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| Section | Weight | Objectives |
|---|---|---|
| Data Research and Analysis | 15% | - Data quality, bias and validation - Data visualization and insight generation - Data collection, preparation and preprocessing |
| Generative AI Models | 20% | - Model hosting options and trade-offs - Model selection and evaluation in AI hubs - Context windows, token limits and response management - Generative AI model families and use cases |
| AI Ethics and Security | 20% | - AI-specific threats, risks and misinformation - Data privacy and security in AI systems - Responsible AI principles - Compliance and governance frameworks |
| Prompt Engineering | 15% | - Defensive prompting and error mitigation - Advanced prompting techniques - Prompt design principles and best practices |
| AI for Code and Workflow Optimization | 15% | - Integration of AI tools in development processes - Automation and workflow enhancement - AI-assisted code generation and improvement |
| Agentic AI | 15% | - Model Context Protocol (MCP) framework - Human-in-the-loop strategies - Agent design principles and capabilities - Agentic AI vs Generative AI use cases |
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NEW QUESTION # 33
What is the role of the client in the Model Context Protocol?
Answer: D
NEW QUESTION # 34
Which generative AI model family should be used for identifying and summarizing technical themes across thousands of unstructured network incident reports?
Answer: B
Explanation:
Large language models are designed to process and generate natural language, making them suitable for identifying recurring technical themes and summarizing large volumes of unstructured incident reports.
NEW QUESTION # 35
Which activity is an agentic AI use case compared to a standard generative AI use case?
Answer: A
Explanation:
Agentic AI can plan and execute multi-step actions toward a goal, such as diagnosing a failed build, applying a fix, running validation, and iterating until the build succeeds.
NEW QUESTION # 36
Why are embeddings essential to the retrieval phase of a RAG system?
Answer: A
Explanation:
Embeddings convert text into numerical vectors that capture semantic meaning, allowing a RAG system to retrieve content that is conceptually relevant to the user's query rather than relying only on exact keyword matches.
NEW QUESTION # 37
What is an advantage of cloud-hosted API services when comparing cloud-hosted and locally hosted AI models?
Answer: A
Explanation:
Cloud-hosted API services can scale inference capacity quickly based on demand, making them suitable for workloads with unpredictable or fluctuating usage.
NEW QUESTION # 38
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