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Google Generative-AI-Leader Exam Syllabus Topics:

Topic
Details

Topic 1

• Google Cloud’s Generative AI Offerings: This section of the exam measures the skills of Cloud Architects and highlights Google Cloud’s strengths in generative AI. It emphasizes Google’s AI-first approach, enterprise-ready platform, and open ecosystem. Candidates will learn about Google’s AI infrastructure, including TPUs, GPUs, and data centers, and how the platform provides secure, scalable, and privacy-conscious solutions. The section also explores prebuilt AI tools such as Gemini, Workspace integrations, and Agentspace, while demonstrating how these offerings enhance customer experience and empower developers to build with Vertex AI, RAG capabilities, and agent tooling.

Topic 2

• Fundamentals of Generative AI: This section of the exam measures the skills of AI Engineers and focuses on the foundational concepts of generative AI. It covers the basics of artificial intelligence, natural language processing, machine learning approaches, and the role of foundation models. Candidates are expected to understand the machine learning lifecycle, data quality, and the use of structured and unstructured data. The section also evaluates knowledge of business use cases such as text, image, code, and video generation, along with the ability to identify when and how to select the right model for specific organizational needs.

Topic 3

• Techniques to Improve Generative AI Model Output: This section of the exam measures the skills of AI Engineers and focuses on improving model reliability and performance. It introduces best practices to address common foundation model limitations such as bias, hallucinations, and data dependency, using methods like retrieval-augmented generation, prompt engineering, and human-in-the-loop systems. Candidates are also tested on different prompting techniques, grounding approaches, and the ability to configure model settings such as temperature and token count to optimize results.

Topic 4

• Business Strategies for a Successful Generative AI Solution: This section of the exam measures the skills of Cloud Architects and evaluates the ability to design, implement, and manage enterprise-level generative AI solutions. It covers the decision-making process for selecting the right solution, integrating AI into an organization, and measuring business impact. A strong emphasis is placed on secure AI practices, highlighting Google’s Secure AI Framework and cloud security tools, as well as the importance of responsible AI, including fairness, transparency, privacy, and accountability.

 

Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q71-Q76):

NEW QUESTION # 71
A security team needs a centralized platform to gain a comprehensive overview of their organization's security health across their entire Google Cloud environment, including potential threats to their generative AI deployments. Which Google Cloud security offering is specifically for this purpose?

• A. Identity and Access Management
• B. Secure-by-design infrastructure
• C. Workload monitoring tools
• D. Security Command Center

Answer: D

Explanation:
Security Command Center is Google Cloud's comprehensive security management and data risk platform. It provides centralized visibility into security posture, identifies vulnerabilities, detects threats, and helps manage compliance across the entire Google Cloud environment, includingservices and deployments like generative AI.
________________________________________

 

NEW QUESTION # 72
A marketing team wants to use a foundation model to create social media and advertising campaigns. They want to create written articles and images from text. They lack deep AI expertiseand need a versatile solution.
Which Google foundation model should they use?

• A. Veo
• B. Gemini
• C. Gemma
• D. Imagen

Answer: B

Explanation:
Gemini is Google's most advanced and multimodal foundation model, capable of understanding and generating various forms of content, including text and images, from a single prompt. Its versatility makes it suitable for marketing teams that need to create diverse campaign materials without deep AI expertise.
Imagen is specifically for image generation, Gemma is a family of smaller, open models, and Veo is for video generation.
________________________________________

 

NEW QUESTION # 73
What does a diffusion model do?

• A. Analyzes data and predicts future trends and patterns.
• B. Generates high-quality content by refining noise into structured data.
• C. Optimizes business processes and resource allocation.
• D. Facilitates the storage and management of structured data.

Answer: B

Explanation:
A Diffusion Model (or Denoising Diffusion Probabilistic Model) is a specific class of generative AI model that is best known for its ability to create highly realistic images (e.g., Google's Imagen and Stable Diffusion are based on this architecture).
The core mechanism of a diffusion model is a two-step process:
Forward Diffusion (Adding Noise): It learns how to gradually corrupt data (like an image) by adding random noise until the original content is completely indistinguishable.
Reverse Diffusion (Denoising): It then learns to reverse this process-to gradually remove the noise-starting from a random noise pattern and iteratively refining it, guided by a text prompt, until a clear, coherent, and high-quality piece of content (an image or video) is generated.
Option D accurately captures this mechanism: the model starts with pure noise and generates the final structured data (the image) by refining that noise.
Option A describes predictive AI (forecasting models).
Option C describes a database or storage service.
Option B describes a workflow agent or optimization AI.
(Reference: Google's training materials on Foundation Models define Diffusion Models as generative models that operate by gradually converting a state of random noise into a structured, meaningful output, most commonly for the generation of high-quality images and video.)

 

NEW QUESTION # 74
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?

• A. To filter out potentially harmful or inappropriate content from the model's output based on the desired level of filtering.
• B. To control the creativity and randomness of the model's output by adjusting the diversity of word choices.
• C. To determine the number of tokens the model can process at once by influencing the complexity and length of inputs and outputs.
• D. To limit the maximum text length that the model generates by ensuring concise responses.

Answer: A

Explanation:
Safety settings in generative AI models are specifically designed to prevent the generation of content that could be harmful, offensive, or inappropriate. This includes filtering for categories like hate speech, sexually explicit content, self-harm, and violence, based on predefined thresholds. Options A, B, and D refer to other parameters like max_output_tokens or temperature, which control output length, input/output processing, and creativity, respectively, not safety.
________________________________________

 

NEW QUESTION # 75
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?

• A. Reinforcement learning
• B. Supervised learning
• C. Deep learning
• D. Unsupervised learning

Answer: B

Explanation:
The machine learning approach is determined by the nature of the data available and the desired output.
Data Available: Customer reviews (input) that are manually tagged with a sentiment category (output/label).
Desired Output: A model that can classify new, untagged reviews into one of the predefined categories (positive, negative, or neutral).
This scenario perfectly aligns with the definition of Supervised Learning (D). Supervised learning is the machine learning paradigm where the model is trained on a labeled dataset-a dataset where the input data is explicitly paired with the correct output label. The model learns a function that maps the input (the review text) to the output (the sentiment tag) and is then used to predict the label for unseen data.
Unsupervised Learning (B) is used for unlabeled data to find hidden patterns or groupings (clustering), which is not the goal here.
Reinforcement Learning (C) is used for training an agent through trial and error using a system of rewards and penalties.
Deep Learning (A) is a type of model (using deep neural networks) that can be used for supervised learning, but the learning approach required here is definitively supervised.
(Reference: Google's training materials on Machine Learning Approaches define Supervised Learning as training a model using labeled data to make predictions or classifications for new, unseen inputs. Sentiment analysis is a canonical example of a supervised learning classification task.)

 

NEW QUESTION # 76
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