WGU D359 Pre-Assessment (RZO1) (PRZO) | Questions and
Answers | 2026 Update | 100% Correc - 180 Questions and
Answers Already Graded A+ Premium Exam Tested And
Verified
Subject Area WGU D359 Pre-Assessment (RZO1) (PRZO) | Questions and Answers | 2026
Update | 100% Correc
Description Comprehensive examination on WGU D359 Pre-Assessment (RZO1) (PRZO) |
Questions and Answers | 2026 Update | 100% Correc.
Expected Grade A+
Total Questions 180
Duration 3 hours
Learning Outcomes 1. Demonstrate mastery of core concepts
Accreditation Aligned with US university standards.
Page 1
,1. A researcher is comparing the performance of two machine learning models on a
binary classification task with a highly imbalanced dataset. Model A achieves an
accuracy of 95% but a precision of 0.3 and recall of 0.9. Model B achieves an
accuracy of 92% but a precision of 0.8 and recall of 0.4. Which model is more
appropriate for a scenario where false positives are extremely costly and the
minority class is of primary interest?
A. Model A, because higher recall ensures capturing most minority class instances.
B. Model B, because higher precision minimizes false positives, which is critical when false
positive cost is high.
C. Model A, because accuracy is higher and overall performance is better.
D. Model B, because recall of 0.4 is acceptable if precision is high.
Answer: B. Model B, because higher precision minimizes false positives, which is
critical when false positive cost is high.
In imbalanced datasets, accuracy is misleading. When false positives are costly,
precision is the key metric. Model B has higher precision (0.8 vs 0.3), meaning fewer
false positives, which aligns with the requirement. While recall is lower, the cost of false
positives outweighs the benefit of higher recall in this scenario.
Page 2
,2. In a supply chain network with multiple suppliers and a single manufacturer, the
manufacturer is considering a dual-sourcing strategy to mitigate disruption risk.
Supplier A has a unit cost of $10 and a disruption probability of 0.05 per period.
Supplier B has a unit cost of $12 and a disruption probability of 0.02. The
manufacturer needs 1000 units per period. If the manufacturer sources 600 units
from A and 400 from B, what is the expected total cost per period, assuming
disruptions are independent and cause complete loss of supply from the affected
supplier?
A. $10,800
B. $11,000
C. $11,200
D. $11,600
Answer: A. $10,800
Expected cost = (600*10) + (400*12) = 6000 + 4800 = 10,800. Disruptions affect
availability but not cost if supply is delivered; however, if a supplier is disrupted, the
manufacturer must source from the other at its cost, but the question asks for expected
cost assuming no disruption or that cost is incurred regardless? Actually, the expected
cost is simply the sum of the costs of ordered quantities, as disruptions do not change
the cost paid for units ordered (they may not be delivered, but the cost is still incurred?
Typically, in such problems, cost is incurred only for units received. But here, it says
"expected total cost per period" and disruptions cause "complete loss of supply",
implying the manufacturer pays only for units actually received? The question is
ambiguous. However, typical interpretation: the manufacturer pays for the ordered
quantity regardless? Or only for received? Given the numbers, the straightforward
calculation yields 10,800. Option A is correct if we assume cost is based on order
quantity. Option D (11,600) would be if we consider expected lost units and emergency
sourcing. But the simplest answer is A.
Page 3
, 3. Which of the following best describes the concept of "ontological commitment" in
the context of knowledge representation?
A. The agreement among agents to use a common vocabulary for communication.
B. The set of assumptions about the nature of reality that underpin a knowledge base.
C. The computational cost of maintaining a knowledge base with many axioms.
D. The process of mapping natural language to formal logic.
Answer: B. The set of assumptions about the nature of reality that underpin a
knowledge base.
Ontological commitment refers to the choices made about what exists in a domain and
how entities are categorized. It determines the conceptual framework that a knowledge
representation system uses to model the world. Option A is about communication
protocols, C is about performance, D is about natural language processing.
4. A data scientist is tuning a gradient boosting model and observes that the training
loss decreases steadily but the validation loss starts increasing after a certain number
of iterations. Which of the following is the most appropriate action to address this
issue?
A. Increase the learning rate to accelerate convergence.
B. Increase the maximum depth of trees to capture more complex patterns.
C. Implement early stopping based on validation loss.
D. Increase the number of estimators to allow more iterations.
Answer: C. Implement early stopping based on validation loss.
The described pattern is classic overfitting: training loss improves while validation loss
worsens. Early stopping halts training when validation loss no longer improves,
preventing overfitting. Increasing learning rate or depth would likely exacerbate
overfitting, and increasing estimators would continue to overfit.
Page 4
Answers | 2026 Update | 100% Correc - 180 Questions and
Answers Already Graded A+ Premium Exam Tested And
Verified
Subject Area WGU D359 Pre-Assessment (RZO1) (PRZO) | Questions and Answers | 2026
Update | 100% Correc
Description Comprehensive examination on WGU D359 Pre-Assessment (RZO1) (PRZO) |
Questions and Answers | 2026 Update | 100% Correc.
Expected Grade A+
Total Questions 180
Duration 3 hours
Learning Outcomes 1. Demonstrate mastery of core concepts
Accreditation Aligned with US university standards.
Page 1
,1. A researcher is comparing the performance of two machine learning models on a
binary classification task with a highly imbalanced dataset. Model A achieves an
accuracy of 95% but a precision of 0.3 and recall of 0.9. Model B achieves an
accuracy of 92% but a precision of 0.8 and recall of 0.4. Which model is more
appropriate for a scenario where false positives are extremely costly and the
minority class is of primary interest?
A. Model A, because higher recall ensures capturing most minority class instances.
B. Model B, because higher precision minimizes false positives, which is critical when false
positive cost is high.
C. Model A, because accuracy is higher and overall performance is better.
D. Model B, because recall of 0.4 is acceptable if precision is high.
Answer: B. Model B, because higher precision minimizes false positives, which is
critical when false positive cost is high.
In imbalanced datasets, accuracy is misleading. When false positives are costly,
precision is the key metric. Model B has higher precision (0.8 vs 0.3), meaning fewer
false positives, which aligns with the requirement. While recall is lower, the cost of false
positives outweighs the benefit of higher recall in this scenario.
Page 2
,2. In a supply chain network with multiple suppliers and a single manufacturer, the
manufacturer is considering a dual-sourcing strategy to mitigate disruption risk.
Supplier A has a unit cost of $10 and a disruption probability of 0.05 per period.
Supplier B has a unit cost of $12 and a disruption probability of 0.02. The
manufacturer needs 1000 units per period. If the manufacturer sources 600 units
from A and 400 from B, what is the expected total cost per period, assuming
disruptions are independent and cause complete loss of supply from the affected
supplier?
A. $10,800
B. $11,000
C. $11,200
D. $11,600
Answer: A. $10,800
Expected cost = (600*10) + (400*12) = 6000 + 4800 = 10,800. Disruptions affect
availability but not cost if supply is delivered; however, if a supplier is disrupted, the
manufacturer must source from the other at its cost, but the question asks for expected
cost assuming no disruption or that cost is incurred regardless? Actually, the expected
cost is simply the sum of the costs of ordered quantities, as disruptions do not change
the cost paid for units ordered (they may not be delivered, but the cost is still incurred?
Typically, in such problems, cost is incurred only for units received. But here, it says
"expected total cost per period" and disruptions cause "complete loss of supply",
implying the manufacturer pays only for units actually received? The question is
ambiguous. However, typical interpretation: the manufacturer pays for the ordered
quantity regardless? Or only for received? Given the numbers, the straightforward
calculation yields 10,800. Option A is correct if we assume cost is based on order
quantity. Option D (11,600) would be if we consider expected lost units and emergency
sourcing. But the simplest answer is A.
Page 3
, 3. Which of the following best describes the concept of "ontological commitment" in
the context of knowledge representation?
A. The agreement among agents to use a common vocabulary for communication.
B. The set of assumptions about the nature of reality that underpin a knowledge base.
C. The computational cost of maintaining a knowledge base with many axioms.
D. The process of mapping natural language to formal logic.
Answer: B. The set of assumptions about the nature of reality that underpin a
knowledge base.
Ontological commitment refers to the choices made about what exists in a domain and
how entities are categorized. It determines the conceptual framework that a knowledge
representation system uses to model the world. Option A is about communication
protocols, C is about performance, D is about natural language processing.
4. A data scientist is tuning a gradient boosting model and observes that the training
loss decreases steadily but the validation loss starts increasing after a certain number
of iterations. Which of the following is the most appropriate action to address this
issue?
A. Increase the learning rate to accelerate convergence.
B. Increase the maximum depth of trees to capture more complex patterns.
C. Implement early stopping based on validation loss.
D. Increase the number of estimators to allow more iterations.
Answer: C. Implement early stopping based on validation loss.
The described pattern is classic overfitting: training loss improves while validation loss
worsens. Early stopping halts training when validation loss no longer improves,
preventing overfitting. Increasing learning rate or depth would likely exacerbate
overfitting, and increasing estimators would continue to overfit.
Page 4
