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Google Generative-AI-Leader Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Fundamentals of generative AI30%- Describe core generative AI (gen AI) concepts and use cases.
  • 1. Identifying how to choose the appropriate foundation model for a business use case (e.g., modality, context window, security, availability and reliability, cost)
  • 2. 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
  • 3. 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)
  • 4. Describing the machine learning approaches (e.g., supervised, unsupervised, reinforcement)
- Describe how various data types are used in gen AI and the business implications.
  • 1. Identifying the differences between structured and unstructured data, and identifying real world examples of each type
  • 2. Identifying the differences between labeled and unlabeled data
  • 3. Explaining the characteristics and importance of data quality and data accessibility in AI (e.g., completeness, consistency, relevance, availability, cost, format)
- Identify the core layers of the gen AI landscape and the business implications.
  • 1. Infrastructure
  • 2. Platforms
  • 3. Applications
  • 4. Agents
  • 5. Models
Topic 2: Business strategies for a successful gen AI solution15%- Describe best practices for a successful gen AI project.
  • 1. Building a business case
  • 2. Evaluating AI solutions
  • 3. Choosing the right model
- Describe change management best practices and their importance.
  • 1. Enabling AI adoption
  • 2. Creating a culture of innovation
- Describe Google's approach to responsible AI and its importance.
  • 1. Responsible AI best practices
  • 2. Google's AI principles
Topic 3: Techniques to improve gen AI model output20%- Describe prompt engineering techniques and their purpose.
  • 1. Zero-shot
  • 2. Few-shot
  • 3. Chain of thought
  • 4. One-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. Supervised tuning
  • 2. Reinforcement learning from human feedback (RLHF)
Topic 4: Google Cloud's generative AI offerings35%- Identify the use cases and strengths of Google's foundation models.
  • 1. Veo
  • 2. Gemma
  • 3. Gemini
  • 4. Imagen
- Describe Google Cloud's gen AI product and service portfolio.
  • 1. Google Workspace
  • 2. Vertex AI
  • 3. Gemini for Google Cloud
  • 4. Vertex AI Studio
  • 5. Model Garden

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q16-Q21):

NEW QUESTION # 16
A company is evaluating different generative AI (gen AI) platforms and wants to understand the role of the infrastructure layer in supporting the development and deployment of gen AI models. What is the function of the infrastructure layer in the gen AI landscape?

Answer: A

Explanation:
The infrastructure layer supplies the foundational computing, storage, networking, and acceleration resources required to train and run generative AI models. This includes CPUs, GPUs, TPUs, high-performance networks, scalable storage, and systems optimized for demanding AI workloads. Training foundation models and serving model responses require substantial processing capacity, while training datasets and model artifacts require reliable storage. Access to pre-trained models belongs primarily to the model layer. A user-friendly model interface is part of the application or experience layer. Development, deployment, tuning, and management tools belong to the platform layer. These layers work together, but their functions are distinct. Because the question asks specifically about the infrastructure layer, the correct function is supplying the computational resources and data storage needed to train and operate AI models.


NEW QUESTION # 17
A global news agency is developing a generative AI tool to quickly summarize breaking newsarticles as they emerge online. The goal is to provide their audience with rapid updates on fast-developing stories from various global sources. What Google Cloud solution should they use?

Answer: D

Explanation:
For summarizing breaking news articles as they emerge online from various global sources, the generative AI model needs access to current, broad, and rapidly updating information. Grounding with Google Search allows the LLM to pull in the latest information from the web, ensuring the summaries are current and comprehensive. While Vertex AI Natural Language API can summarize text, it wouldn't inherently have access to the latest breaking news unless explicitly fed.
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NEW QUESTION # 18
A creative agency named Northshore Images plans to fine tune an image generation model using Vertex AI, and it needs a single repository to hold about 32 TB of source pictures. The data science group requires extremely durable and elastically scalable storage for unstructured objects that will feed their Vertex AI training runs. Which Google Cloud service best fits storing large collections of object data such as image files?

Answer: D

Explanation:
The correct option is Cloud Storage because it is a highly durable and elastically scalable object store for unstructured data such as image files and it integrates seamlessly with Vertex AI training. It can comfortably hold a single repository of about 32 TB.
This service offers bucket level durability and availability with regional or multi regional placement for resilience. It provides simple object access using gs paths that Vertex AI training jobs can read directly. It also supports lifecycle management, versioning and granular access control which are all valuable when managing large image datasets for machine learning.


NEW QUESTION # 19
SkyTrail Travel runs a high volume support center that captures about 9,500 customer calls each day. Leadership wants an automated approach to mine the transcripts so they can detect new complaint themes, understand common causes of dissatisfaction, and verify that agents follow the approved script without manually reviewing every recording. Within Google Cloud's Contact Center AI portfolio, which component is purpose built to deliver these analytics across call data?

Answer: A

Explanation:
The correct option is Conversational Insights because it is purpose built within Contact Center AI to analyze large volumes of conversations and deliver themes, sentiment, root causes, and compliance insights without manual review.
This Insights capability ingests recordings and transcripts at scale and applies machine learning to cluster topics, surface emerging complaint patterns, and track customer sentiment. It also evaluates agent behavior so leaders can verify script adherence and other quality signals. It provides dashboards and searchable analytics so operations teams can quickly understand dissatisfaction drivers and improve processes.


NEW QUESTION # 20
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?

Answer: D

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.
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NEW QUESTION # 21
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