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| Section | Weight | Objectives |
|---|---|---|
| Business Strategies for Successful Generative AI Solutions | 15% | - Governance, risk management, and compliance - Scaling and measuring success of generative AI initiatives - Planning and adoption frameworks - Identifying business use cases and value opportunities |
| Fundamentals of Generative AI | 30% | - Key technologies and differences from traditional AI - Foundation models: definition, capabilities, and use cases - Core concepts and characteristics of generative AI - Responsible AI principles and application |
| Techniques to Improve Generative AI Model Output | 20% | - Fine-tuning and adaptation methods - Evaluation and optimization of output quality - Mitigation of bias and inaccuracies - Prompt engineering principles and best practices |
| Google Cloud's Generative AI Offerings | 35% | - Enterprise integration and security features - Generative AI application development platforms - Vertex AI generative AI capabilities - Model Garden and available models - Overview of Google Cloud generative AI services and tools |
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NEW QUESTION # 43
In which situation would it be most beneficial to ground a language model in first-party information?
Answer: B
Explanation:
First-party information is data owned or directly collected by an organization, such as customer transactions, account records, support history, and purchase details. A chatbot cannot reliably answer a question about a customer's recent purchase history from a foundation model's general training data. It must be grounded in the company's current, authorized customer records to provide an accurate and personalized response.
Appropriate identity verification and access controls must also be applied before retrieving the information.
Public sentiment is generally evaluated using external public data, while definitions of common scientific terms can normally be answered from general model knowledge. Summarizing global news requires grounding in external news sources rather than proprietary first-party records. Therefore, retrieving specific purchase-history information is the clearest situation where first-party grounding provides essential factual context.
NEW QUESTION # 44
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: A
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 # 45
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?
Answer: A
Explanation:
According to Google's principles for successful AI adoption, organizations should adopt a
"problem-first" approach to ensure their investments deliver measurable value. The strategic choice of a use case should always be motivated by a clear business imperative. Determining the specific business problems and desired outcomes (B) is the foundational step in any successful Gen AI strategy. Without a well-defined problem (e.g., "reduce customer response time by 30%") and a measurable desired outcome (e.g., "increase customer satisfaction scores"), any AI solution runs the risk of being a technology in search of a purpose, leading to limited adoption or failure to deliver meaningful ROI.
NEW QUESTION # 46
A company trains a generative AI model designed to classify customer feedback as positive, negative, or neutral. However, the training dataset disproportionately includes feedback from a specific demographic and uses outdated language norms that don't reflect current customer communication styles. When the model is deployed, it shows a strong bias in its sentiment analysis for new customer feedback, misclassifying reviews from underrepresented demographics and struggling to understand current slang or phrasing. What type of model limitation is this?
Answer: B
Explanation:
The core reason for the model's failure is that the training data itself was flawed (disproportionate demographic representation and outdated language). This flaw directly leads to the observed bias and poor performance on underrepresented groups and modern communication styles.
This is a classic example of Data Dependency, a fundamental limitation of all machine learning models, including generative AI. Data dependency refers to the absolute reliance of an AI model on the quality, completeness, and fairness of the data on which it was trained. Since the model essentially only mimics the patterns it learned from its dataset, if the dataset contains societal, demographic, or linguistic biases, the model will faithfully reproduce and amplify those biases in its output, leading to unfair classification for certain groups.
Hallucination (C) is the invention of facts or data.
Overfitting (D) is poor generalization because the model memorized the training data too well, typically resulting in very poor performance across all unseen data, not just specific demographics.
Bias is the result of the data dependency, not the fundamental limitation itself.
(Reference: Google's training on Generative AI Limitations identifies Data Dependency as the fundamental limitation where the model is limited by the scope and quality of its training data, directly leading to issues of bias when the data is not diverse or representative.)
NEW QUESTION # 47
A finance team wants to use Gemma to help with daily tasks so that the financial analysts can focus on other work. Which business problem can Gemma most efficiently address?
Answer: A
Explanation:
Gemma is a family of lightweight, open-source Large Language Models (LLMs) from Google that are based on the same research and technology as the Gemini models. As an LLM, its core strength lies in language-based tasks, particularly the generation and summarization of text. The problem that Gemma, or any pure LLM, can most efficiently address is:
Generating text: creating new content quickly (Option D). Summarizing text: condensing long communications or documents (Option D). Option D, producing high-quality written summaries and initial drafts, is a natural language generation task that aligns perfectly with the core function of an LLM like Gemma. It is a key productivity booster for analysts needing to draft reports or emails quickly.
NEW QUESTION # 48
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