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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Ethics and Security | 15% | - Responsible AI principles
|
| Topic 2: Generative AI Models | 20% | - Context and retrieval systems
|
| Topic 3: AI for Data Research and Analysis | - Data exploration and transformation
| |
| Topic 4: Agentic AI Systems | - Agent architecture
| |
| Topic 5: AI for Code and Workflow Optimization | - AI-assisted software development
| |
| Topic 6: Prompt Engineering | 15% | - Security and robustness
|
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NEW QUESTION # 19
Which software development lifecycle phase utilizes AI to analyze stakeholder documentation for logical inconsistencies and to draft structured user stories?
Answer: C
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 # 20
What is the role of the client in the Model Context Protocol?
Answer: C
NEW QUESTION # 21
Within a multi-agent AI framework, what is the role of a coordinator agent?
Answer: A
Explanation:
A coordinator agent is a supervisory component within a multi-agent AI architecture responsible for organizing work, delegating tasks, managing dependencies, and consolidating results from specialized agents.
Option A is correct because orchestration and output aggregation are defining responsibilities of a coordinator.
Multi-agent architectures distribute responsibilities among agents with different skills, tools, or operational roles.
For example, an AI-powered infrastructure assessment framework might contain a discovery agent, security assessment agent, capacity planning agent, and documentation agent.
The coordinator receives the overall objective, decomposes it into manageable subtasks, assigns responsibilities, and coordinates execution according to task dependencies.
After the specialized agents complete their assignments, the coordinator evaluates their outputs, resolves inconsistencies where possible, and consolidates the findings into a coherent response.
Option B is incorrect because replacing specialized agents with one reasoning component describes a single- agent architecture rather than multi-agent coordination.
Option C is incorrect because external knowledge sources are typically provided through databases, retrieval systems, APIs, or other tools.
Option D is incorrect because model fine-tuning is a training process involving parameter updates, not a normal coordinator-agent responsibility.
Effective coordination improves modularity, task specialization, workflow traceability, and operational control.
Specific Cisco AITECH Course Guide References:
* AI-Powered Workflows and Agentic AI
* AI Systems Integration with APIs
* Exam Objective 6.2: AI agent design principles, autonomous capabilities, and orchestration Cisco's blueprint directly identifies orchestration as an essential agentic AI competency.
NEW QUESTION # 22
Within an AI-driven automation workflow, what is the purpose served by an approval gate?
Answer: C
Explanation:
An approval gate is a workflow checkpoint that prevents a potentially consequential operation from proceeding until an authorized human reviewer evaluates and approves the proposed action. Option D is correct because human judgment is deliberately introduced before high-impact actions are executed.
AI agents can autonomously analyze information, propose actions, and interact with external systems.
However, unrestricted execution may introduce operational, security, financial, or compliance risks.
For example, an AI-powered network operations agent might identify an inefficient routing configuration and generate a proposed change. An approval gate would require a qualified engineer to review the modification before it is applied to production infrastructure.
The reviewer can evaluate the proposed configuration, potential service disruption, rollback procedure, and operational justification.
Option A is incorrect as the general definition because blocking deployment based on system health is a conditional policy gate, not necessarily a human approval gate.
Option B is incorrect because approval mechanisms do not make model internals immutable.
Option C is incorrect because completion notifications communicate workflow status rather than authorize execution.
Approval gates strengthen governance through explicit authorization, accountability, and auditable decision records. They are particularly valuable for irreversible operations or changes affecting critical services.
Specific Cisco AITECH Course Guide References: AI-Powered Workflows and Agentic AI; Exam Objective
5.3 - Workflow design and monitoring; Objective 6.4 - Human-in-the-Loop strategies.
Cisco Learning Content
NEW QUESTION # 23
A practitioner uses self-consistency by running the same prompt three times and comparing the results. What is the goal of this strategy?
Answer: B
Explanation:
Self-consistency compares multiple responses to the same prompt to check whether the model produces stable, reliable results across repeated runs.
NEW QUESTION # 24
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