WGU D685 Final Exam (NEW 2026/ 2027
Update) Practical Applications of
Prompt Engineering Review | Questions
& Answers | Grade A| 100% Correct
(Verified Solutions)
SECTION 1: PROMPT ENGINEERING FUNDAMENTALS
(Questions 1-25)
Question 1
What is prompt engineering?
A) A software development methodology for building AI systems from
scratch
B) The practice of designing and refining inputs to AI language models
to produce desired outputs
C) A hardware optimization technique for running large language
models faster
D) A method of training neural networks using reinforcement learning
Correct ,,,answer,,,: B
,Rationale: Prompt engineering is the disciplined practice of crafting,
refining, and optimizing input prompts to guide AI language models
toward generating specific, accurate, and useful outputs. It involves
understanding how models interpret language and strategically
structuring instructions to achieve desired results.
Question 2
Which of the following best describes a 'prompt' in the context of large
language models (LLMs)?
A) The GPU memory allocated to a model during inference
B) A hyperparameter used during model training
C) The input text or instruction provided to a language model to guide
its response
D) The tokenization scheme used to encode model outputs
Correct ,,,answer,,,: C
Rationale: A prompt is the user-provided input—whether a question,
instruction, or statement—that serves as the starting point for an LLM's
response generation. The quality and structure of the prompt directly
influence the relevance and accuracy of the model's output.
Question 3
What does the term 'temperature' control in a language model's output?
A) The maximum number of tokens in the response
B) The randomness or creativity of the model's output
,C) The speed at which the model generates tokens
D) The number of layers processed during inference
Correct ,,,answer,,,: B
Rationale: Temperature is a sampling parameter that controls the
probability distribution of token selection. Higher temperatures (e.g.,
0.8–1.0) increase randomness and creativity, making outputs more
diverse but potentially less focused. Lower temperatures (e.g., 0.0–0.3)
make outputs more deterministic and predictable.
Question 4
A temperature setting of 0.0 in an LLM will typically produce:
A) Highly creative and unpredictable responses
B) Random, incoherent text
C) Deterministic, most-likely token outputs
D) Responses limited to a single sentence
Correct ,,,answer,,,: C
Rationale: At temperature 0.0, the model always selects the token with
the highest probability at each step, producing deterministic outputs. The
same prompt will generate the same (or very similar) response each
time, making this setting ideal for tasks requiring consistency and factual
accuracy.
, Question 5
What is 'zero-shot prompting'?
A) Providing the model with many examples before asking a question
B) Asking the model to perform a task without providing any examples
C) Using a model that has not been fine-tuned on any data
D) Querying the model with an empty prompt
Correct ,,,answer,,,: B
Rationale: Zero-shot prompting relies entirely on the model's pre-
trained knowledge. The user provides instructions without any example
inputs or outputs. This technique works well for common, well-
understood tasks but may struggle with niche domains or specific
formatting requirements.
Question 6
What is 'few-shot prompting'?
A) Providing several examples of input-output pairs in the prompt
before asking the model to complete a new task
B) Fine-tuning a model on a small dataset
C) Using a reduced model size to speed up inference
D) Limiting the model response to a few words
Correct ,,,answer,,,: A
Rationale: Few-shot prompting includes 2–5 examples of the desired
task within the prompt. These examples "teach" the model the expected
pattern, format, or reasoning approach without requiring model
Update) Practical Applications of
Prompt Engineering Review | Questions
& Answers | Grade A| 100% Correct
(Verified Solutions)
SECTION 1: PROMPT ENGINEERING FUNDAMENTALS
(Questions 1-25)
Question 1
What is prompt engineering?
A) A software development methodology for building AI systems from
scratch
B) The practice of designing and refining inputs to AI language models
to produce desired outputs
C) A hardware optimization technique for running large language
models faster
D) A method of training neural networks using reinforcement learning
Correct ,,,answer,,,: B
,Rationale: Prompt engineering is the disciplined practice of crafting,
refining, and optimizing input prompts to guide AI language models
toward generating specific, accurate, and useful outputs. It involves
understanding how models interpret language and strategically
structuring instructions to achieve desired results.
Question 2
Which of the following best describes a 'prompt' in the context of large
language models (LLMs)?
A) The GPU memory allocated to a model during inference
B) A hyperparameter used during model training
C) The input text or instruction provided to a language model to guide
its response
D) The tokenization scheme used to encode model outputs
Correct ,,,answer,,,: C
Rationale: A prompt is the user-provided input—whether a question,
instruction, or statement—that serves as the starting point for an LLM's
response generation. The quality and structure of the prompt directly
influence the relevance and accuracy of the model's output.
Question 3
What does the term 'temperature' control in a language model's output?
A) The maximum number of tokens in the response
B) The randomness or creativity of the model's output
,C) The speed at which the model generates tokens
D) The number of layers processed during inference
Correct ,,,answer,,,: B
Rationale: Temperature is a sampling parameter that controls the
probability distribution of token selection. Higher temperatures (e.g.,
0.8–1.0) increase randomness and creativity, making outputs more
diverse but potentially less focused. Lower temperatures (e.g., 0.0–0.3)
make outputs more deterministic and predictable.
Question 4
A temperature setting of 0.0 in an LLM will typically produce:
A) Highly creative and unpredictable responses
B) Random, incoherent text
C) Deterministic, most-likely token outputs
D) Responses limited to a single sentence
Correct ,,,answer,,,: C
Rationale: At temperature 0.0, the model always selects the token with
the highest probability at each step, producing deterministic outputs. The
same prompt will generate the same (or very similar) response each
time, making this setting ideal for tasks requiring consistency and factual
accuracy.
, Question 5
What is 'zero-shot prompting'?
A) Providing the model with many examples before asking a question
B) Asking the model to perform a task without providing any examples
C) Using a model that has not been fine-tuned on any data
D) Querying the model with an empty prompt
Correct ,,,answer,,,: B
Rationale: Zero-shot prompting relies entirely on the model's pre-
trained knowledge. The user provides instructions without any example
inputs or outputs. This technique works well for common, well-
understood tasks but may struggle with niche domains or specific
formatting requirements.
Question 6
What is 'few-shot prompting'?
A) Providing several examples of input-output pairs in the prompt
before asking the model to complete a new task
B) Fine-tuning a model on a small dataset
C) Using a reduced model size to speed up inference
D) Limiting the model response to a few words
Correct ,,,answer,,,: A
Rationale: Few-shot prompting includes 2–5 examples of the desired
task within the prompt. These examples "teach" the model the expected
pattern, format, or reasoning approach without requiring model
