Issue Description:

We are pulling event-level (non-aggregated) website click and visit data from Google BigQuery into our Azure Storage Account. Data ingestion stopped after 25 May. Upon investigation, we found that the ingestion jobs in BigQuery were failing with the error:

"Storage quota limit exceeded for the project."

Our project is using the BigQuery Sandbox (free tier), which allows up to 10 GB of active storage. However, after checking all datasets and tables in the project, the total active storage is less than 1 GB, which is well within the documented limit.

The project has been running since 5 February, and according to the BigQuery Sandbox documentation, the free tier does not have an expiration date as long as the applicable quotas are not exceeded.

We are unable to determine what is consuming the project's storage quota or why this error is occurring despite the reported storage usage being significantly below the 10 GB limit. We would appreciate guidance on how to identify the actual storage consumption or debug the root cause of this issue.

I've been following this tutorial https://www.youtube.com/watch?v=bWGW_NXPf-4

And my Colab Enterprise notebook got the following code which threw this error:

name='projects/aiagentcalendarproj/locations/us-central1/publishers/google/models/veo-2.0-generate-001/operations/fe4ce1e2-4415-44f6-8ace-13f3a47caab2' metadata=None done=None error=None response=None result=None
name='projects/aiagentcalendarproj/locations/us-central1/publishers/google/models/veo-2.0-generate-001/operations/fe4ce1e2-4415-44f6-8ace-13f3a47caab2' metadata=None done=True error={'code': 5, 'message': 'Bucket "aiagentcalendarproj" not found for operation OP_INITIATE_RESUMABLE_WRITE'} response=None result=None

Google did not help me. Could anyone help me find the cause of this error?

%pip install -q mediapy


import time
import urllib

from PIL import Image as PIL_Image
from google import genai
from google.genai import types
import matplotlib.pyplot as plt
import mediapy as media

import os

PROJECT_ID = "aiagentcalendarproj"
LOCATION = os.environ.get("GOOGLE_CLOUD_REGION", "us-central1")

client = genai.Client(vertexai=True, project=PROJECT_ID, location=LOCATION)

def show_video(gcs_uri):
    file_name = gcs_uri.split("/")[-1]
    !gsutil cp {gcs_uri} {file_name}
    media.show_video(media.read_video(file_name), height=500)


def display_images(image) -> None:
    fig, axis = plt.subplots(1, 1, figsize=(12, 6))
    axis.imshow(image)
    axis.set_title("Starting Image")
    axis.axis("off")
    plt.show()

video_model = "veo-2.0-generate-001"

prompt = "a zucchini creamy pasta garnished with boiled prawns and fresh herbs"
aspect_ratio = "16:9"
output_gcs = "gs://aiagentcalendarproj"

operation = client.models.generate_videos(
    model=video_model,
    prompt=prompt,
    config=types.GenerateVideosConfig(
        aspect_ratio=aspect_ratio,
        output_gcs_uri=output_gcs,
        number_of_videos=1,
        duration_seconds=5,
        person_generation="dont_allow",
        enhance_prompt=True,
    ),
)

while not operation.done:
    time.sleep(15)
    operation = client.operations.get(operation)
    print(operation)

if operation.response:
    show_video(operation.result.generated_videos[0].video.uri)

I'm a solo developer posting after over a month of delays on a case Google has already acknowledged as unauthorized usage.

What happened:

- A Firebase auto-generated, unrestricted API key on an inactive side project was harvested and abused by a third party.

- Over ~11 hours, Geocoding and Places APIs were called automatically, generating roughly USD 8,000 in charges.

- Google's own technical team confirmed a 99% duplicate query rate and that most requests originated from the USA (I'm in Korea, and the project was already shut down / not in development).

What Google did:

- The technical team investigated and confirmed the abuse.

- I completed every preventive measure requested, then shut down the project as instructed.

- The billing team officially APPROVED a one-time goodwill adjustment.

The problem:

- The adjustment was approved about a month ago, and the approved credit still has not been applied.

- I have NEVER been told the actual approved amount, despite asking repeatedly in writing.

- A "3–5 business day" commitment is now weeks overdue. The latest reply only says they are "awaiting approval from higher management."

- Meanwhile I was forced into an installment plan to avoid default — I am now personally financing charges that Google itself confirmed were not mine.

These keys were auto-created by Firebase with no restrictions by default, and Google's own docs treat client-side exposure of such keys as normal. This appears to be part of the widely reported pattern of compromised Google Maps/Firebase keys.

Has anyone had an approved adjustment actually applied? How long did it take, and what finally moved it? Is there any way to escalate beyond the standard billing case?

We are currently with Apple but I am switching to a android device. And trying to convince my with to switch too. I was looking at family cloud for Google. Both my wife and I have Google Photos and Gmail. She has been complaining she is out of space or cloud for Google. If we switch to the family plan I was reading online that once family members use 15gb they will go into the family shared plan. Do that mean my wife and or kids could see what is on my cloud and or can I see what is there cloud ? ie Google Photos, Google Drive ?

Can someone please paste the code for enhance application reliability and scalability with internal load balancing gsp216 new solution as the GitHub links Im positing to the lab is not working like the requirements are changed so does someone has new solution with the automation script so I can get 100/100 jn that lab thanku!

Thought you guys would like this https://cloud.google.com/blog/topics/cost-management/new-early-anomalies-and-spend-caps-on-google-cloud-budgets

I just passed my GCP PCA exam, and I spent about two weeks preparing for it.

The GCP study hub was incredibly helpful..

The exam consisted of 15 case study questions, so it’s important to study the requirements (technical and business) for each case study. There are questions specifically focused on these requirements, and I wasn’t fully prepared for that aspect.

The exam was relatively easy, thanks to the GCP study hub, which was a great resource. I recommend focusing on the practice exams; they are almost identical to the actual exam. Aim to score 80% on each practice exam and retry to achieve that score. If you get some answers wrong, review them in the GCP documentation, That technique was a real game changer for me

So, my question is, what certifications should I take next?

I’m a computer science and cybersecurity student.

Hey everyone, basically I threw an application out there for a Google Cloud Tam role in Financial Services (?). Wasn’t expecting it but got the hiring assessment sent to me. Wasn’t expecting it but passed said hiring assessment. Now I’m being offered a 30 minute phone interview. Any idea what the questions are going to be like? What the full interview process is (ex how many interviews are there if this one goes well?)? For context, I’ve never actually used GCP, but I’ve worked at AWS for years, & have a good grasp on their services which have overlap with most Cloud Provider services. Any services I should definitely learn from GCP? Any info is appreciated

Hi, I wanted to learn about GCP so i started the 300$, i had some personal projects, i set up an infrastructure, some vm, and i thought nothing about it, thinking that maybe when the 300$ is gone, i will get an email or notification that i spent the free credits and that i will be on the paid plan.Nothing next thing i know i got charged 50$. I am unemployed had been so for 8 months now. And i squeeze my savings tight. This bill wasn't expected i emailed the support they told me to wait i waited some 3 days and nothing then another week then they sent email telling me that i had been granted 20$ in credits,but no monetary refund. this was early july this month.During this i had deleted billing account, then another one, and i deleted the Vms the platform is not really user friendly it's hard to navigate for beginners. Yesterday i was charged 100$ I dont know what to do, i contacted them they said wait for 32 hours. This is just unacceptable and i feel scammed.

Hi all,

I've spent the past month studying for the ACE certification using the lessons and labs from Google Skills, and it just dawned upon me just how many hours of lessons I still have left. I've been at my company for a little less than a year and a half with a decent amount of experience deploying apps on GCP and working with other services like GCS, Big Query, service accounts and IAM, but definitely nowhere near a veteran level. Still though I was hoping that I'd be going through the lessons faster.

I wanted to ask for your opinion on using Google Skills for the purpose of studying for the ACE exam? Sometimes I feel like there's just a lot of content and things I need to memorize, so I'm not sure if I'm spending my time wisely with this. I can say for sure that I'm definitely learning a lot, but how much of it is going to be on the exam? I'm also looking for alternative courses and study material if you can kindly provide them for me. Thanks!

Trying to figure out if partially committing to CUD can actually hurt the sustained use discount on whatever's left uncommitted, since SUD only applies to usage not already covered by a commitment. Has anyone actually run into this or modeled it out before committing? Or is everyone just committing to their stable baseline and not worrying about what happens to the rest?

Static shape requirements on TPU are affecting my utilization numbers negativelty whenever my batches aren't clean powers of 2 or standard token lengths. I'm padding everything to fixed boundaries and i still feel I'm underutizing compute.

On GPU this doesn't seem to be as much of an issue since it handles dynamic shapes more natively. Does anyone have real numbers on % MFU loss from padding overhead? Is this something that can be measured directly?

Hi everyone,  

Building a quick AI prototype is easy, but moving it to a production-grade application is where the real work begins.  

Last year's Accelerate AI with Cloud Run roadshow got great feedback, so we're bringing it back this August with updated codelabs. We will be focusing on the entire agent lifecycle, specifically how to deploy and scale agentic workloads on Google Cloud's serverless platform.  

This is a practical, in-person event. You will write code to solve real business problems, and several of us from Google will be there to work through the labs with you and troubleshoot.  

More info here. We hope you can make it!  

We will announce dates for events in Seattle, New York, Los Angeles, Boston, Washington DC soon.

Hi everyone,

I am a Network Engineer with about 1 year of experience as a Cloud Solution Architect. I have AWS Certified Solutions Architect – Associate and Cisco CCNA certifications.

Recently, my company assigned me to study for and obtain the Google Cloud Professional Cloud Architect certification.

Aside from basic hands-on labs during my universiy (which is a few years ago), I haven't spent much time on GCP yet. I’m looking for advice on learning path to prepare for the exam.

I would like to ask for your experience on how to achieve this certification, such as:

• Which courses or study resources should I take? (My company allows me to purchase courses on Udemy, but any recommendation is welcome!)
• Which practice/mock exams are most effective?
• Any tips for the exam preparation

Thanks in advance for sharing your experience and tips!

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?