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WGU D685 Practical Applications of Prompt Engineering – Objective Assessment Newest Exam Questions and Correct Answers

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Pass the WGU D685 Objective Assessment on your first attempt with this newest set of exam questions and verified correct answers. Covers all key topics including AI hallucinations (causes, risks, mitigation), misinformation vs. disinformation, training data improvements for reliability, generative AI for data sorting, ethical concerns (user trust, bias, fairness, transparency, accountability), AI image generation tools (DALL-E, Midjourney, InVideo), text-to-image and text-to-video capabilities, prompt engineering techniques (Zero-Shot, Few-Shot, Chain-of-Thought, Tree-of-Thought, Self-Consistency, Cognitive Verifier Pattern, Least-to-Most, Generated Knowledge Prompting), large language models (LLMs), machine learning types (supervised, unsupervised, reinforcement learning), neural networks, deep learning, natural language processing (NLP), data bias types (sampling bias, measurement bias, algorithmic bias, selection bias, confirmation bias), echo chambers, user confirmation bias, bias mitigation strategies (diverse datasets, detection tools, transparency, continuous monitoring), ethical AI principles (fairness, privacy, safety, accountability, societal impact), data preprocessing (cleaning, handling missing values, outlier detection), classification, clustering, dynamic sorting, real-time data processing, structured vs. unstructured data, responsible AI, deepfakes, intellectual property concerns in AI-generated content, regulatory frameworks, and AI limitations. Written for WGU D685 computer science and AI literacy candidates.

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WGU D685 - PRACTICAL
APPLICATIONS OF

PROMPT EXAM | OBJECTIVE
ASSESSMENT NEWEST

EXAM QUESTIONS AND CORRECT

ANSWERS



Why does rapid AI content generation pose a misinformation risk?

A. It allows faster multi-language support that leads to confusion

B. It overwhelms fact-checking processes, enabling falsehoods to proliferate

C. It increases the model's preference for shorter completions

D. It reduces the diversity of neural pathways in fine-tuning - ✔✔✔ Correct
Answer > B. It overwhelms fact-checking processes, enabling falsehoods to
proliferate



What technical challenge contributes to AI hallucinations?

A. Models are trained with datasets that vary widely in source reliability

B. Models struggle with image-to-text conversion pipelines


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,C. Transformer layers are over-optimized for speed rather than depth

D. Sampling techniques override prompt constraints - ✔✔✔ Correct Answer
> A. Models are trained with datasets that vary widely in source reliability



How can training data improvements help reduce hallucinations?

A. By excluding multimodal content from text generation tasks

B. By integrating curated, trustworthy data into training sets

C. By removing low-entropy text to reduce variability

D. By using more dropout layers during model optimization - ✔✔✔

Correct Answer > B. By integrating curated, trustworthy data into training sets



What is an AI hallucination?

A. A language model generating plausible but false or misleading
information

B. An AI system refusing to make predictions on unfamiliar input

C. An error in converting training data into structured output

D. A model failing to retrieve relevant knowledge from its database - ✔✔✔
Correct Answer > A. A language model generating plausible but false or
misleading information



What is the primary advantage of using generative AI for data sorting?

A. It can infer, organize, and optimize data beyond static rulebased methods


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,B. It eliminates the need for database schemas

C. It simplifies hardware configurations for data centers

D. It prevents all forms of data loss - ✔✔✔ Correct Answer > A. It can infer,
organize, and optimize data beyond static rule-based methods



What is a key ethical concern related to AI hallucinations?

A. They erode user trust in the credibility of AI-generated content

B. They result in redundant token generation that reduces efficiency

C. They make it difficult to audit system logs post-deployment

D. They cause latency spikes in multi-modal models - ✔✔✔ Correct Answer
> A. They erode user trust in the credibility of AI-generated content



Why are AI hallucinations particularly concerning in critical fields?

A. They can lead to harmful decisions based on inaccurate information

B. They increase model entropy in large-scale deployment

C. They trigger fallback protocols that delay system performance

D. They reduce training efficiency in domain-specific models -

✔✔✔ Correct Answer > B. They can lead to harmful decisions based on
inaccurate information



How do AI hallucinations contribute to misinformation?

A. They ignore token limits during generation


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, B. They increase memory usage when summarizing content

C. They inadvertently generate and spread inaccurate content

D. They rely too heavily on cached prompts - ✔✔✔ Correct Answer > C.

They inadvertently generate and spread inaccurate content



What distinguishes AI-generated misinformation from disinformation?

A. Misinformation involves malformed syntax - ✔✔✔ Correct Answer >



disinformation does not

B. Misinformation is generated unintentionally - ✔✔✔ Correct Answer >



disinformation is deliberately false

C. Misinformation originates from third-party plugins

D. Misinformation requires adversarial input, disinformation does not -
✔✔✔ Correct Answer > B. Misinformation is generated unintentionally



disinformation is deliberately false -



Which of the following is a potential social impact of AIgenerated
misinformation?

A. Amplification of biased narratives and erosion of public discourse

B. Reduction of AI output latency across platforms

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