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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Google Cloud's Generative AI Offerings | 35% | - Model Garden and available models - Vertex AI generative AI capabilities - Enterprise integration and security features - Overview of Google Cloud generative AI services and tools - Generative AI application development platforms |
| Topic 2: Fundamentals of Generative AI | 30% | - Core concepts and characteristics of generative AI - Foundation models: definition, capabilities, and use cases - Key technologies and differences from traditional AI - Responsible AI principles and application |
| Topic 3: Techniques to Improve Generative AI Model Output | 20% | - Prompt engineering principles and best practices - Mitigation of bias and inaccuracies - Evaluation and optimization of output quality - Fine-tuning and adaptation methods |
| Topic 4: Business Strategies for Successful Generative AI Solutions | 15% | - Planning and adoption frameworks - Scaling and measuring success of generative AI initiatives - Governance, risk management, and compliance - Identifying business use cases and value opportunities |
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NEW QUESTION # 17
What is the definition of prompt engineering?
Answer: D
Explanation:
Prompt engineering is the systematic practice of designing and refining instructions, context, examples, constraints, and output requirements so that a generative AI model produces a desired response. It can include assigning a role, specifying the task, supplying relevant background, defining a response format, and using zero-shot, one-shot, or few-shot examples. Option A more broadly describes natural language processing rather than prompt engineering. Option C describes grounding, which connects generated output to trusted and verifiable information. Option D describes a zero-shot prompt, only one possible prompting technique, and therefore is too narrow to serve as the general definition. Effective prompt engineering normally involves iterative testing and refinement to improve the relevance, consistency, accuracy, and usefulness of model responses. Thus, option B provides the complete definition.
NEW QUESTION # 18
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 However, the training dataset disproportionately includes feedback from a specific demographic and uses outdated language norms that don't reflect current customer communication styles.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: C
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 # 19
A global news agency is developing a generative AI tool to quickly summarize breaking newsarticles as they emerge online. The goal is to provide their audience with rapid updates on fast-developing stories from various global sources. What Google Cloud solution should they use?
Answer: D
Explanation:
For summarizing breaking news articles as they emerge online from various global sources, the generative AI model needs access to current, broad, and rapidly updating information. Grounding with Google Search allows the LLM to pull in the latest information from the web, ensuring the summaries are current and comprehensive. While Vertex AI Natural Language API can summarize text, it wouldn't inherently have access to the latest breaking news unless explicitly fed.
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NEW QUESTION # 20
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?
Answer: A
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.
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NEW QUESTION # 21
What does Model Garden enable a company to do?
Answer: B
Explanation:
Model Garden is a key component of the Vertex AI Platform on Google Cloud, positioned as an AI/ML model library. Its core function is to provide a central, organized place for users to find and utilize a wide variety of machine learning assets.
Specifically, Model Garden enables customers to:
Discover a curated collection of models, including Google's latest Foundation Models (like Gemini and Imagen), specialized models, and enterprise-ready models from Google partners and the open-source community (e.g., Gemma).
Test and customize these models, often with tools like Vertex AI Studio for prompt tuning or fine- tuning with custom data.
Deploy the selected and customized models directly to applications with a consistent deployment pattern.
NEW QUESTION # 22
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