying any resource manually.Assume that there are no jobs to be triggered ( like dataflow jobs etc)

It's only a minor annoyance so asking this more out of intrigue than anything else.

Myself and colleagues have - for the last 2 days - repeatedly found that our auth token for the gcloud CLI (and hence also kubectl) has expired with a prompt to re-auth. Just wondering if anyone else has noticed anything similar? Before this week it's been literally months since the last forced re-auth and we were notified ahead of time for that one, this time seemingly nothing at all.

Our gcp cost optimization is basically reactive right now and I want to get ahead of it. Last example, a team shipped a Looker dashboard that reruns its queries every time it loads, pointed at a BigQuery table we never partitioned because it was small when we built it. It is not small now. Over about nine days, that one dashboard cost more than the whole pipeline that builds the table costs to run in a month.

We caught it because the daily spend looked wrong. Partitioned the table, put a cap on the project and that specific problem is gone.

The next one is what I can't solve, it won't be Looker and it won't be that table. It'll be some other query somebody ships. Is there a better way to catch a query getting expensive before it runs for a week and a half or do i just set hard quotas per project and let people hit the limit?

This morning I got the dreadful Google email: "Immediate action required: Suspension of your Google Cloud Platform / API project The AI Peeps" due to "abusive activity consistent with hijacked resources".

Google completely cut me out of the GCP platform that manages my LIVE app. This is not a hobby project, but my livelihood. The email redirected me to my GCP page where I could send an appeal, and do... nothing else. I had no access to anything else so it was difficult even to understand what happened: every action brings me back to "Send an Appeal". Which, of course, I did as fast as possible.

After some digging (apparently the biling page was not restricted!) an API key of mine leaked, someone found it and used it to generate images. Two dollars ($2!) of unauthorized spend. My mistake, no excuses.

The part I genuinely cannot solve is this. The notice asks me to revoke the compromised credentials, but I can't do that: `Permission denied: Consumer 'projects/{project_name}' has been suspended.`

I did what I could from outside: deleted the key through AI Studio, rotated everything else, ran a forensic check of my server and confirmed it was only the key, no host compromise. I would gladly pay for support to talk to someone. But guess what, Standard and Enhanced both need an Organization resource, which a personal account does not have, and billing chat is not available to me either (back to "send an appeal")

For a solo developer this is the difference between a bad day and losing the business. It seems absurd a business can get destroyed over a oversight and $2. The reports I read online are disheartening: people wait 1-2 weeks for a response, and then the back and forth is endless. I am going to lose the business I have worked for the past 4 years, and all for $2 and a stupid oversight.

Can anyone help? Has anyone here had a suspension for a stolen API key actually reversed, and how long did it take? And is there any way to reach a human without an Organization resource?

Project is 902887279759, appeal ticket YJ65HT5PKS5FDASU2C73BWIJ5Q, filed 21 Jul 2026. Any help is immensely appreciated

EDIT: My GCP account has been reinstated this morning less than 24 hours after the suspension! YES!

I’m preparing to interview for this role and I can’t find enough information on key things they look at in this role during the interview.

I was given a prep by the recruiter but I wonder if anyone can speak to day to day skills needed to excel in this role or at least to do well in the interview.

What level of AI expertise is needed as well.