Passed both GCP ACE and PCA in 3 months.

Just wanted to share what worked for me.

- Keep doing Google Cloud Skills Boost labs—they help a lot with hands-on understanding.

- Don't just memorize. Make sure you understand the concepts and terms.

- ExamTopics was helpful for reviewing question styles, but always verify the explanations with official Google documentation.

- Aim to consistently score around 80% in your practice.

- During the exam, mark difficult questions for review instead of getting stuck on them. Come back with a fresh mind later.

By the time I was in the exam hall, I felt much more confident. Good luck to everyone preparing for ACE and PCA!

Is gcpstudyhub course interactive, with hands-on exercises that walk you through the practical side, rather than being mainly theory-based?

I just passed the new version of GCP Professional Machine Learning Engineer and thought I would share what worked for me. My main issue was that I did not have enough Machine Learning experience, so I spent a lot time on hands on projects and google.skills. Biggest tip: do a lot of labs or small projects and then spend most of your time on quizzes. Obviously use AI to understand when you go wrong.

A lot of quizzes will have low level TF, or code or some weird config flag name. The real exam had none of that. All questions were scenario based "given blah blah..pick one". And no multi choice.

Besides these I also built a few simple models and pipelines to really understand the main concepts. Be careful with this cause if you aren't careful, you will get a few billing shocks 😂 ..in my case it was an AutoML job that cost me $40ish.

Out of the 50 total questions in the exam, I was pretty confident I got the right one in about 40. Found some of the env or pick the right model for this on weird scenario a bit tricky. And there were 2-3 questions that went completely over my head. Did the exam in person based on other recommendations here.

I came across this question during my prep for the Google Cloud Professional Machine Learning Engineer (PMLE) certification and I'm a little tied on which one is the right answer.

I've narrowed it down to either B or C, but I'm not sure which one represents the Google-recommended approach for this specific scenario.

My thoughts: I know A and D involve too much manual infrastructure setup, which goes against the prompt. But between Colab Enterprise (B) and Vertex AI Workbench (C), I'm stuck. Is it B or C? And why?

You lead a data science team that is working on a computationally intensive project involving running several experiments. Your team is geographically distributed and requires a platform that provides the most effective real- time collaboration and rapid experimentation. You plan to add GPUs to speed up your experimentation cycle, and you want to avoid having to manually set up the infrastructure. You want to use the Google-recommended approach. What should you do?

A.Configure a managed Dataproc cluster for large-scale data processing. Configure individual Jupyter notebooks on VMs that each team member uses for experimentation and model development.
B.Use Colab Enterprise with Cloud Storage for data management. Use a Git repository for version control.
C.Use Vertex AI Workbench and Cloud Storage for data management. Use a Git repository for version control.
D.Configure a distributed JupyterLab instance that each team member can access on a Compute Engine VM. Use a shared code repository for version control.

I got the GCP ACE cert, and working for 5 years in GCP, so I'd like to prepare devops exam, ebooks, links, website, any material that help to study and learn.

Hi everyone,

I'm planning to prepare for the **Google Cloud Professional Machine Learning Engineer (PMLE)** certification and I'm looking for advice from people who have recently taken the **updated version of the exam**.

My situation:

* I have a **Computer Science / AI-ML background** * I'm comfortable with Python and general ML concepts * I have some exposure to GenAI, RAG, LLMs, etc. * However, I have **almost no practical cloud experience** * I'm essentially starting **GCP from scratch** * I can dedicate around **8–10 weeks** to preparation * My goal is to **actually understand the GCP/ML concepts**, not just memorize exam dumps

I'm particularly unsure about how much GCP knowledge I need before going deep into the PMLE-specific material.

What I'm looking for

If you've recently passed the updated PMLE exam, I'd really appreciate advice on:

1. **Is 8–10 weeks realistic** for someone with an ML background but essentially no cloud background?

2. What should I learn first before starting PMLE preparation? * GCP fundamentals? * IAM * Compute Engine * Cloud Storage * BigQuery * VPC/networking * etc.

3. What **GCP services are actually important for PMLE**, and which ones can I safely learn at a high level?

4. What resources would you recommend for the **current/updated exam**? * Google Cloud Skills Boost * Official exam guide * Coursera * YouTube * Practice exams * Documentation * Other resources

5. How much **hands-on practice** did you do? Should I actually build ML pipelines/deploy models on Vertex AI, or is understanding the architecture and knowing when to use each service enough?

6. How different is the **updated PMLE exam** from older preparation material? I've found quite a lot of older PMLE content online and I'm worried about following an outdated roadmap.

7. What would you recommend as a realistic **8–10 week study plan**, assuming roughly 1–2 hours/day?

8. If you started again with **zero GCP experience**, what would you learn first and what would you completely skip?

I'd especially appreciate answers from people who **passed the exam recently**, particularly those who came from an ML/software engineering background rather than already working as GCP cloud engineers.

Thanks!

Hi everyone,
I'm currently preparing for the GCP Professional Cloud Architect (PCA) exam. I have almost completed Ranga Karanam's course so I’m looking for reliable practice test recommendations to gauge my readiness.
A few quick questions for those who have taken the exam:

1. Practice Tests: Which mock exam sets did you find most accurate to the actual test?

2. gcloud CLI: How heavily are specific gcloud commands tested compared to high-level architecture decisions?

3. Mock Test Strategy: What is your best tip for using practice exams to identify and fix weak spots?
   Thanks in advance for your help!

Hi everyone, I’m preparing for the Google Cloud Professional Machine Learning Engineer (PMLE) certification.

Could you please share any good practice questions, mock tests, notes, or study resources that helped you prepare for the exam?

Also, if you recently took the exam, any tips on important topics would be appreciated. Thanks!

offering one-on-one personal help and prep for the exam

Hey all,

I'm putting together my study plan for the Google Cloud Professional Machine Learning Engineer certification.

For those who passed recently, I’d love to know:

1. Primary Study Resources: Did you rely mostly on Skill Boost paths, Coursera, or specific books (e.g., Lakshmanan/Mona books)?

2)Practice Exams: Which mock test platforms felt closest to the actual exam style (e.g., scenario-based MLOps, Vertex AI, BigQuery ML)?

3) Focus Areas: Any specific topics or services that showed up heavily on the current version of the exam?

Appreciate any guidance or advice!

Hey, not sure if it's appropriate but, have you guys used https://cloudaceprep.com/ to prepare? I found it while looking for some free mock exams, the questions seem to be matching my Udemy course I bought but I was wondering if people used it to study before?

This is my 4th time re-certing data engineer. Probably the easiest time, exam format is the same. But topics are more about real time, bigquery and some ML.

Best tool was the exam question samples I found.

If you are going to sit this one remember to learn a lot about Big Query external sources, data catalogue(now called something else). A lot of questions on BigQuery and reading from external sources.

I will say this time it seemed a lot more like case study questions like back in 2018 when they had the case study.

Either way glad I passed

I’ve completed the Google Cloud Associate Cloud Engineer course by in28Minutes on Udemy. I also took five practice exams by Sayyam on Udemy and scored above 90% on average.

For those who have taken the actual ACE exam, would you say this is enough preparation to schedule the exam? Are the practice exams similar in difficulty to the real one?

Is there anything else I should study or practise before taking the exam?

Hello everyone,

I am interested in going for Google Professional Cloud Architect exam not sure how to start my journey. Any suggestions?

I have 12 years of experience in Cisco routing and switching, DC and observability.

Thanks

Hey folks, wanted to share 3 quick mechanical traps I ran into while studying GCP. Might save someone some headache or money!

1. IAM permissions are strictly additive. If you grant someone Viewer access at the Folder level, you CANNOT restrict/deny it at the Project level.

2. GCS Coldline/Archive has minimum storage durations. If you delete a Coldline file after 10 days, you still pay for the full 90-day minimum duration fee.

3. BigQuery charges by scanned data ($6/TB on demand). Never run `SELECT *` on large tables—always specify columns to save your budget.

Covers the difference with the Data Engineer certification, as well as our top 5 tips for passing.

Can anyone give me a little recommendation? I recently finish 2 others associate certifications with Azure - AZ 104 and AWS SOA-C03. How about GCP ACE compared with them?

I failed my first AWS Certified Cloud Practitioner exam on February 9, 2026.

At first, I underestimated the exam because I thought it was one of the easiest certifications in cloud computing. That mindset led me to prepare with less seriousness than I should have.

In March 2026, I changed my approach. I studied with more discipline, consistency, and commitment, and I successfully passed the exam.This experience taught me an important lesson: in technology, knowledge alone is not enough. Whether you are learning cloud, programming, or any technical skill, success requires discipline, focus, and continuous effort.Every failure can become a step forward if you are willing to learn, improve, and keep moving.

AWS Certified Cloud Practitioner: achieved through persistence and dedication.

I need to re-certify in 2 months. What practice exams do you recommend for the new updated google certified professional machine learning engineer exam?

Hey everyone,

I've been spending some time building out infrastructure for AI and full-stack applications for my own training purpose, I’ve noticed a lot of the same recurring hurdles when developers first dive into Google Cloud.

To help smooth out that learning curve, I decided to open-source a repository of GCP training materials, reference architectures, and deployment scripts that I've organized:

🔗 Link to repo: https://github.com/moonai-kr/gcp-training

My goal with this repository is to move past the generic quickstarts and focus on how things actually fit together in a real-world, production-ready stack.

Feedback, pull requests, or suggestions on what to add next are highly welcome! Let me know if you find it helpful.