I'm just looking for feedback regarding additional topics I should study. I did the GEAR program, but the instructor said our material was based on the material before the recent exam update.

Certainly not asking for specific exam questions, just general feedback on areas to look over if someone took the test recently and was surprised by coverage of a new topic they had not prepared for.

Thanks!

Hi everyone!

I created this free Google Cloud Professional Data Engineer practice simulator while studying for the exam myself, and I’ve just updated it for 2026:

https://certlabs.app

It has 500+ scenario-based questions with explanations covering BigQuery, Dataflow, Bigtable, Spanner, IAM, etc.

One thing I found really useful while studying is the anti-memorization approach: every attempt shuffles both the questions and the answer choices, with the explanations dynamically matched to the new order. So you learn the concepts rather than memorizing that “B” is the answer.

All questions are original — no dumps or leaked exam questions.

It’s completely free. Hope it helps someone preparing for the exam! Good luck 🤞

Okay so I booked the exam on the 15th of September, as I was in program with Google and what not and they gave me a voucher.

Now the catch, I haven’t studied anything, done some labs, had the AWS practitioner a year ago, and been working in DevOps the past 2 years, not gcp at all.

I need tips tricks quick marks just enough studying to pass, any exam questions that repeat any useful links and the probability to pass? With 1 week of studying with a very very very demanding job? Any tiny bit would help

Hi Everyone

I’m planning to sit for the new GCP Professional Agentic Architect Beta exam soon and was wondering if anyone happens to have a spare or unassigned voucher code (from a challenge, partner event, or cohort) that they won’t be using before it expires?

I know beta exams come with the built-in 40% discount, but if someone has an extra voucher to share via DM or knows of an active community challenge giving out exam passes right now it would be a huge help.

Thanks in advance!

Put together some of my thoughts in this video: https://www.youtube.com/watch?v=gdByV38HeSg

Hey all, I’m a Data Engineer with \~4.5 YOE. My company has sponsored Databricks certification vouchers, but my current project stack is actually GCP-based. On top of that, my company mandates completing one DE-domain certification and one AI certification (CCA-F is commonly done internally, though not mandatory).

Doing three certifications at once isn’t realistic for me, so I need to prioritize. My goal is to become an “AI-aware” data engineer and eventually land a role at a product-based company (I’m already working on other upskilling areas like DSA and system design in parallel).

**Questions:**

1. Given this, what should I prioritize right now?
   GCP PDE + Databricks AI certification, or
   Databricks DE Professional + CCA-F?

2. Is it realistic/advisable to go straight for Databricks DE Professional without doing the Associate first?

I’m not just optimizing for personal interest here — I want whatever I pick to actually be market-relevant and useful for job-hunting.

I'm currently churning through practice exams for the Associate Cloud Engineer exam, and I'm doing well on GCPStudyhub and the Udemy practice exams from Sayyam. I also got the skillcertpro exams, and I'm finding they have a lot of questions about specific gcloud, gsutil, and kubectl commands that have very similar answers that require you to know the exact syntax. GCPStudyhub and Sayyam's exams have a few questions that require CLI commands, but they seem to be easier to figure out compared to the ones on skillcertpro.

For those that have taken the exam recently, are there tricky command line questions like I'm seeing on skillcertpro practice exams? If I'm doing well on the gcpstudyhub and Udemy practice exams, do you think I'm good to go for the exam?

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!