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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI for Code and Workflow Optimization | - Workflow automation
| |
| Topic 2: Agentic AI | 20% | - Data transformation and mapping
|
| Topic 3: Prompt Engineering | 15% | - Prompt structures
|
| Topic 4: Generative AI Models | 20% | - Common use cases
|
| Topic 5: Data Research and Analysis | - Data collection and preparation
| |
| Topic 6: AI Ethics and Security | - Security threats and mitigations
|
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NEW QUESTION # 45
Which threat involves an attacker intentionally contaminating the training set of a model to influence its future behavior?
Answer: D
Explanation:
Data poisoning occurs when an attacker deliberately manipulates or contaminates training data so the model learns harmful, biased, or attacker-controlled behavior.
NEW QUESTION # 46
A team uses AI to scaffold a new microservice. What does this process involve?
Answer: D
Explanation:
AI-assisted scaffolding refers to the automated generation of the initial structure, foundational components, and boilerplate code required to establish a software application or microservice. Option B is correct because scaffolding accelerates software development by creating a consistent project foundation before developers implement application-specific business logic.
In microservice architecture, AI-powered scaffolding tools can generate directory structures, configuration files, API endpoint templates, dependency manifests, application entry points, and basic service interfaces.
For example, a developer can instruct a generative AI coding assistant to initialize a Python FastAPI microservice with predefined REST endpoints, dependency management, and a standard project layout.
This approach reduces repetitive development tasks, promotes architectural consistency, and enables engineers to concentrate on service functionality.
Option A is incorrect because removing redundant logic constitutes code refactoring or optimization rather than scaffolding.
Option C is incorrect because production code testing involves functional verification, integration testing, and quality assurance after implementation.
Option D is incorrect because generating automated deployment scripts belongs primarily to deployment automation and CI/CD processes, although scaffolding tools may optionally generate initial deployment configuration templates.
Cisco's AITECH curriculum emphasizes AI-powered code generation and improving software engineering efficiency throughout the development lifecycle.
Specific Cisco AITECH Course Guide References:
* AI-Driven Software Engineering
* AI Systems Integration with APIs
* Exam domain: AI for Code and Workflow Optimization
These topics appear in Cisco's official AITECH course overview and learning objectives.
NEW QUESTION # 47
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 # 48
A practitioner is estimating the operational cost of integrating a cloud-hosted LLM API into an application.
How does tokenization affect the cost of using this API?
Answer: D
Explanation:
Tokenization is the process of dividing text into smaller computational units called tokens, which are processed by large language models during inference. Option D is correct because many cloud-hosted LLM APIs calculate usage charges according to the number of input and output tokens processed.
Input tokens represent information transmitted to the model, including prompts, instructions, conversation history, and supporting context. Output tokens represent the content generated by the model.
For example, consider an API charging $2 per million input tokens and $8 per million output tokens. A request containing 2,000 input tokens and generating 1,000 output tokens costs approximately $0.012.
This illustrates why prompt length, context management, and response limits affect operational expenditure.
Option A is incorrect because usage-based token billing is common among commercial LLM APIs, although some services offer subscriptions or provisioned-capacity pricing.
Option B is incorrect because requests with substantially different token counts can incur different charges, even when the API call count remains identical.
Option C is incorrect because tokenization is not itself an additional GPU execution charge. Its cost significance primarily arises from token-based inference billing.
Organizations can optimize expenditure by reducing redundant context, selecting appropriate models, and monitoring token consumption.
Specific Cisco AITECH Course Guide References:
* AI Architect's Toolkit
* Generative AI Ecosystem
* Exam Objective 1.3: Context windows, token limits, and response management
* Exam Objective 5.4: Token usage, prototyping cost, latency, and output quality Cisco explicitly associates token management with AI application economics.
NEW QUESTION # 49
An analyst needs to use a model capable of simultaneously analyzing a network topology diagram along with a set of configuration logs. Which type of model should be chosen for this task?
Answer: D
Explanation:
A multimodal AI model is designed to process information from multiple data modalities, such as text, images, audio, and video. Option A is correct because the task requires simultaneous interpretation of a visual network topology diagram and textual configuration logs.
Traditional text-only language models primarily process textual information, while vision-only models focus on images.
A multimodal model can combine information extracted from both sources to develop a more complete understanding of the network environment.
For example, the model might identify connections between routers and switches from a topology diagram and compare those connections against interface configurations, routing information, and error messages contained in device logs.
This can help identify configuration inconsistencies, interface mismatches, or potential connectivity problems.
Option B is incorrect because diffusion models are primarily used for iterative generative tasks, particularly image synthesis.
Option C is incorrect because embedding models generate numerical representations for similarity analysis and retrieval rather than necessarily performing integrated multimodal reasoning.
Option D is incorrect because generative adversarial networks are primarily associated with adversarial generative training architectures.
Multimodal analysis is valuable for network troubleshooting, infrastructure documentation, technical diagram interpretation, and operational diagnostics.
However, important findings should still be verified against authoritative device configurations and telemetry.
Specific Cisco AITECH Course Guide References: Generative AI Ecosystem; AI-Driven Multimodal Asset Creation; Exam Objective 1.4 - Model selection for reasoning and multimodality.
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NEW QUESTION # 50
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