WGU D685
WGU D685 Practical Applications of Prompt |PA and OA | Explore
New 253 Questions and Answers | 2026 Update | 100% Correct.
Gradegurus
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,Page 1
1. Why is transparency (or explainability) important in AI systems?
A. Explainable AI (XAI) optimizes AI performance by streamlining decision
processes.
B. Explainable AI (XAI) provides understandable justifications for AI decisions and
supports accountability.
C. Explainable AI (XAI) improves AI efficiency by automating justification
processes.
,D. Explainable AI (XAI) enhances AI security by encrypting decision logic.
Answer: B
Rationale: Transparency allows users to understand why an AI made a decision,
which supports trust and accountability.
2. How do advanced prompting techniques improve LLM outputs?
A. Advanced techniques like chain-of-thought and zero-shot enhance LLM data
storage.
B. Advanced techniques like few-shot and self-consistency simplify LLM
reasoning.
C. Advanced techniques like zero-shot and chain-of-thought reduce LLM
processing time.
D. Advanced techniques like few-shot, chain-of-thought, and self-consistency
guide LLM reasoning and improve response quality.
Answer: D
Rationale: Techniques like few-shot, CoT, and self-consistency structure how the
model reasons, improving accuracy and relevance.
3. includes text documents, images, videos, audio recordings, social media
posts, and other types of data that do not fit neatly into a structured format
A. unstructured data
B. semi structured data
C. big data
D. semistructured data
Answer: A
Rationale: Unstructured data lacks a predefined data model or organization.
Page 2
4. the process of identifying and correcting errors, inconsistencies, and
inaccuracies in datasets to ensure quality and reliability
A. data cleaning
B. data visualization
C. data exploration
, D. data integration
Answer: A
Rationale: Data cleaning (or cleansing) fixes errors and missing values before
analysis.
5. What type of bias? The AI model may not accurately predict treatment
outcomes for other age groups if clinical trial data are predominantly from a
specific age group.
A. adaptability
B. output quality
C. summarizing text
D. Selection Bias
Answer: D
Rationale: Selection bias occurs when training data is not representative of the
population.
6. How can prompts be improved for domain-specific or technical topics?
A. Using general terms and omitting context helps the AI process queries faster.
B. Simplifying language and using basic vocabulary improves AI comprehension.
C. Providing context and avoiding jargon helps the AI understand specialized
queries more accurately.
D. Providing visual aids and diagrams assists the AI in understanding specialized
queries.
Answer: C
Rationale: Context and clear (but not oversimplified) language help the model
interpret technical terms correctly.
Page 3
7. the phenomenon where the performance of an AI model deteriorates over time
as the underlying data distribution changes
A. AI drift
B. AI model
C. Hallucinations
WGU D685 Practical Applications of Prompt |PA and OA | Explore
New 253 Questions and Answers | 2026 Update | 100% Correct.
Gradegurus
➢
➢
➢
➢
➢
,Page 1
1. Why is transparency (or explainability) important in AI systems?
A. Explainable AI (XAI) optimizes AI performance by streamlining decision
processes.
B. Explainable AI (XAI) provides understandable justifications for AI decisions and
supports accountability.
C. Explainable AI (XAI) improves AI efficiency by automating justification
processes.
,D. Explainable AI (XAI) enhances AI security by encrypting decision logic.
Answer: B
Rationale: Transparency allows users to understand why an AI made a decision,
which supports trust and accountability.
2. How do advanced prompting techniques improve LLM outputs?
A. Advanced techniques like chain-of-thought and zero-shot enhance LLM data
storage.
B. Advanced techniques like few-shot and self-consistency simplify LLM
reasoning.
C. Advanced techniques like zero-shot and chain-of-thought reduce LLM
processing time.
D. Advanced techniques like few-shot, chain-of-thought, and self-consistency
guide LLM reasoning and improve response quality.
Answer: D
Rationale: Techniques like few-shot, CoT, and self-consistency structure how the
model reasons, improving accuracy and relevance.
3. includes text documents, images, videos, audio recordings, social media
posts, and other types of data that do not fit neatly into a structured format
A. unstructured data
B. semi structured data
C. big data
D. semistructured data
Answer: A
Rationale: Unstructured data lacks a predefined data model or organization.
Page 2
4. the process of identifying and correcting errors, inconsistencies, and
inaccuracies in datasets to ensure quality and reliability
A. data cleaning
B. data visualization
C. data exploration
, D. data integration
Answer: A
Rationale: Data cleaning (or cleansing) fixes errors and missing values before
analysis.
5. What type of bias? The AI model may not accurately predict treatment
outcomes for other age groups if clinical trial data are predominantly from a
specific age group.
A. adaptability
B. output quality
C. summarizing text
D. Selection Bias
Answer: D
Rationale: Selection bias occurs when training data is not representative of the
population.
6. How can prompts be improved for domain-specific or technical topics?
A. Using general terms and omitting context helps the AI process queries faster.
B. Simplifying language and using basic vocabulary improves AI comprehension.
C. Providing context and avoiding jargon helps the AI understand specialized
queries more accurately.
D. Providing visual aids and diagrams assists the AI in understanding specialized
queries.
Answer: C
Rationale: Context and clear (but not oversimplified) language help the model
interpret technical terms correctly.
Page 3
7. the phenomenon where the performance of an AI model deteriorates over time
as the underlying data distribution changes
A. AI drift
B. AI model
C. Hallucinations
