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| Section | Weight | Objectives |
|---|---|---|
| Agentic AI | 15% | - Model Context Protocol (MCP) framework - Agent design principles and capabilities - Human-in-the-loop strategies - Agentic AI vs Generative AI use cases |
| Prompt Engineering | 15% | - Prompt design principles and best practices - Defensive prompting and error mitigation - Advanced prompting techniques |
| AI for Code and Workflow Optimization | 15% | - Automation and workflow enhancement - AI-assisted code generation and improvement - Integration of AI tools in development processes |
| Generative AI Models | 20% | - Generative AI model families and use cases - Model selection and evaluation in AI hubs - Model hosting options and trade-offs - Context windows, token limits and response management |
| AI Ethics and Security | 20% | - Responsible AI principles - AI-specific threats, risks and misinformation - Compliance and governance frameworks - Data privacy and security in AI systems |
| Data Research and Analysis | 15% | - Data quality, bias and validation - Data collection, preparation and preprocessing - Data visualization and insight generation |
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NEW QUESTION # 15
A developer gives an AI model three examples, each pairing a log entry with a specific error category, before asking it to categorize a new log entry. Which prompting technique does this represent?
Answer: A
Explanation:
Few-shot prompting is a technique in which a generative AI model receives a small number of representative examples demonstrating the expected relationship between inputs and outputs. Option C is correct because the developer supplies three labeled log examples before requesting classification of a previously unseen entry.
In this scenario, each example establishes a relationship between a diagnostic log message and its corresponding error category.
For example, a prompt might associate authentication failures with an authentication category, connection timeouts with a connectivity category, and resource exhaustion messages with a capacity category.
The model uses these demonstrations as contextual guidance when classifying the new entry.
Option A is incorrect because zero-shot prompting requests task completion without providing task-specific examples.
Option B is incorrect because chain-of-thought prompting focuses on intermediate reasoning processes rather than representative input-output demonstrations.
Option D is incorrect because metaprompting involves prompts that help generate, evaluate, or improve other prompts.
Few-shot examples should be accurate, representative, and consistent with the desired classification rules.
The technique can improve task adherence without modifying the model's underlying weights, although performance depends on model capability, example quality, and the complexity of the classification problem.
Specific Cisco AITECH Course Guide References: Prompt Engineering for Technical Precision; Advanced Prompting Strategies; Exam Objective 2.2 - Few-shot, iterative, sequential, and chained prompting techniques.
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NEW QUESTION # 16
A practitioner uses self-consistency by running the same prompt three times and comparing the results. What is the goal of this strategy?
Answer: D
Explanation:
Self-consistency compares multiple responses to the same prompt to check whether the model produces stable, reliable results across repeated runs.
NEW QUESTION # 17
What is the role of embeddings in AI systems?
Answer: C
Explanation:
Embeddings convert text or objects into vectors that capture semantic meaning.
NEW QUESTION # 18
When recommending AI for use with proprietary software documentation, which argument addresses the main intellectual-property legal risk?
Answer: B
Explanation:
Organizations using AI services to analyze proprietary software documentation must consider confidentiality, intellectual property protection, and contractual data-handling obligations. Option A is the best answer because disclosing confidential technical information to an unauthorized external service can compromise trade secrets and create legal or commercial risks.
Proprietary documentation may contain source code, system designs, unpublished algorithms, customer- specific implementations, or confidential architectural information.
Submitting such material to an unapproved AI platform may expose it to third-party processing, retention, or access.
Trade secret protection can be particularly sensitive because it generally depends on maintaining reasonable measures to preserve confidentiality.
Option B is incorrect because AI training datasets are not necessarily restricted to public-domain material, and generated outputs are not universally exempt from copyright claims.
Option C is incorrect because copyright eligibility for AI-generated material depends on applicable law and the nature of human authorship.
Option D is incorrect because a commercial AI license does not automatically determine ownership or legal protection of preexisting proprietary documentation.
Importantly, disclosure does not automatically transfer IP ownership. The actual consequences depend on applicable law, contractual terms, and circumstances.
Organizations should assess vendor agreements, data retention, training-use provisions, and access controls before sharing confidential material.
Specific Cisco AITECH Course Guide References:
* Generative AI Security and Privacy Fundamentals
* AI Architect's Toolkit
* Exam Objective 3.2: Corporate data privacy and security
* Exam Objective 3.4: AI governance, policy, risk management, and compliance
NEW QUESTION # 19
A marketing team must generate high-fidelity images for a new product launch. Which model is designed to handle this task through the process of iterative noise reduction?
Answer: B
Explanation:
Diffusion models generate high-fidelity images by starting from noise and iteratively denoising it over multiple steps until a coherent image is produced.
NEW QUESTION # 20
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