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| Section | Weight | Objectives |
|---|---|---|
| AI for Code and Workflow Optimization | - Workflow automation
| |
| Generative AI Models | 20% | - Context windows and token management
|
| Data Research and Analysis | - Data analysis techniques
| |
| AI Ethics and Security | - Ethical considerations in AI
| |
| Prompt Engineering | 15% | - Principles and patterns
|
| Agentic AI | 20% | - Model Context Protocol (MCP)
|
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NEW QUESTION # 99
Which software development lifecycle phase utilizes AI to analyze stakeholder documentation for logical inconsistencies and to draft structured user stories?
Answer: D
Explanation:
In the requirements phase, AI can analyze stakeholder documentation to identify logical gaps or inconsistencies and help convert business needs into structured user stories.
NEW QUESTION # 100
When preparing a comprehensive proposal from multiple AI-synthesized research documents, which workflow is most effective at ensuring the AI preserves context and consistency throughout the entire document?
Answer: C
Explanation:
A Master Briefing Document provides a centralized, consistent reference containing the essential information required to produce a coherent document across multiple AI-assisted drafting stages. Option B is correct because repeatedly supplying the same authoritative context helps preserve terminology, requirements, assumptions, and structural consistency.
A comprehensive technical proposal may incorporate architecture specifications, implementation constraints, stakeholder priorities, financial assumptions, and research findings.
Without a shared reference, independently generated sections may introduce conflicting statements, inconsistent terminology, or unsupported assumptions.
A Master Briefing Document can establish the proposal's objectives, intended audience, technical scope, approved findings, formatting requirements, and document structure.
Each drafting prompt can reference this shared information while focusing on a particular section.
Option A is incorrect because isolated chat sessions may increase inconsistency when they lack common contextual information.
Option C is incorrect because model memory is not guaranteed to retain every relevant research finding accurately or completely.
Option D is incorrect because generating an extensive document in a single prompt can increase context- management challenges and reduce opportunities for intermediate validation.
An effective workflow combines centralized context, sequential drafting, consistency reviews, and verification against original research sources.
Specific Cisco AITECH Course Guide References:
* Prompt Engineering for Technical Precision
* Advanced Prompting Strategies
* AI-Powered Discovery and Synthesis
* Exam Objective 2.2: Iterative, sequential, and chained prompting
* Exam Objective 4.4: AI-assisted research and content drafting
NEW QUESTION # 101
What is the primary goal of Retrieval-Augmented Generation (RAG) in AI systems?
Answer: A
Explanation:
RAG combines a large language model with external data retrieval systems so the model can answer using updated and domain-specific knowledge.
NEW QUESTION # 102
Drag the responsible AI principle on the left to its corresponding action on the right. Not every option will be used.
Answer:
Explanation:
Explanation:
Correct Drag-and-Drop Sequence:
* Fairness
* Accountability
* Transparency
Responsible AI principles establish ethical and operational standards for developing, deploying, and governing artificial intelligence systems. The three scenarios illustrate Fairness, Accountability, and Transparency, which Cisco explicitly identifies under its Ethics and Security examination domain.
1. Fairness: This principle requires AI systems to minimize unjustified discrimination and systematically biased outcomes. Actively identifying and mitigating biases in training datasets and model outputs helps ensure that AI-generated recommendations do not unfairly disadvantage particular individuals or groups.
Fairness audits, representative datasets, and subgroup performance evaluations are important mitigation techniques.
2. Accountability: Accountability establishes responsibility for AI-generated decisions and their consequences. Implementing Human-in-the-Loop (HITL) reviews ensures that qualified personnel examine critical recommendations before deployment or execution. Human oversight supports governance, provides opportunities to identify errors, and establishes clear decision-making responsibility.
3. Transparency: Transparency requires organizations to communicate relevant information about AI involvement in processes and outputs. Disclosing that documents, software code, or other content were produced with AI assistance enables stakeholders to understand the technology's role and evaluate its limitations.
Reliability is the unused option. Reliability concerns consistent, dependable system performance under expected operating conditions rather than the three specific actions described.
Specific Cisco AITECH Course Guide References:
* Generative AI Security and Privacy Fundamentals
* Responsible AI Principles and Bias Mitigation
* AI Governance, Policy, and Risk Management
* Exam Objective 3.1: Fairness, Transparency, Accountability, Bias Mitigation, and Safety
* Exam Objective 3.4: AI Governance Considerations
Official Cisco Reference: Cisco AI Technical Practitioner (810-110) Exam Topics
NEW QUESTION # 103
Which AI-assisted task standardizes inconsistent date formats (e.g., MM/DD/YY and DD-MM- YYYY)?
Answer: C
Explanation:
Data transformation converts inconsistent data into a consistent format, such as standardizing multiple date formats into one required representation.
NEW QUESTION # 104
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