2026 New Generative-AI-Leader Dumps – Real Google Exam Questions [Q36-Q52]

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2026 New Generative-AI-Leader Dumps – Real Google Exam Questions

Dependable Generative-AI-Leader Exam Dumps to Become Google Certified

Google Generative-AI-Leader Exam Syllabus Topics:

Section	Weight	Objectives
Google Cloud’s generative AI offerings	35%	– Describe Google Cloud’s gen AI product and service portfolio. 1. Gemini for Google Cloud 2. Model Garden 3. Vertex AI Studio 4. Vertex AI 5. Google Workspace – Identify the use cases and strengths of Google’s foundation models. 1. Gemma 2. Imagen 3. Veo 4. Gemini
Fundamentals of generative AI	30%	– Identify the core layers of the gen AI landscape and the business implications. 1. Applications 2. Infrastructure 3. Agents 4. Models 5. Platforms – Describe core generative AI (gen AI) concepts and use cases. 1. Identifying the stages of the machine learning lifecycle (e.g., data ingestion, data preparation, model training, model deployment, model management) and the Google Cloud tools for each stage 2. Describing the machine learning approaches (e.g., supervised, unsupervised, reinforcement) 3. Identifying how to choose the appropriate foundation model for a business use case (e.g., modality, context window, security, availability and reliability, cost) 4. Defining core gen AI concepts (e.g., artificial intelligence, natural language processing, machine learning, generative AI, foundation models, multimodal foundation models, diffusion models, prompt tuning, prompt engineering, large language models) – Describe how various data types are used in gen AI and the business implications. 1. Explaining the characteristics and importance of data quality and data accessibility in AI (e.g., completeness, consistency, relevance, availability, cost, format) 2. Identifying the differences between labeled and unlabeled data 3. Identifying the differences between structured and unstructured data, and identifying real world examples of each type
Business strategies for a successful gen AI solution	15%	– Describe change management best practices and their importance. 1. Creating a culture of innovation 2. Enabling AI adoption – Describe Google’s approach to responsible AI and its importance. 1. Responsible AI best practices 2. Google’s AI principles – Describe best practices for a successful gen AI project. 1. Building a business case 2. Choosing the right model 3. Evaluating AI solutions
Techniques to improve gen AI model output	20%	– Describe prompt engineering techniques and their purpose. 1. Chain of thought 2. Few-shot 3. One-shot 4. Zero-shot – Describe how grounding can be used to improve model output. 1. Grounding with Google Search 2. Grounding with enterprise data – Describe the process of fine-tuning gen AI models. 1. Reinforcement learning from human feedback (RLHF) 2. Supervised tuning

QUESTION 36
A sales manager wants to responsibly use generative AI (gen AI) to increase efficiency with their existing tasks. They want to allow the sales team to focus on building customer relationships and closing deals. How should the sales team use gen AI?

To replace the sales team ‘ s CRM system with a more intuitive and user-friendly interface.

To analyze customer interactions on social media and automatically generate sales pitches tailored to their public profiles.

To draft emails and provide real-time insights about customer needs.

To automate creative content like blog posts and social media updates to attract new leads.

QUESTION 37
What is a key advantage of using Google ‘ s custom-designed TPUs?

TPUs are lightweight processors intended for deployment on edge devices.

TPUs increase the storage capacity and data retrieval speeds within Google Cloud data centers.

TPUs are specialized AI processors that excel at parallel processing for machine learning workloads.

TPUs are primarily designed to improve the general processing speed of virtual machines in the cloud.

QUESTION 38
An organization wants to use generative AI to create a chatbot that can answer customer questions about their account balances. They need to ensure that the chatbot can access previous portions of the conversation with the customer. Which prompting technique should they use?

Use zero-shot prompting.

Use role prompting.

Use few-shot prompting.

Use prompt chaining.

QUESTION 39
A company wants to build a model to classify customer reviews as positive, negative, or neutral.
They have collected a dataset of thousands of customer reviews, and each review has been manually tagged with the corresponding sentiment: positive, negative, or neutral. What machine learning should the company use?

Deep learning

Unsupervised learning

Reinforcement learning

Supervised learning

QUESTION 40
An organization with a team of live customer service agents wants to improve agent efficiency and customer satisfaction during support interactions. They are looking for a tool that can provide real-time guidance to agents, suggest helpful information, and streamline the support process without fully automating customer conversations. Which component of Google’s Customer Engagement Suite should they use?

Agent Assist

Conversational Agents

Conversational Insights

Google Cloud Contact Center as a Service

QUESTION 41
At mcnz.com your AI team wants one versatile model that they can prompt or fine tune to handle text generation, multilingual translation, and question answering across 18 languages for three product lines. What is the term for a large pretrained model that serves as a general purpose starting point for many downstream applications?

Task-specific model

Foundation model

Vertex AI Model Garden

Reinforcement learning agent

QUESTION 42
A learning and development team wants to quickly create a new hire training video with a custom avatar and voiceover that matches their company’s branding and key messaging. They did not receive any money to spend on the production. What should they do?

Generate the video frames with Imagen.

Prompt the Gemini app to create a video.

Train a model with Vertex AI and produce a video.

Create a video with Google Vids.

QUESTION 43
What is a characteristic of Google Cloud as a generative AI company?

Google Cloud provides fully autonomous AI agents that require zero configuration or management overhead.

Google Cloud has an AI-first focus that enables innovation, with continuous updates and broad integration across its platform.

Google Cloud ensures that all generative AI models and data are completely secured and isolated from external networks.

Google Cloud relies on proprietary, closed-source AI technologies for maximum security benefits.

QUESTION 44
A boutique sneaker label plans to use generative AI to render concept images for upcoming footwear lines. They can choose between a compact and carefully curated set of about 15,000 images of their own past collections and a very large and mixed set of roughly 2.2 million generic footwear photos from public sources. If they prioritize the smaller curated set of their proprietary designs as the primary training data, what outcome should they expect from the resulting model?

Is cheaper to train yet may have difficulty learning the brand specific aesthetic

Generates a much broader spectrum of entirely novel styles that do not resemble the label identity

More closely mirrors the brand signature style and design language even if overall variety may be reduced

Is far more likely to memorize and output near replicas of training images

QUESTION 45
A research team has collected a large dataset of sensor readings from various industrial machines. This dataset includes measurements like temperature, pressure, vibration levels, and electrical current, recorded at regular intervals. The team has not yet assigned any labels or categories to these readings and wants to identify potential anomalies, malfunctions, or natural groupings of machine behavior based on the sensor data alone.
What type of machine learning should they use?

Reinforcement learning

Unsupervised learning

Deep learning

Supervised learning

QUESTION 46
An engineering team at example.com spends about 12 hours each week producing boilerplate and scaffolding for routine service endpoints, and they want an AI capability that can generate this repetitive code from concise requirements or from established patterns so the developers can concentrate on complex tasks. What primary use of generative AI does this scenario represent?

Personalized user experience

Automated code synthesis

Data analysis

Image generation

QUESTION 47
A financial institution uses generative AI (gen AI) to approve and reject loan applications, but gives no reasons for rejection. Customers are starting to file complaints. The company needs to implement a solution to reduce the complaints. What should the company do?

Collect a larger and more diverse dataset for the gen AI model.

Implement explainable gen AI policies.

Fine-tune the gen AI model.

Develop fairness assessments for the gen AI model.

QUESTION 48
A social media platform uses a generative AI model to automatically generate summaries of user-submitted posts to provide quick overviews for other users. While the summaries are generally accurate for factual posts, the model occasionally misinterprets sarcasm, satire, or nuanced opinions, leading to summaries that misrepresent the original intent and potentially cause misunderstandings or offense among users. What should the platform do to overcome this limitation of the AI-generated summaries?

Implement stricter safety settings to filter out potentially misinterpreted content altogether.

Increase the temperature parameter of the model to encourage more varied and less literal interpretations.

Decrease the output length of the summaries to make them more concise.

Incorporate a human-in-the-loop (HITL) review process to refine the summaries.

QUESTION 49
A company is developing a generative AI-powered customer support chatbot. They want to ensure the chatbot can answer a wide range of customer questions accurately, even those related to recently updated product information not present in the model’s original training dat

What is a key benefit of implementing retrieval-augmented generation (RAG) in this chatbot?

RAG will significantly reduce the computational resources required to run the generative AI model.

RAG will primarily help the chatbot generate more creative and engaging conversational responses.

RAG will enable the chatbot to fine-tune its underlying language model on the fly based on customer interactions.

RAG will enable the chatbot to access and utilize external, up-to-date knowledge sources to provide more accurate and relevant answers.

QUESTION 50
A development team is configuring a generative AI model for a customer-facing application and wants to ensure the generated content is appropriate and harmless. What is the primary function of the safety settings parameter in a generative AI model?

To control the creativity and randomness of the model’s output by adjusting the diversity of word choices.

To filter out potentially harmful or inappropriate content from the model’s output based on the desired level of filtering.

To limit the maximum text length that the model generates by ensuring concise responses.

To determine the number of tokens the model can process at once by influencing the complexity and length of inputs and outputs.

QUESTION 51
A company ‘ s customer service chatbot is built on limited rules and struggles with complex customer queries, which causes customer frustration. They want to try different generative AI models and modify them to improve performance, but their current AI setup makes this difficult and costly. Which Google Cloud benefit would help solve this problem?

Pre-trained and tunable models

Integrated MLOps tools

Comprehensive security features

AI-optimized infrastructure

QUESTION 52
A research team has collected a large dataset of sensor readings from various industrial machines. This dataset includes measurements like temperature, pressure, vibration levels, and electrical current, recorded at regular intervals. The team has not yet assigned any labels or categories to these readings and wants to identify potential anomalies, malfunctions, or natural groupings of machine behavior based on the sensor data alone. What type of machine learning should they use?

Reinforcement learning

Unsupervised learning

Deep learning

Supervised learning