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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Research and Analysis | - Data collection and preparation
| |
| Topic 2: Prompt Engineering | 15% | - Prompting techniques
|
| Topic 3: Agentic AI | 20% | - Differentiation from Generative AI
|
| Topic 4: AI Ethics and Security | - Security threats and mitigations
| |
| Topic 5: AI for Code and Workflow Optimization | - Workflow automation
| |
| Topic 6: Generative AI Models | 20% | - Model hosting strategies
|
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NEW QUESTION # 25
Drag each analytical bias from the left to the scenario it describes on the right. Not all options will be used.
Answer:
Explanation:
Explanation:
Analytical biases can compromise the reliability, fairness, and interpretation of AI-generated insights. The three scenarios demonstrate distinct mechanisms through which analytical conclusions become distorted.
1. Aggregation Bias: This occurs when combining data into broad summaries conceals meaningful differences across subgroups. In the first scenario, the AI emphasizes majority customer trends while overlooking a significant issue affecting a smaller customer segment. Segment-level analysis helps identify these hidden patterns.
2. Automation Bias: This describes the tendency to over-rely on automated recommendations while neglecting independent evaluation. The second scenario demonstrates automation bias because users accept AI-generated conclusions without applying professional judgment or validating the evidence.
3. Confirmation Bias: This occurs when individuals preferentially seek, interpret, or select information supporting their existing beliefs. In the third scenario, the practitioner directs the AI to collect evidence supporting a predetermined opinion rather than objectively evaluating competing explanations.
Historical Bias (unused): This arises when datasets reflect existing inequalities, outdated practices, or historical patterns that can produce distorted or unfair outcomes.
Mitigation strategies include subgroup analysis, independent verification, balanced research questions, human oversight, and representative datasets.
Specific Cisco AITECH Course Guide References:
* AI for Data Engineering and Exploration
* Generative AI Security and Privacy Fundamentals
* Exploratory Data Analysis and Bias Identification
* Responsible AI: Fairness and Bias Mitigation
NEW QUESTION # 26
An AI model detects 300 duplicate entries in a customer feedback dataset. What is the responsible way to address these duplicates before continuing with analysis?
Answer: C
Explanation:
Duplicate detection is an important data preparation activity that identifies potentially repeated records before analytical processing. Option B is correct because flagging duplicates and reviewing representative examples provides a validation mechanism before potentially irreversible changes are made.
AI-assisted deduplication systems can identify exact matches, near-duplicate text, and semantically similar feedback entries.
However, records that appear similar are not necessarily redundant. Multiple customers may independently report the same issue, and those individual complaints can carry legitimate statistical significance.
For example, two support tickets containing identical descriptions may represent separate incidents affecting different customers.
Automatically deleting both as duplicates could distort complaint frequency and produce inaccurate conclusions.
Option A is incorrect because retaining confirmed duplicate records may artificially inflate counts.
Option C is not universally appropriate because merging records without verifying their identity can combine distinct observations.
Option D is incorrect because deleting records without validation risks losing legitimate data.
A responsible deduplication process should define matching criteria, preserve original records, assess false- positive matches, and maintain an audit trail.
Sample review is a useful validation step, but higher-risk cases may require broader inspection or automated integrity checks.
Specific Cisco AITECH Course Guide References: AI for Data Engineering and Exploration; Objective 4.2
- Data preparation, quality checks, cleaning, and transformation; Objective 4.3 - Ethical considerations in AI-assisted analysis.
NEW QUESTION # 27
Which threat involves an attacker intentionally contaminating the training set of a model to influence its future behavior?
Answer: A
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 # 28
A developer gives an AI model three examples, each pairing a log entry with a specific error category, before asking it to categorize a new log entry. Which prompting technique does this represent?
Answer: D
Explanation:
Few-shot prompting is a technique in which a generative AI model receives a small number of representative examples demonstrating the expected relationship between inputs and outputs. Option C is correct because the developer supplies three labeled log examples before requesting classification of a previously unseen entry.
In this scenario, each example establishes a relationship between a diagnostic log message and its corresponding error category.
For example, a prompt might associate authentication failures with an authentication category, connection timeouts with a connectivity category, and resource exhaustion messages with a capacity category.
The model uses these demonstrations as contextual guidance when classifying the new entry.
Option A is incorrect because zero-shot prompting requests task completion without providing task-specific examples.
Option B is incorrect because chain-of-thought prompting focuses on intermediate reasoning processes rather than representative input-output demonstrations.
Option D is incorrect because metaprompting involves prompts that help generate, evaluate, or improve other prompts.
Few-shot examples should be accurate, representative, and consistent with the desired classification rules.
The technique can improve task adherence without modifying the model's underlying weights, although performance depends on model capability, example quality, and the complexity of the classification problem.
Specific Cisco AITECH Course Guide References: Prompt Engineering for Technical Precision; Advanced Prompting Strategies; Exam Objective 2.2 - Few-shot, iterative, sequential, and chained prompting techniques.
Cisco Learning Content
NEW QUESTION # 29
When using AI to draft a technical proposal, what is the benefit of adopting a format-agnostic strategy?
Answer: D
Explanation:
A format-agnostic content strategy focuses on producing reusable information without tightly coupling the initial content to a particular presentation format, document template, or publishing platform. Option C is correct because format independence allows AI-generated material to be adapted for different audiences and delivery channels.
For example, a technical proposal may initially be generated as structured content containing objectives, architectural requirements, implementation considerations, and expected outcomes.
That content can subsequently be converted into a formal PDF proposal, an executive presentation, a Markdown document, or a web-based project summary.
Separating the underlying information from its final presentation format reduces duplicated drafting effort and improves content maintainability.
Option A is incorrect because contextual alignment depends on the accuracy and relevance of supplied instructions, background information, and constraints.
Option B is incorrect because format independence does not inherently reduce token consumption, although efficient reuse may reduce repeated generation.
Option D is incorrect because creativity depends on model capabilities and prompting strategies rather than format independence.
A format-agnostic approach is especially valuable when technical documentation must support multiple stakeholders, content management systems, or reporting requirements.
The generated material should retain consistent terminology, factual accuracy, and traceability across its different output formats.
Specific Cisco AITECH Course Guide References: Prompt Engineering for Technical Precision; AI-Powered Discovery and Synthesis; Exam Objective 2.2 - Prompt structures for content generation; Objective 4.4 - AI-assisted content drafting.
Cisco Learning Content
NEW QUESTION # 30
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