Google Generative AI Leader Exam

I recently passed the Google Cloud Generative AI Leader exam. The experience was rewarding on its own, but it also fit into a broader certification process that I have refined over time across previous Google Cloud exams.

For anyone preparing for this exam or a similar certification, the most useful lesson is that lightweight structure matters more than cramming. A clear review loop, targeted notes, and realistic timeboxing can make the preparation much more effective.

Start with the official sample test

My first step was taking the sample questions available on the official Google Cloud website.

That gave me two immediate benefits:

• a practical view of the question style
• an early signal about which topics needed more review

After finishing the sample test, I reviewed each answer carefully, noted the gaps, and kept the result link bookmarked together with the test date so I could compare progress later.

Turn weak areas into a study list

Once the sample test exposed the weak spots, I converted them into a short topic list for revision.

That step is simple, but it matters. Without it, study sessions can become too broad and inefficient. With it, the preparation becomes more focused and easier to schedule.

I also reviewed the official exam guide in detail because sample questions never cover the whole scope. The guide helped surface areas that deserved attention even if they did not appear in the sample set.

Create your own questions and notes

One habit that consistently helps me is writing my own questions.

For this exam, I drafted around ten questions, mixing broader conceptual prompts with more technical ones. That process forced me to think in the same way the exam does instead of just rereading source material passively.

I also kept organized notes throughout the preparation process. Whether I was reviewing videos, reading documentation, or revisiting earlier materials, I captured the important points on my laptop so they were easy to search, revise, and print if needed.

Reuse what already works

I did not start from zero.

Some of my preparation reused notes from the Professional Machine Learning Engineer exam, especially where the topics overlapped with AI platform capabilities and applied GenAI concepts. I adapted parts of those notes into audio summaries and listened to them while exercising or during short free periods.

NotebookLM was particularly useful in that workflow. It helped organize material, summarize key themes, and generate audio-style review content from my notes. For this kind of certification prep, that turned passive time into useful repetition.

Give the exam a clear deadline

I recommend booking the exam one to two weeks in advance.

That creates a concrete preparation window without dragging the process out too long. At the same time, it is helpful to know that short-notice booking can be possible. In my experience, a slot may be available even shortly before the exam starts.

The key is not the exact booking timing. It is using the exam date to create focus.

Prepare the online exam environment carefully

I chose the online proctored format to avoid travel time.

That convenience is real, but it only works well if the setup is clean and predictable. Before the exam, I recommend:

• choosing a quiet room that can be closed or locked
• clearing the desk of unrelated items
• checking the webcam and microphone
• making sure the internet connection is stable
• confirming the secure browser is installed and updated

These details reduce stress and help avoid last-minute friction before the session begins.

What the exam experience felt like

The exam lasted about 90 minutes and included roughly 50 questions.

Overall, I did not find it overly difficult, but several questions were long enough that careful reading mattered. That made pacing more important than speed.

My practical approach was:

• read every question carefully
• mark uncertain questions and return later
• use the full allotted time if needed
• treat the exam as both an assessment and a learning exercise

That last point is especially useful. Even when a question is challenging, it can still reinforce how Google wants you to think about real GenAI use cases and platform decisions.

Post-exam review still matters

After the exam, I wrote down the topics and question styles that felt less clear.

Those notes are valuable even after a pass. They help with future refreshers, make retakes easier if a newer exam version appears later, and improve the preparation process for related certifications.

I also noted that Credly typically issues the badge a couple of days after the exam, so there can be a short delay before the result is reflected there.

Final recommendation

The Generative AI Leader exam felt more approachable than many associate-level or professional-level Google Cloud certifications, but it still benefited from structured preparation.

If you are planning to take it, I would recommend a simple method:

• start with the sample test
• turn weak areas into a focused study list
• review the exam guide carefully
• create your own questions
• reuse notes in formats that fit your daily routine

That approach kept the preparation efficient and helped reinforce real understanding rather than just short-term recall.

References

• Google Cloud Generative AI Leader certification
• Google Cloud certifications overview
• NotebookLM
• Credly