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| Section | Weight | Objectives |
|---|---|---|
| AI for Code and Workflow Optimization | - Automation workflows
| |
| Prompt Engineering | 15% | - Prompt design principles
|
| Ethics and Security | 15% | - Data privacy and governance
|
| Agentic AI Systems | - Agent architecture
| |
| Generative AI Models | 20% | - Model hosting and architecture
|
| AI for Data Research and Analysis | - Data exploration and transformation
|
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NEW QUESTION # 50
When using AI to automate data preparation, why is it essential to include a "quality check" step?
Answer: B
Explanation:
Data quality validation is a critical stage of automated data preparation that verifies whether transformation, normalization, cleaning, and enrichment operations have preserved the intended meaning and integrity of source information. Option A is correct because AI-driven processing can inadvertently introduce errors or misinterpret records.
Automated data preparation may involve changing date formats, removing duplicates, standardizing field names, handling missing values, and converting data types.
For example, a system converting customer transaction records from multiple regional formats might interpret an ambiguous date incorrectly, causing transactions to appear in the wrong reporting period.
A quality check can detect such errors before the dataset is used for analytical reporting or model training.
Option B is incorrect because quality validation does not guarantee perfectly balanced class distributions, and perfect balance is not necessarily appropriate for every dataset.
Option C is incorrect because data preparation checks do not automatically update a model's training knowledge.
Option D is incorrect because reducing memory consumption is a performance optimization objective, not the primary purpose of data validation.
Effective quality checks examine schema compliance, missingness, data types, value ranges, duplicates, referential integrity, and consistency with original records.
Specific Cisco AITECH Course Guide References: AI for Data Engineering and Exploration; Objective 4.2
- Automated data preparation, quality checks, formatting, transformation, and cleaning.
NEW QUESTION # 51
Compared to a typical generative AI use case, which of the following represents an agentic AI use case?
Answer: A
Explanation:
Agentic AI refers to systems capable of pursuing objectives through coordinated, multi-step operations involving planning, reasoning, tool invocation, feedback evaluation, and authorized actions. Option C is correct because autonomously fixing a software build requires the AI system to investigate a problem and execute corrective operations rather than simply generate content.
An agentic development assistant might begin by examining a failed continuous integration pipeline, retrieving build logs, identifying the probable source of the failure, and inspecting relevant source files.
The agent can then propose or apply code changes, execute tests, evaluate the results, and continue troubleshooting until the build succeeds or a predefined execution limit is reached.
This represents an iterative observe-plan-act-evaluate workflow.
Option A is incorrect because generating a meeting summary is primarily a content synthesis task.
Option B is incorrect because writing a poem represents creative text generation.
Option D is incorrect because translating an email is a conventional language transformation task.
Although traditional generative AI capabilities can support every stage of an agentic workflow, the distinguishing characteristic is the ability to coordinate actions toward an operational objective.
Production agents should implement permissions, execution limits, audit trails, and human approval requirements for consequential changes.
Specific Cisco AITECH Course Guide References: AI-Powered Workflows and Agentic AI; AI-Driven Software Engineering; Exam Objective 6.1 - Agentic versus generative AI use cases; Objective 6.2 - Autonomous capabilities and orchestration.
Cisco AI Technical Practitioner (810-110 AITECH) - Questions 51-75
The 25 questions are continued in the existing sequence and evaluated against Cisco's official AITECH v1.0 exam blueprint and course outline. These cover Generative AI Models, Prompt Engineering, Ethics and Security, Data Research and Analysis, Development and Workflow Automation, and Agentic AI.
NEW QUESTION # 52
In the software development lifecycle, which phase makes use of AI to review stakeholder documentation for logical inconsistencies and to generate structured user stories?
Answer: C
Explanation:
The requirements phase of the Software Development Lifecycle (SDLC) focuses on identifying, analyzing, documenting, and validating the functional and nonfunctional requirements of a software solution. Option A is correct because reviewing stakeholder documentation and generating structured user stories are fundamental requirements engineering activities.
Artificial intelligence can assist requirements analysts by processing business documents, meeting transcripts, project specifications, and stakeholder feedback to identify functional expectations and technical constraints.
For example, an AI assistant might analyze a stakeholder's request for a customer authentication system and generate user stories describing account registration, password recovery, multifactor authentication, and access control requirements.
AI can also identify ambiguous terminology, conflicting requirements, missing acceptance criteria, and dependencies between requested features.
Option B is incorrect because maintenance focuses on correcting defects, improving performance, and updating software after deployment.
Option C is incorrect because implementation involves translating approved designs and requirements into executable code.
Option D is incorrect because testing validates software behavior against requirements, although AI can also generate test cases from user stories.
AI-assisted requirements analysis improves documentation consistency, requirement traceability, and development readiness. Generated requirements nevertheless require stakeholder validation before implementation.
Specific Cisco AITECH Course Guide References:
* AI-Driven Software Engineering
* AI-Powered Discovery and Synthesis
* Exam Objective 5.1: AI's role across the software development lifecycle Cisco explicitly includes requirements analysis within its AI-assisted software development objectives.
NEW QUESTION # 53
When instructing an AI system to summarize a particular technical document, which approach helps reduce the risk of hallucinations in the output?
Answer: D
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 # 54
What is the benefit of AI Ops (AIOps) in networking?
Answer: C
Explanation:
AIOps uses AI to improve IT operations, detect anomalies, and accelerate troubleshooting.
NEW QUESTION # 55
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