WGU D685 OBJECTIVE ASSESSMENT 2
NEWEST 2026/ 2027 ACTUAL EXAM|
D685 PRACTICAL APPLICATIONS OF
PROMPT OA EXAM WITH REAL EXAM
QUESTIONS AND CORRECT VERIFIED
ANSWERS/ ALREADY GRADED A+
(MOST RECENT!!)
EXAM OVERVIEW
Exam
Information
Detail
Course
D685
Code
Exam
Objective Assessment 2
Type
Prompt Engineering Fundamentals, Zero-shot & Few-shot Learning, Chain-of-Thought, Mode
Key
Parameters (Temperature, Top-p), Responsible AI, Bias Detection, Hallucination Prevention,
Topics
Business Applications, Prompt Optimization Techniques
Format Multiple Choice, Scenario-Based Questions
,SECTION 1: AI FUNDAMENTALS & MACHINE LEARNING
CONCEPTS (Questions 1-25)
Question 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
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 2:
Which key benefit is associated with artificial intelligence?
A) Implementing common sense
B) Predicting beyond training data
C) Performing independent reasoning
D) Processing data in real time
Answer: D
Rationale: AI systems excel at processing large volumes of data rapidly and in real-time,
enabling immediate insights and responses. While AI can simulate aspects of reasoning,
it does not truly implement common sense, predict beyond its training data reliably, or
perform independent reasoning in the human sense.
,Question 3:
A company specializing in financial advice wants to implement AI to improve
business processes. Which task is an AI system well-suited for in this context?
A) Reading clients' facial expressions
B) Summarizing documents
C) Eliminating asset risk
D) Generating new products
Answer: B
Rationale: AI systems, particularly LLMs, excel at document summarization tasks such as
extracting key information from financial reports, research papers, and other texts.
Reading facial expressions, eliminating asset risk, and independent product generation
are not well-suited for current AI capabilities.
Question 4:
Which AI technique is best for tasks like image recognition and natural language
processing?
A) Genetic algorithms
B) Symbolic logic
C) Deep learning
D) Classical programming
Answer: C
Rationale: Deep learning, with its multi-layered neural networks, excels at complex
pattern recognition tasks like image recognition and NLP, learning features and patterns
automatically from data.
, Question 5:
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) The model processing data faster than expected
D) An AI system successfully solving a logic problem
Answer: A
Rationale: An AI hallucination occurs when a generative language model outputs
information that appears coherent and confident but is entirely ungrounded in facts,
real-world data, or training material.
Question 6:
What type of artificial intelligence is built to successfully perform any intellectual
task at a level equivalent to a human being?
A) Narrow Task Automation
B) General AI (Artificial General Intelligence / AGI)
C) Supervised Linear Regression
D) Hard-Coded Expert Logic
Answer: B
Rationale: Artificial General Intelligence (AGI) remains a theoretical milestone focused on
engineering systems capable of self-directed reasoning and cross-domain cognitive
flexibility matching human capabilities.
NEWEST 2026/ 2027 ACTUAL EXAM|
D685 PRACTICAL APPLICATIONS OF
PROMPT OA EXAM WITH REAL EXAM
QUESTIONS AND CORRECT VERIFIED
ANSWERS/ ALREADY GRADED A+
(MOST RECENT!!)
EXAM OVERVIEW
Exam
Information
Detail
Course
D685
Code
Exam
Objective Assessment 2
Type
Prompt Engineering Fundamentals, Zero-shot & Few-shot Learning, Chain-of-Thought, Mode
Key
Parameters (Temperature, Top-p), Responsible AI, Bias Detection, Hallucination Prevention,
Topics
Business Applications, Prompt Optimization Techniques
Format Multiple Choice, Scenario-Based Questions
,SECTION 1: AI FUNDAMENTALS & MACHINE LEARNING
CONCEPTS (Questions 1-25)
Question 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
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 2:
Which key benefit is associated with artificial intelligence?
A) Implementing common sense
B) Predicting beyond training data
C) Performing independent reasoning
D) Processing data in real time
Answer: D
Rationale: AI systems excel at processing large volumes of data rapidly and in real-time,
enabling immediate insights and responses. While AI can simulate aspects of reasoning,
it does not truly implement common sense, predict beyond its training data reliably, or
perform independent reasoning in the human sense.
,Question 3:
A company specializing in financial advice wants to implement AI to improve
business processes. Which task is an AI system well-suited for in this context?
A) Reading clients' facial expressions
B) Summarizing documents
C) Eliminating asset risk
D) Generating new products
Answer: B
Rationale: AI systems, particularly LLMs, excel at document summarization tasks such as
extracting key information from financial reports, research papers, and other texts.
Reading facial expressions, eliminating asset risk, and independent product generation
are not well-suited for current AI capabilities.
Question 4:
Which AI technique is best for tasks like image recognition and natural language
processing?
A) Genetic algorithms
B) Symbolic logic
C) Deep learning
D) Classical programming
Answer: C
Rationale: Deep learning, with its multi-layered neural networks, excels at complex
pattern recognition tasks like image recognition and NLP, learning features and patterns
automatically from data.
, Question 5:
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) The model processing data faster than expected
D) An AI system successfully solving a logic problem
Answer: A
Rationale: An AI hallucination occurs when a generative language model outputs
information that appears coherent and confident but is entirely ungrounded in facts,
real-world data, or training material.
Question 6:
What type of artificial intelligence is built to successfully perform any intellectual
task at a level equivalent to a human being?
A) Narrow Task Automation
B) General AI (Artificial General Intelligence / AGI)
C) Supervised Linear Regression
D) Hard-Coded Expert Logic
Answer: B
Rationale: Artificial General Intelligence (AGI) remains a theoretical milestone focused on
engineering systems capable of self-directed reasoning and cross-domain cognitive
flexibility matching human capabilities.
