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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Google Cloud's Generative AI Offerings | 35% | - Vertex AI generative AI capabilities - Generative AI application development platforms - Overview of Google Cloud generative AI services and tools - Enterprise integration and security features - Model Garden and available models |
| Topic 2: Techniques to Improve Generative AI Model Output | 20% | - Mitigation of bias and inaccuracies - Prompt engineering principles and best practices - Fine-tuning and adaptation methods - Evaluation and optimization of output quality |
| Topic 3: Business Strategies for Successful Generative AI Solutions | 15% | - Scaling and measuring success of generative AI initiatives - Planning and adoption frameworks - Identifying business use cases and value opportunities - Governance, risk management, and compliance |
| Topic 4: Fundamentals of Generative AI | 30% | - Key technologies and differences from traditional AI - Foundation models: definition, capabilities, and use cases - Responsible AI principles and application - Core concepts and characteristics of generative AI |
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NEW QUESTION # 33
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: C
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 # 34
A pharmaceutical company's research and development department spends significant time manually reviewing new scientific papers to identify potential drug targets. They need a solution that can answer questions about these documents and provide summarized insights to researchers without requiring extensive coding expertise. What should the organization do?
Answer: B
Explanation:
The requirement is to answer questions about the documents and provide summarized insights without requiring extensive coding expertise. Vertex AI Agent Builder is designed precisely for creating custom AI agents, often with low-code or no-code capabilities, that can interact with and process large volumes of information like scientific papers. While Vertex AI Search could index papers for keyword searches, it doesn't directly answer questions or provide summarized insights in the same way a generative AI agent built with Agent Builder could. Gemini for Google Workspace is for collaborative work, not specifically for building custom AI agents for document analysis. Vertex AI AutoML is for training classification models, which is different from answering questions and summarizing.
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NEW QUESTION # 35
A generative AI assistant at a mid-size logistics firm is asked to create a multi-city delivery itinerary. It collects initial constraints and preferences, drafts a tentative route, asks for clarifications or queries a tool for external data, updates the plan with the new information, and repeats these steps until the objective is satisfied or a limit of eight iterations is reached. This recurring cycle of observing context, reasoning internally, deciding on the next step, and acting until a goal or constraint is met is a defining characteristic of which component in an AI agent?
Answer: B
Explanation:
The scenario describes a repeated cycle of observing context, thinking, choosing the next action, performing that action such as calling a tool or asking for clarification, then incorporating the result and continuing until a goal or a limit is reached. This is exactly what the agent's control loop does. It governs how the agent plans across turns, manages tool use, updates working state, and stops when a success condition or an iteration cap such as eight steps is met.
NEW QUESTION # 36
What is the definition of generative AI?
Answer: C
Explanation:
The defining characteristic of generative AI is its ability to create new, original content that resembles its training data. This includes various modalities like text, images, music, and code, rather than just classifying, predicting, or analyzing existing data.
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NEW QUESTION # 37
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?
Answer: B
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.
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NEW QUESTION # 38
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