Google Cloud Generative AI Leader Practice Questions: 5 Drills and the Question Split
The paper is 50-60 questions in 90 minutes and costs $99. Here is how those questions spread across the four domains, five drills written to the published objectives, and the business-framing trap that catches technical candidates.
- 50-60Questions
- 90 minTime
- $99Cost
- 3 yearsValid
Table of Contents
The Google Cloud Generative AI Leader paper is 50-60 questions in 90 minutes and costs $99. Google does not publish percentage weights for this exam, so treat the four domains as roughly equal and revise the one you are weakest in rather than betting on a weighting that has never been stated.
What follows is the question split by domain, then five practice questions written to Google Cloud’s published objectives, each with the reasoning for the right answer and for every wrong one. These are original practice items, not real exam content – the point is to show you how the questions are shaped and where the traps sit.
How the 50-60 questions split
Google Cloud lists the exam sections but does not publish percentage weights for this exam, so nobody can tell you an exact question count per domain. The honest version is below: the sections, and what an even split across a 55-question paper would look like.
| Domain | Weight | If even | |
|---|---|---|---|
| Fundamentals of gen AI | not published | ~14 | |
| Google Cloud's gen AI offerings | not published | ~14 | |
| Techniques to improve gen AI model output | not published | ~14 | |
| Business strategies for a successful gen AI solution | not published | ~14 |
Be sceptical of precise weightings. Sites quoting exact percentages for this exam are inventing them. Google Cloud has not published them, so revise the sections evenly and spend your extra time on whichever one you can least explain out loud.
Five practice questions
Answer each one before reading the key underneath it.
A retail company wants a model to answer staff questions using its own product catalogue, which changes weekly. The team wants to avoid retraining every week. Which approach fits best?
- A Fine-tune the foundation model on the catalogue every week
- B Use retrieval-augmented generation so the model reads the catalogue at query time
- C Increase the model's temperature so it produces more varied answers
- D Train a new foundation model from scratch on the catalogue
Answer: B
Retrieval-augmented generation keeps the model fixed and pulls current documents into the prompt at query time, so a weekly catalogue change means reindexing content, not retraining a model.
Why the others fail
A Fine-tuning bakes knowledge into weights, so a weekly change means a weekly training run - exactly the cost the team asked to avoid.
C Temperature controls randomness of wording, not what the model knows. Raising it makes answers less predictable, not more current.
D Training a foundation model from scratch costs orders of magnitude more than any of the alternatives and is never the answer to a freshness problem.
A business analyst with no coding background needs to build and test prompts against Google's foundation models, then hand a working prompt to engineering. Which product is the intended starting point?
- A Vertex AI Studio
- B Cloud Run
- C BigQuery BI Engine
- D Cloud Composer
Answer: A
Vertex AI Studio is the low-code surface for designing, testing and iterating prompts against Google's foundation models before anything is wired into an application.
Why the others fail
B Cloud Run hosts containers. It runs an application that might call a model, but it is not where prompts are designed.
C BI Engine is an in-memory analysis accelerator for BigQuery. It has nothing to do with prompt design.
D Cloud Composer is managed Apache Airflow for orchestrating pipelines, not a prompt workbench.
A summarisation prompt keeps returning answers in an inconsistent shape - sometimes bullets, sometimes prose. The team cannot change the model. What is the cheapest fix to try first?
- A Fine-tune the model on a thousand correctly formatted summaries
- B Add worked examples of the desired output format to the prompt
- C Lower the model's token limit so it has less room to vary
- D Switch to a larger model
Answer: B
Few-shot prompting - showing the model two or three examples of the exact output shape you want - is the standard first move for format inconsistency and costs nothing but prompt tokens.
Why the others fail
A Fine-tuning works but is the expensive answer to a problem that examples usually solve. The question asks for the cheapest first try.
C A smaller token limit truncates output. It does not teach the model which format to choose.
D A larger model may still vary its format. Model size is not a formatting control.
An executive asks how to judge whether a gen AI customer-service pilot was worth continuing. Which measure best answers that question?
- A The number of tokens processed per month
- B The model's benchmark score against published leaderboards
- C Change in average handling time and customer satisfaction against the pre-pilot baseline
- D The number of prompts engineers wrote during the pilot
Answer: C
Business value is measured against a baseline in the metric the business already cares about. For customer service that is handling time and satisfaction, compared with what those numbers were before the pilot.
Why the others fail
A Token volume is a cost input. High usage can mean value or waste; on its own it says nothing.
B Leaderboard scores describe the model in general, not the outcome in this company's queue.
D Prompt count measures effort spent, not value delivered.
A team reports that their model confidently produced a product specification that does not exist. What is this called, and what is the correct first mitigation?
- A Overfitting; reduce the number of training epochs
- B Hallucination; ground the model in verified source documents and cite them
- C Data drift; retrain on more recent data
- D Bias; rebalance the training set
Answer: B
A fluent, confident but fabricated output is a hallucination. The standard first mitigation is grounding - forcing answers to come from retrieved, verifiable sources and surfacing those citations.
Why the others fail
A Overfitting is a training-time failure where a model memorises its training set. It is not the name for invented output at inference.
C Data drift is the gradual mismatch between training data and live data over time. This answer was invented in one response, not degraded over months.
D Bias describes systematically skewed outputs across groups, which is a different failure from fabricating a specific fact.
What catches people out
- This is a business exam wearing a cloud badge. Candidates from engineering lose marks by picking the most technically sophisticated option when the stem asked what a business should do.
- Know which Google product does what at a one-line level - Vertex AI Studio, Model Garden, Agent Builder, Gemini for Google Cloud. You are not asked to configure them, only to place them.
- Google publishes no percentage weights and no passing score for this exam. Anyone quoting you a precise pass mark is guessing.
What it really costs to pass
The sticker price is $99, but that is the cost of passing first time. The number worth budgeting is different.
- First attempt: $99.
- Resit: Google Cloud does not publish a discounted resit alongside the headline fee, so budget a second full fee unless the official page says otherwise when you book.
- Renewal: the credential lasts 3 years, so the honest cost is the fee divided across that period, plus whatever renewal Google Cloud requires at the end of it.
- Your time: 90 minutes in the chair, and realistically several weeks of preparation before it. That is the largest cost on this list and the only one you cannot pay to avoid.
There is no published pass mark. Google Cloud does not publish one for this exam, so you cannot calculate how many mistakes you can afford. Prepare for a comfortable margin rather than aiming at a threshold nobody has stated.
Who should sit it: there are no formal prerequisites. Going in well under that bar is usually how people end up paying the fee twice.
How to prepare
A workable sequence for this exam, assuming you already work near the material:
- Read the official guide first. Everything on this page is secondary to what Google Cloud publishes at the link in the sidebar, and guides get revised.
- Work the heaviest domain until you can teach it. Use the table above to decide which one that is.
- Practise under the clock. 90 minutes for 50-60 questions is roughly 98 seconds each. Knowing the material and being able to apply it at that pace are different skills.
- Review every wrong answer to the level of the distractor. Being able to say why the other three options fail is what turns a 60% into a pass.
Frequently asked questions
How many questions is the Google Cloud Generative AI Leader exam?
Google states 50-60 multiple choice questions in 90 minutes. The range means you cannot count on a fixed number, so pace yourself at roughly 90 seconds per question.
Does the Generative AI Leader exam require hands-on Google Cloud experience?
No. Google lists no prerequisites and no recommended experience, and describes it as suitable for any job role with or without hands-on technical experience.
How much does the Generative AI Leader exam cost?
$99 plus tax where applicable, which makes it one of the cheapest certifications Google offers.
How long is the Generative AI Leader certification valid?
Three years. Google offers a renewal path within the renewal eligibility period.
Sources
Every figure on this page comes from the certifying body rather than a third-party summary:
- https://cloud.google.com/learn/certification/generative-ai-leader
- https://services.google.com/fh/files/misc/generative_ai_leader_exam_guide_english.pdf
Checked September 2, 2026. Formats and fees change – confirm against Google Cloud before you pay for anything.