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| Section | Weight | Objectives |
|---|---|---|
| Google Cloud's Generative AI Offerings | 35% | - Model Garden and available models - Generative AI application development platforms - Overview of Google Cloud generative AI services and tools - Vertex AI generative AI capabilities - Enterprise integration and security features |
| Techniques to Improve Generative AI Model Output | 20% | - Prompt engineering principles and best practices - Mitigation of bias and inaccuracies - Fine-tuning and adaptation methods - Evaluation and optimization of output quality |
| Business Strategies for Successful Generative AI Solutions | 15% | - Governance, risk management, and compliance - Identifying business use cases and value opportunities - Planning and adoption frameworks - Scaling and measuring success of generative AI initiatives |
| Fundamentals of Generative AI | 30% | - Foundation models: definition, capabilities, and use cases - Core concepts and characteristics of generative AI - Key technologies and differences from traditional AI - Responsible AI principles and application |
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NEW QUESTION # 41
A company wants to use generative AI to create a chatbot that can answer customer questions about their products and services. They need to ensure that the chatbot only uses information from the company's official documentation. What should the company do?
Answer: A
Explanation:
The core requirement is to guarantee that the chatbot only uses information from the company's official documentation and does not rely on its general knowledge base. This is crucial for ensuring factual accuracy, relevance to the company's specific products, and preventing the generation of fabricated or incorrect information (hallucinations).
The specific technique designed to address this challenge is Grounding. Grounding is the process of connecting the Large Language Model's (LLM's) responses to a trusted, verifiable source of information, such as an organization's internal documents, databases, or live data feeds. When an LLM is grounded, it is forced to base its answers only on the provided context, effectively preventing it from drawing on its broad, generalized training data. Grounding is often implemented using a method called Retrieval-Augmented Generation (RAG), particularly with tools like Google Cloud's Vertex AI Search, which indexes the official documentation and feeds the relevant snippets to the model.
Options A, B, and C address different aspects of model output: Role prompting sets the model's persona, adjusting temperature controls creativity, and prompt chaining manages conversation history, but none of these techniques restrict the model's source of truth to the official documentation. Therefore, Grounding is the correct and most effective technique for this requirement.
NEW QUESTION # 42
An organization wants to use generative AI to create a chatbot that can answer customer questions about their account balances. They need to ensure that the chatbot can access previous portions of the conversation with the customer. Which prompting technique should they use?
Answer: D
Explanation:
Prompt chaining (or conversational memory/context management) is the technique used to maintain the conversational context. It involves feeding previous turns of a conversation (or a summary of them) back into the model along with the current user query, allowing the chatbot to
"remember" and reference past interactions for coherent and contextually relevant responses, especially crucial for tasks like checking account balances that span multiple turns.
NEW QUESTION # 43
A user asks a generative AI model about the scientific accuracy of a popular science fiction movie. The model confidently states that humans can indeed travel faster than light, referencing specific but entirely fictional theories and providing made-up explanations of how this is achieved according to the movie's "established science." The model presents this information as factual, without indicating that it originates from a fictional work. What type of model limitation is this?
Answer: A
NEW QUESTION # 44
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: B
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 # 45
A nationwide retail chain plans to retire its aging on premises contact center stack and move to a cloud first model that uses AI throughout customer interactions. The company requires a single enterprise ready foundation that unifies telephony, IVR, conversational virtual agents, and real time agent assist features while scaling globally as call volumes grow. Which Google Cloud solution best fits this fully managed end to end contact center platform need?
Answer: C
Explanation:
This fully managed solution provides a single enterprise ready foundation that unifies telephony, IVR, conversational virtual agents built with Dialogflow CX, and real time Agent Assist. It is designed to scale globally as call volumes grow and to deliver reliability, security, and compliance while reducing the need to stitch together multiple products.
NEW QUESTION # 46
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