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| Section | Weight | Objectives |
|---|---|---|
| AI for Code and Workflow Optimization | 15% | - Integration of AI tools in development processes - Automation and workflow enhancement - AI-assisted code generation and improvement |
| Data Research and Analysis | 15% | - Data quality, bias and validation - Data collection, preparation and preprocessing - Data visualization and insight generation |
| Prompt Engineering | 15% | - Advanced prompting techniques - Defensive prompting and error mitigation - Prompt design principles and best practices |
| Agentic AI | 15% | - Agentic AI vs Generative AI use cases - Model Context Protocol (MCP) framework - Agent design principles and capabilities - Human-in-the-loop strategies |
| AI Ethics and Security | 20% | - Data privacy and security in AI systems - Compliance and governance frameworks - AI-specific threats, risks and misinformation - Responsible AI principles |
| Generative AI Models | 20% | - Generative AI model families and use cases - Context windows, token limits and response management - Model hosting options and trade-offs - Model selection and evaluation in AI hubs |
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NEW QUESTION # 62
A practitioner must reduce the financial cost of a high-volume summarization task. Which strategy addresses token-based billing?
Answer: D
Explanation:
Token-based billing is driven by the number of input and output tokens processed, so reducing the prompt size lowers the total tokens consumed across high-volume summarization requests.
NEW QUESTION # 63
When instructing an AI system to summarize a particular technical document, which approach helps reduce the risk of hallucinations in the output?
Answer: A
Explanation:
AI hallucination occurs when a generative model produces information that is unsupported, inaccurate, or fabricated while presenting it as plausible. Option A is correct because requesting citations encourages the model to associate factual claims with supporting evidence from the technical document.
For example, a practitioner summarizing a networking standard can instruct the AI system to include section references for significant technical assertions.
This creates an opportunity to check whether the generated summary accurately reflects the source material.
For stronger reliability, the application should provide the authoritative document, require references to specific passages, and verify that cited evidence supports the corresponding statements.
Option B is incorrect because reducing system prompt length does not inherently improve factual accuracy.
Removing essential instructions may make reliability worse.
Option C is incorrect because increasing generation randomness can introduce greater variability and does not inherently reduce hallucinations.
Option D is incorrect because removing useful constraints may weaken the boundaries governing the model's response.
Importantly, citations generated by an AI model can themselves be fabricated. Merely requesting citations does not establish factual correctness.
The strongest approach combines source-grounded generation, accurate references, evidence validation, and explicit instructions to avoid unsupported claims.
Specific Cisco AITECH Course Guide References: Prompt Engineering for Technical Precision; Debugging and Correcting AI-Generated Outputs; Exam Objective 2.4 - Defensive prompting and mitigation of AI- generated errors.
Cisco Learning Content
NEW QUESTION # 64
Which approach protects PII when utilizing a public AI service for data analysis?
Answer: A
Explanation:
Anonymizing or redacting sensitive personal information before sending data to a public AI service prevents PII from being exposed outside the organization's controlled environment.
NEW QUESTION # 65
What is an advantage of cloud-hosted API services when comparing cloud-hosted and locally hosted AI models?
Answer: C
Explanation:
Cloud-hosted API services can scale inference capacity quickly based on demand, making them suitable for workloads with unpredictable or fluctuating usage.
NEW QUESTION # 66
Which of the following represents a use case for applying generative AI during the testing phase of the software development lifecycle?
Answer: A
Explanation:
Generative AI can support software testing by producing synthetic datasets that resemble expected application inputs without requiring the direct use of real customer or production records. Option B is correct because generating synthetic test data is a direct application of generative AI during the testing phase of the Software Development Lifecycle.
Synthetic test data can simulate typical user interactions, boundary conditions, exceptional inputs, and unusual combinations of application values.
For example, an AI system could generate fictional customer records containing valid email formats, international address structures, different account categories, and representative transaction histories.
Developers can use these datasets to test validation rules, database operations, API behavior, and application error handling.
Option A is incorrect as the best answer because generating automated documentation is primarily a documentation activity, although it can support several SDLC stages.
Option C is incorrect because drafting user stories belongs primarily to requirements analysis.
Option D is less appropriate because static code analysis examines source code without executing it and is not inherently a generative AI task.
Synthetic datasets should be evaluated for realism, boundary-condition coverage, privacy leakage, and consistency with application requirements.
Generated test data supplements, rather than replaces, comprehensive testing and quality assurance.
Specific Cisco AITECH Course Guide References: AI-Driven Software Engineering; Exam Objective 5.1 - AI across SDLC phases, including testing; Objective 5.2 - AI-assisted code generation and prototyping.
Cisco Learning Content
NEW QUESTION # 67
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