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Google Generative-AI-Leader Verified Answers | Generative-AI-Leader Reliable Test Objectives

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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

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.

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

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.

Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q34-Q39):

NEW QUESTION # 34
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?

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

Answer: A

Explanation:
Since the team has not yet assigned any labels or categories to the sensor readings and wants to identify "anomalies, malfunctions, or natural groupings" based on the data alone, this is a classic unsupervised learning problem. Unsupervised learning techniques like clustering or anomaly detection are used to find hidden patterns or structures in unlabeled data.
________________________________________

NEW QUESTION # 35
A company's large learning model (LLM) is producing hallucinations that are a result of the Knowledge cutoff. How does retrieval-augmented generation (RAG) overcome this limitation?

A. RAG enhances the creative writing capabilities of the LLM to generate more engaging and informative responses.
B. RAG enables the LLM to retrieve relevant and up-to-date information from knowledge sources.
C. RAG uses human oversight to ensure accuracy before presenting information to the customer.
D. RAG fine-tunes the LLM on specific customer query patterns to improve the speed and efficiency of response generation.

Answer: B

Explanation:
The primary purpose of RAG is to address the "knowledge cutoff" and hallucination issues of LLMs. It does this by retrieving relevant, up-to-date information from external knowledge sources (like databases or documents) at inference time and then using this retrieved information to ground the LLM's generation, ensuring factual accuracy and relevance to the specific query.
________________________________________

NEW QUESTION # 36
A company is developing an AI character for a video game. The AI character needs to learn how to navigate a complex environment and make decisions to achieve certain objectives within the game. When the AI takes actions that lead to positive outcomes, like finding a reward or overcoming an obstacle, it receives a positive score. When it takes actions that lead to negative outcomes, like hitting a wall or losing progress, it receives a negative score. Through this process of trial and error, the AI gradually improves the character's ability to play the game effectively. What machine learning should the company use?

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

Answer: B

Explanation:
This scenario perfectly describes reinforcement learning. In reinforcement learning, an agent learns to make decisions by interacting with an environment, receiving1 rewards for desirable actions and penalties for undesirable ones,2 and iteratively improving its behavior through trial and error to maximize cumulative reward.
________________________________________

NEW QUESTION # 37
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 expertise and need a versatile solution. Which Google foundation model should they use?

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

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 # 38
A human resources team is implementing a new generative AI application to assist the department in screening a large volume of job applications. They want to ensure fairness and build trust with potential candidates. What should the team prioritize?

A. Focusing on minimizing the processing time for each application to improve efficiency.
B. Ensuring that the AI application can automatically rank all candidates without requiring human review.
C. Integrating the AI application with various job boards to maximize candidate reach.
D. Ensuring AI operates transparently, especially regarding application evaluation and data usage.

Answer: D

Explanation:
To ensure fairness and build trust, especially in sensitive areas like job applications, transparency in how AI evaluates applications and uses data is paramount. This involves understanding potential biases, explaining decisions (where possible), and ensuring human oversight.
________________________________________

NEW QUESTION # 39
......

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