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| Section | Weight | Objectives |
|---|---|---|
| Fundamentals of Generative AI | 30% | - Core concepts and characteristics of generative AI - Responsible AI principles and application - Foundation models: definition, capabilities, and use cases - Key technologies and differences from traditional AI |
| Google Cloud's Generative AI Offerings | 35% | - Generative AI application development platforms - Vertex AI generative AI capabilities - Overview of Google Cloud generative AI services and tools - Model Garden and available models - Enterprise integration and security features |
| Techniques to Improve Generative AI Model Output | 20% | - Mitigation of bias and inaccuracies - Evaluation and optimization of output quality - Fine-tuning and adaptation methods - Prompt engineering principles and best practices |
| Business Strategies for Successful Generative AI Solutions | 15% | - Planning and adoption frameworks - Governance, risk management, and compliance - Scaling and measuring success of generative AI initiatives - Identifying business use cases and value opportunities |
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NEW QUESTION # 44
What is the definition of prompt engineering?
Answer: C
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 # 45
A large enterprise company is experiencing challenges managing their model. They have many versions of their model, including the code, data, and parameters used to train the models. They need to find a solution to manage versions, track changes, and stay organized throughout their lifecycle. What Gemini Enterprise Agent Platform tool should the company use?
Answer: D
Explanation:
Model Registry provides a centralized repository for organizing and governing machine learning models and their versions throughout the model lifecycle. It allows teams to register models, retain version history, associate metadata, track lineage and deployment status, and consistently identify which model artifact is approved or operating in an environment. These capabilities directly address the company's need to manage numerous versions and remain organized. Model Monitoring observes deployed model behavior, including performance changes and data drift, but is not the primary version-management repository. Pipelines automate repeatable machine learning workflows such as training, evaluation, and deployment. Feature Store manages reusable machine learning features rather than complete model versions and their lifecycle metadata.
Consequently, Model Registry is the appropriate tool for tracking, organizing, and controlling the company's evolving model assets.
NEW QUESTION # 46
A company's development team is eager to start building generative AI solutions with Google Cloud, but has limited experience in AI development. They need to launch their gen AI solution quickly. What Google Cloud benefit would help the company achieve their goal?
Answer: A
Explanation:
For a team with limited AI experience needing to launch quickly, leveraging pre-trained models (foundation models) and low-code/no-code tools significantly reduces the development burden and accelerates time to market. This allows them to build and deploy generative AI solutions without requiring deep expertise from scratch. While other options are helpful, this directly addresses the need for quick launch with limited experience.
NEW QUESTION # 47
A large multinational corporation with geographically dispersed teams struggles with knowledge silos and inconsistent access to crucial internal information. What is a key business benefit of using Google Agentspace in this scenario?
Answer: B
Explanation:
Google Agentspace (or similar agent-based frameworks) aims to connect and orchestrate various AI capabilities and data sources. In a scenario with knowledge silos, a key benefit would be to enable seamless knowledge sharing and collaboration by allowing agents to access, process, and disseminate information across different internal systems and teams.
________________________________________
NEW QUESTION # 48
What is an example of supervised machine learning?
Answer: A
Explanation:
Supervised machine learning trains a model using examples containing both input features and known output labels. Historical support tickets can include characteristics such as issue category, priority, customer type, and complexity, together with the actual resolution time. The model learns the relationship between those labeled examples and then predicts resolution time for new tickets. The remaining scenarios focus on discovering previously unknown structures in unlabeled data. Identifying customer segments from purchase history and finding behavior clusters from website clicks are clustering tasks. Automatically discovering recurring topics in customer reviews is generally a topic-modeling task. These are examples of unsupervised learning because the system is not trained against predetermined target outcomes. Therefore, predicting resolution time from past support tickets with known resolution-time labels is the clear example of supervised machine learning.
NEW QUESTION # 49
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