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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Generative AI Models | 20% | - Model hosting options and trade-offs - Context windows, token limits and response management - Model selection and evaluation in AI hubs - Generative AI model families and use cases |
| Topic 2: Prompt Engineering | 15% | - Defensive prompting and error mitigation - Advanced prompting techniques - Prompt design principles and best practices |
| Topic 3: AI for Code and Workflow Optimization | 15% | - AI-assisted code generation and improvement - Automation and workflow enhancement - Integration of AI tools in development processes |
| Topic 4: Agentic AI | 15% | - Agent design principles and capabilities - Human-in-the-loop strategies - Agentic AI vs Generative AI use cases - Model Context Protocol (MCP) framework |
| Topic 5: Data Research and Analysis | 15% | - Data visualization and insight generation - Data collection, preparation and preprocessing - Data quality, bias and validation |
| Topic 6: AI Ethics and Security | 20% | - Compliance and governance frameworks - AI-specific threats, risks and misinformation - Responsible AI principles - Data privacy and security in AI systems |
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NEW QUESTION # 94
During the Testing phase of the SDLC, a QA engineer must validate a new payment-processing function.
Why is AI-generated synthetic data regarded as a high-value asset at this stage?
Answer: C
Explanation:
AI-generated synthetic data enables software testing teams to construct representative, artificial datasets that simulate realistic application inputs without requiring exposure of actual customer records. Option C is correct because synthetic data supports testing of normal transactions, boundary conditions, exceptional scenarios, and error-handling mechanisms while reducing the privacy risks associated with using production payment information.
For example, a QA engineer can generate fictional payment transactions involving expired cards, insufficient balances, duplicate payment requests, invalid currencies, unusually large amounts, and transaction timeouts.
These scenarios help validate payment-processing logic, input validation, exception handling, and transaction consistency.
Option A is incorrect because synthetic data supplies test inputs but does not replace testing frameworks, execution environments, or automated test assertions.
Option B is incorrect because generating test data does not establish compliance with PCI DSS, financial regulations, or other legal requirements. Compliance requires independent assessment against applicable controls.
Option D is incorrect because synthetic data generation does not automatically discover and remediate vulnerabilities.
Synthetic datasets should undergo quality assessment to ensure appropriate coverage and avoid reproducing sensitive records.
AI-generated test data is most valuable when combined with established testing frameworks, controlled test environments, and reproducible validation procedures.
Specific Cisco AITECH Course Guide References:
* AI-Driven Software Engineering
* AI for Data Engineering and Exploration
* Exam Objective 5.1: AI across SDLC phases, including testing
* Exam Objective 4.2: Automated data preparation and quality checks
NEW QUESTION # 95
Which algorithm category is used when no labels exist in data?
Answer: B
Explanation:
Unsupervised learning discovers patterns or groupings without labeled outputs.
NEW QUESTION # 96
A team uses AI to scaffold a new microservice. What does this process involve?
Answer: A
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 # 97
Drag and drop the use case from the left onto the AI system type on the right.
Answer:
Explanation:
Explanation:
Correct Drag-and-Drop Placement:
Generative AI
Drafting a technical summary of a meeting transcript for content creation.
Producing realistic synthetic datasets to validate software edge cases during testing.
Agentic AI
Executing a multi-step sequence to autonomously manage database record deletions.
Observing a failed build, analyzing logs, and deciding which diagnostic tool to run next.
Generative AI and Agentic AI represent different approaches to applying artificial intelligence, particularly regarding content creation, autonomous decision-making, and workflow execution.
1. Generative AI - Technical Summary: Generative AI produces new content based on supplied information.
Summarizing meeting transcripts involves processing existing text, identifying important information, and generating a coherent technical summary. The task does not require autonomous execution of external actions.
2. Generative AI - Synthetic Test Data: Generative AI can create realistic artificial datasets to support software testing. These datasets can simulate normal transactions, boundary conditions, and exceptional scenarios without exposing actual customer information.
3. Agentic AI - Database Record Deletion: Agentic AI can coordinate multiple actions toward a defined objective. Managing database deletions may involve identifying qualifying records, validating deletion criteria, invoking database tools, and verifying results. Production deletions should require appropriate human authorization.
4. Agentic AI - Build Troubleshooting: An agentic system can observe a failed software build, analyze diagnostic logs, select troubleshooting tools, and adapt its next action based on execution results. This demonstrates iterative, goal-directed problem-solving.
The fundamental distinction is that Generative AI primarily creates content, whereas Agentic AI coordinates decisions and actions to accomplish operational goals.
Specific Cisco AITECH Course Guide References:
* Generative AI Ecosystem
* AI-Driven Software Engineering
* AI-Powered Workflows and Agentic AI
* Exam Objective 6.1: Differentiate Agentic AI from Generative AI use cases
* Exam Objective 6.2: AI agent design principles, autonomous capabilities, and orchestration
* Exam Objective 5.1: AI across the software development lifecycle
Cisco's published AITECH blueprint explicitly covers these distinctions.
NEW QUESTION # 98
An organization operates under regulatory requirements that forbid any proprietary source code from leaving its internal network. Which hosting strategy offers the greatest degree of data sovereignty?
Answer: C
Explanation:
Data sovereignty concerns an organization's ability to control where information is stored, processed, and governed, particularly when legal or regulatory requirements restrict data movement. Option D is correct because deploying an LLM on internally controlled hardware offers the strongest direct control over proprietary source code and its processing environment.
With a fully local deployment, model inference can occur within the organization's internal network without transmitting prompts or sensitive source code to external AI providers.
For example, an enterprise can deploy an internally hosted coding assistant that processes confidential repositories while operating under its existing authentication, network security, and access control policies.
Option A is incorrect because Zero Data Retention does not necessarily eliminate external transmission or processing. The data still leaves the internal network.
Option B is incorrect because cloud-based backup may transfer proprietary information outside the permitted environment.
Option C is incorrect because SOC 2 attestation provides assurance regarding specified controls but does not establish that sensitive data remains within the organization's internal network.
A local deployment must still enforce security controls, including access restrictions, encryption, audit logging, and secure model management.
Administrators should also disable unauthorized telemetry, external tool calls, and data synchronization features that could violate the organization's restrictions.
Specific Cisco AITECH Course Guide References: AI Architect's Toolkit; Customizing AI Models; Generative AI Security and Privacy Fundamentals; Exam Objective 1.2 - Cloud versus local hosting trade- offs; Objective 3.2 - Corporate data privacy and security.
Cisco Learning Content
NEW QUESTION # 99
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