WGU D685 PRACTICAL APPLICATIONS OF PROMPT ENGINEERING | COMPLETE EXAM QUESTIONS & VERIFIED ANSWERS | GRADED A+

WGU D685 PRACTICAL APPLICATIONS OF PROMPT
ENGINEERING

Complete Exam Questions and Verified Answers

2026/2027 Edition | Grade A+ | 100% Verified




1. Which of the following best defines prompt engineering?
A) Training a large language model from scratch using custom datasets
B) Designing and optimizing input text to guide a language model's output
C) Evaluating the hardware performance of AI inference systems
D) Writing production deployment code for machine learning models

Correct Answer: B) Designing and optimizing input text to guide a language
model's output
Rationale: Prompt engineering is the practice of designing, refining, and optimizing input
prompts to achieve desired outputs from pre-trained language models. It does not involve
training models from scratch, hardware evaluation, or deployment coding .




2. What is the primary goal of prompt engineering?
A) To write longer prompts with more details
B) To maximize the likelihood of a desired model output
C) To reduce the number of model parameters
D) To train the model from scratch using custom datasets

,Correct Answer: B) To maximize the likelihood of a desired model output
Rationale: Prompt engineering focuses on crafting precise, effective inputs to guide AI
toward accurate, relevant, and high-quality responses without changing model weights .




3. A language model receives a prompt and generates a response. The model has
not been fine-tuned on new data. This is an example of:
A) Zero-shot learning
B) Supervised fine-tuning
C) Reinforcement learning from human feedback (RLHF)
D) Model pre-training

Correct Answer: A) Zero-shot learning
Rationale: Zero-shot learning occurs when a model performs a task without any examples
or additional training, relying solely on its pre-trained knowledge .




4. Which prompting technique provides a few examples to guide the model's
response?
A) Zero-shot prompting
B) Few-shot prompting
C) Chain-of-thought prompting
D) Negative prompting

Correct Answer: B) Few-shot prompting
Rationale: Few-shot prompting provides a small number (typically 2-10) of examples in
the prompt to guide the model via in-context learning .

,5. What is a key risk of writing a poor prompt?
A) Greater data usage
B) Opinion-based information
C) Imprecise results
D) Outdated results

Correct Answer: C) Imprecise results
Rationale: Vague prompts lead the AI to produce imprecise or irrelevant outputs because
they don't clearly communicate intent .




6. A prompt that asks the AI to "Act as a career counselor" is an example of:
A) Zero-shot prompting
B) Role prompting (persona prompting)
C) Chain-of-thought prompting
D) Negative prompting

Correct Answer: B) Role prompting (persona prompting)
Rationale: Role prompting assigns a persona or role to the AI, improving response
relevance by establishing context and perspective .




7. Which component of a prompt provides the AI with background information
and relevant facts to narrow the scope of the query?
A) Persona
B) Context
C) Output format
D) Instruction

, Correct Answer: B) Context
Rationale: Context provides relevant background that shapes how the AI interprets and
answers the query. It gives background knowledge and relevant facts that contextualize a
user's query .




8. Which component of the PIIOA framework includes a list of prohibited words
and strict restrictions?
A) Persona
B) Input
C) Instructions
D) Output format

Correct Answer: C) Instructions
Rationale: The Instructions component of the PIIOA framework outlines the exact
operations the model must perform, along with structural guardrails, negative constraints,
and logical rules it must follow .




9. Which parameter directly controls the trade-off between randomness and
determinism in a language model's output?
A) Max tokens
B) Top_p
C) Temperature
D) Frequency penalty

Correct Answer: C) Temperature
Rationale: Temperature scales the logits before the softmax function. Lower temperatures