WGU D685 OBJECTIVE ASSESSMENT 2 Practical Applications of Prompt Engineering 200 Practice

WGU D685 OBJECTIVE ASSESSMENT 2 Practical Applications of Prompt
Engineering 200 Practice,..

WGU D685 Objective Assessment 2: Practical Applications of Prompt Engineering

210 Practice Questions with Answers, Rationales, and Multiple Choice

DOMAIN 1: FOUNDATIONS OF PROMPT ENGINEERING (Questions 1-30)

Question 1

What is the primary goal of prompt engineering in generative AI?



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



Rationale: Prompt engineering focuses on designing inputs that guide a
generative AI model to produce accurate, relevant, and useful outputs without
modifying the model's weights or architecture. It is not about prompt length (A),
parameter reduction (C), or model training (D).

,Question 2

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



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 (A), hardware evaluation
(C), or deployment coding (D).



Question 3

What does the "context window" in a large language model refer to?



A) The number of training parameters in the model

B) The maximum number of tokens the model can process in a single input

,C) The number of layers in the neural network

D) The size of the model's vocabulary



Correct Answer: B



Rationale: The context window is the maximum number of tokens (words or
subwords) that a language model can consider in a single prompt. Exceeding this
limit results in truncation or errors. Understanding context window limitations is
crucial for effective prompt engineering.



Question 4

Which statement about prompt engineering is TRUE?



A) Prompt engineering requires retraining the model on new data

B) Prompt engineering only works with commercial models like ChatGPT

C) Prompt engineering modifies model behavior through input design without
changing model weights

D) Prompt engineering is a one-time process that does not require iteration



Correct Answer: C

, Rationale: Prompt engineering is a lightweight approach that guides model
outputs through careful input design. It does not require model retraining, is
applicable across various models, and is an iterative process that involves testing
and refinement.



Question 5

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



Rationale: Zero-shot learning occurs when a model performs a task without any
examples or additional training, relying solely on its pre-trained knowledge. Fine-
tuning (B) and RLHF (C) involve additional training. Pre-training (D) is the initial
training phase.