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| Section | Weight | Objectives |
|---|---|---|
| Techniques to Improve Generative AI Model Output | 20% | - Mitigation of bias and inaccuracies - Fine-tuning and adaptation methods - Evaluation and optimization of output quality - Prompt engineering principles and best practices |
| Business Strategies for Successful Generative AI Solutions | 15% | - Identifying business use cases and value opportunities - Scaling and measuring success of generative AI initiatives - Planning and adoption frameworks - Governance, risk management, and compliance |
| Google Cloud's Generative AI Offerings | 35% | - Enterprise integration and security features - Vertex AI generative AI capabilities - Overview of Google Cloud generative AI services and tools - Model Garden and available models - Generative AI application development platforms |
| Fundamentals of Generative AI | 30% | - Foundation models: definition, capabilities, and use cases - Responsible AI principles and application - Core concepts and characteristics of generative AI - Key technologies and differences from traditional AI |
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NEW QUESTION # 92
A marketing team wants to use a generative AI model to create product descriptions for their new line of eco-friendly water bottles. They provide a brief prompt stating, "Write a product description for our new water bottle." The model generates a generic, lackluster description that is factually accurate but lacks engaging language and doesn't highlight the environmental benefits that are key to their brand. What should the marketing team do to overcome this limitation of the generated product description?
Answer: B
Explanation:
The core problem described is a lackluster and generic output that fails to capture the desired tone and key information (environmental benefits). This is a classic limitation of zero-shot prompting (a brief, un-detailed prompt), where the generative AI model relies solely on its general training data and lacks the necessary context to produce a highly relevant and engaging response. The solution is to improve the quality of the prompt itself, a process known as Prompt Engineering.
NEW QUESTION # 93
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?
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.
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NEW QUESTION # 94
A large e-commerce company has a vast catalog of product images and needs to classify these images to improve product categorization and search functionality on their website. Most of the images in their dataset are labeled. They want to build and train an image recognition model for their product catalog. Which Google Cloud offering should they use?
Answer: D
Explanation:
AutoML is appropriate because the company possesses a largely labeled image dataset and wants to train a customized image-classification model. AutoML simplifies model development by automating important activities such as architecture selection, training, tuning, and evaluation, reducing the amount of specialist machine learning code required. The labeled product images provide the input examples and target categories needed for supervised image classification. Gemini Code Assist supports software development and does not train image-recognition models. Google AI Studio is primarily used to prototype applications and prompts with Gemini models rather than create a dedicated supervised classifier from a labeled image collection.
Agent Search focuses on retrieving enterprise information and is unrelated to image-model training.
Therefore, AutoML on Gemini Enterprise Agent Platform is the offering that best satisfies the custom image- recognition requirement.
NEW QUESTION # 95
A retail company with a large online catalog wants to improve customer experience and drive sales by implementing multimodal search capabilities (image, voice, and text). What is a primary business benefit of this capability?
Answer: B
Explanation:
Multimodal search directly enhances the customer experience by allowing them to find products using various intuitive methods (images, voice, text). This leads to easier product discovery, higher engagement, and ultimately increased customer satisfaction and potential sales, which is a primary business benefit.
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NEW QUESTION # 96
An organization wants granular control over who can use and see their generative AI models and related resources on Google Cloud. Which Google Cloud security offering is specifically for this purpose?
Answer: B
Explanation:
Identity and Access Management (IAM) is the fundamental Google Cloud service that allows you to define who has what access to which resources. It provides granular control over permissions for users, groups, and service accounts, including access to generative AI models and related data.
NEW QUESTION # 97
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