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Google Generative-AI-Leader Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

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 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.

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q45-Q50):

NEW QUESTION # 45
An organization wants to use generative AI to create a marketing campaign. They need to ensure that the AI model generates text that is appropriate for the target audience. What should the organization do?

A. Adjust the temperature parameter.
B. Use few-shot prompting.
C. Use role prompting.
D. Use prompt chaining.

Answer: C

Explanation:
Role prompting is a technique where you instruct the generative AI model to "act as" a specific persona or character. By assigning the model a role (e.g., "Act as a marketing expert writing for a young, tech-savvy audience"), you can guide its tone, style, and content to be appropriate for the target audience of the marketing campaign.
________________________________________

NEW QUESTION # 46
A company wants to adopt generative AI and is concerned about vendor lock-in. They want to maintain flexibility in their technology stack. What Google Cloud strength would ease their concerns?

A. Google Cloud's focus on automation aims to replace human jobs with AI systems, potentially leading to significant workforce reductions.
B. Google Cloud's AI solutions are pre-packaged for easy deployment, eliminating the need for customization and integration efforts.
C. Google Cloud's AI solutions have an open approach that supports customer choice across offerings.
D. Google Cloud's strict adherence to proprietary technologies ensures the highest level of security and performance.

Answer: C

Explanation:
Google Cloud promotes an open and flexible approach to its AI offerings, supporting open standards, open- source initiatives (like TensorFlow, Kubernetes, and Gemma), and providing various integration options. This helps alleviate vendor lock-in concerns by giving customers choice and control over their technology stack.
________________________________________

NEW QUESTION # 47
What is the definition of generative AI?

A. A type of predictive model that estimates a relationship by fitting a line to the observed data.
B. A type of artificial intelligence that can create new content and ideas, including text, images, music, and code.
C. A type of artificial intelligence that enables a system to autonomously learn and improve using neural networks and deep learning.4
D. A type of machine learning algorithm inspired by the human brain that is made up of interconnected nodes.

Answer: B

Explanation:
The defining characteristic of generative AI is its ability to create new, original content that resembles its training data. This includes various modalities like text, images, music, and code, rather than just classifying, predicting, or analyzing existing data.
________________________________________

NEW QUESTION # 48
A company wants to choose a generative AI (gen AI) use case that will be successful and have the most impact. What key factor should they determine first according to Google Cloud-recommended practices?

A. The specific business problems the company aims to solve and the desired outcomes.
B. The availability of pre-trained models that are offered on various cloud computing platforms.
C. The number of employees who will be trained to use the new gen AI tools.
D. The frequency of updates to the underlying foundation models used by different gen AI platforms.

Answer: A

Explanation:
A fundamental principle for successful AI adoption, including generative AI, is to start with clear business problems and desired outcomes. Without a well-defined problem, the AI solution might not deliver meaningful value, regardless of the technology used. This "problem-first" approach is crucial for impactful AI strategy.
________________________________________

NEW QUESTION # 49
An organization needs an AI tool to analyze and summarize lengthy customer feedback text transcripts. You need to choose a Google foundation model with a large context window. What foundation model should the organization choose?

A. CodeGemma
B. Imagen
C. Gemini
D. Chirp

Answer: C

Explanation:
Gemini models are known for their large context windows, making them highly suitable for processing and summarizing lengthy texts like customer feedback transcripts. CodeGemma is specialized for code, Imagen for image generation, and Chirp for speech.
________________________________________

NEW QUESTION # 50
......

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