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| Section | Weight | Objectives |
|---|---|---|
| AI for Data Research and Analysis | - Data exploration and transformation
| |
| Agentic AI Systems | - Agent architecture
| |
| Ethics and Security | 15% | - AI security threats
|
| Generative AI Models | 20% | - Model fundamentals and use cases
|
| Prompt Engineering | 15% | - Prompting techniques
|
| AI for Code and Workflow Optimization | - Automation workflows
|
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NEW QUESTION # 20
What is a use case for generative AI in the testing phase of the software development lifecycle?
Answer: D
Explanation:
Generative AI can create synthetic test data that simulates realistic inputs and edge cases, helping validate application behavior during the testing phase without relying only on production data.
NEW QUESTION # 21
What is the main advantage of cloud-based AI services?
Answer: A
Explanation:
Cloud AI platforms provide scalable infrastructure and elastic computing power for AI workloads.
NEW QUESTION # 22
Which design principle helps stop an autonomous agent from getting stuck in an infinite loop and wasting tokens?
Answer: A
Explanation:
Autonomous AI agents may execute iterative cycles involving planning, tool use, observation, and further decision-making. Without operational limits, these cycles can continue unnecessarily, resulting in excessive token consumption and repeated actions. Option B is correct because implementing constraints establishes explicit boundaries on agent execution.
Important constraints include maximum iteration counts, tool-call limits, execution deadlines, token budgets, and predefined termination conditions.
For example, an AI troubleshooting agent might be permitted to perform no more than ten diagnostic iterations before producing a status report or requesting human intervention.
Additional safeguards can detect repeated tool calls with identical arguments, unchanged system states, or recurring unsuccessful actions.
Option A is incorrect because removing system prompts can eliminate important behavioral instructions and safety constraints.
Option C is incorrect because known workflow patterns may improve design consistency but do not independently enforce termination.
Option D is incorrect because reducing available context does not guarantee that an agent will terminate. It may instead cause the agent to lose information about previous actions.
Reliable agent design requires explicit stopping rules, error handling, progress monitoring, and resource consumption limits.
These controls improve operational predictability and prevent unnecessary expenditure.
Specific Cisco AITECH Course Guide References: AI-Powered Workflows and Agentic AI; Exam Objective
6.2 - Autonomous capabilities and orchestration; Objective 5.3 - AI workflow monitoring principles.
Cisco Learning Content
NEW QUESTION # 23
A prototype application has high latency during the "time-to-first-token" phase. To ensure the AI has the needed information, the developer currently includes the entire 50-page technical manual in every prompt.
What is the most effective approach to reduce latency while preserving prototype quality?
Answer: B
Explanation:
Retrieval-Augmented Generation (RAG) is an architectural approach that retrieves relevant information from an external knowledge source and supplies that information to a generative model during inference. Option A is correct because retrieving only the relevant portions of a 50-page technical manual significantly reduces unnecessary input-token processing while preserving task-specific information.
Time-to-first-token (TTFT) measures the interval between submitting an inference request and receiving the first generated token. Processing extensive input context can contribute to increased prefill latency.
A RAG pipeline typically divides the technical manual into passages, creates searchable representations, and retrieves relevant sections based on the user's query. Only selected passages are included in the prompt.
Option B is incorrect because removing formatting may save some tokens but does not address the fundamental inefficiency of transmitting the entire manual.
Option C is incorrect because increasing the output-token allowance does not reduce input processing time.
Option D is incorrect because larger models can have higher inference costs and latency.
RAG can improve response relevance and resource efficiency, although retrieval introduces its own processing overhead. Effective implementation requires appropriate chunking, indexing, retrieval evaluation, and source validation.
Specific Cisco AITECH Course Guide References: Customizing AI Models; AI Architect's Toolkit; Objective
1.5 - RAG and vector databases; Objective 5.4 - Token usage, latency, cost, and output quality.
NEW QUESTION # 24
In a multi-agent system built for complex software testing, why is the coordinator pattern favored instead of letting every agent run in parallel?
Answer: B
Explanation:
The coordinator pattern is a multi-agent orchestration approach in which a supervisory component manages task allocation, dependencies, communication, and the consolidation of results. Option D is the best answer because structured hand-offs enable dependent tasks to execute in an appropriate order with intermediate validation.
In complex software testing, different specialized agents may be responsible for test generation, execution, failure analysis, remediation, and regression verification.
For example, a coordinator may direct one agent to generate integration tests, wait for test execution results, and then assign a failure-analysis agent to investigate unsuccessful cases.
This dependency-aware sequence prevents subsequent agents from operating on incomplete or unverified information.
Option A is incorrect because coordination does not inherently reduce the number of API calls.
Option B is incorrect because cooperating agents do not normally exchange or modify one another's model weights.
Option C is incomplete because parallel execution can be supported by a coordinator, but maximizing parallelism does not explain why coordination is preferable for dependent tasks.
A coordinator does not necessarily force every task to run sequentially. Independent tasks can execute concurrently, while tasks requiring previous results use controlled hand-offs.
This approach improves traceability, error handling, and workflow reliability.
Specific Cisco AITECH Course Guide References: AI-Powered Workflows and Agentic AI; Objective 6.2 - AI agent design, autonomous capabilities, and orchestration.
NEW QUESTION # 25
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