,Exam Blueprint & Instructions
WGU D600 Task 3: Principal Component Analysis (PCA) | Complete Regression Report, Python Modeling & Results
Part I · Exam Questions (1-200)
1. PCA Fundamentals & Dimensionality Reduction (22 questions) .......4
2. Data Preprocessing, Scaling & Assumptions (22 questions) . . . . . . . 11
3. Python Implementation: sklearn.decomposition.PCA (22 questions) . . . . . . . 18
4. Eigenvalues, Explained Variance & Scree Plots (22 questions) . . . . . . . 26
5. Regression Modeling After PCA & Multicollinearity (22 questions) . . . . . . . 33
6. Model Evaluation, Cross-Validation & Metrics (22 questions) . . . . . . . 40
7. WGU D600 Task Requirements, Report Structure & Documentation (22 questions) . . . . . . . 48
8. Visualization: Biplots, Loadings & Score Plots (23 questions) . . . . . . . 55
9. Advanced Topics, Limitations & Business Implications (23 questions) . . . . . . . 63
Instructions:
• This practice test contains 200 original multiple-choice questions designed for WGU D600 Task 3 mastery.
• Covers PCA fundamentals, Python implementation (sklearn), regression modeling, report documentation per WGU rubric.
• Pace yourself: 2 minutes per question recommended. Read the last line of the question first.
• All questions are original and not from actual WGU assessment. Use for study and concept reinforcement.
• Correct answers and detailed rationales follow each item immediately for active learning.
,SECTION ONE: QUESTIONS 1-200
Choose the single best answer. Correct answers and rationales follow each item.
PCA Fundamentals & Dimensionality Reduction
Question 1. In the context of WGU D600 Task 3, what is the primary purpose of using cross-
validation to assess stability of PCA-regression pipeline in pca fundamentals &
dimensionality reduction scenario 1-6618?
A. Fit PCA on entire dataset including test data and report optimistic performance
B. Correct principle for using cross-validation to assess stability of PCA-regression is to
maximize variance retention while ensuring reproducibility and WGU documentation
C. Use raw unscaled data directly in PCA without checking variance or outliers
D. Ignore WGU D600 report requirements and omit code, visualizations, and assumptions
documentation
CORRECT ANSWER: B. Correct principle for using cross-validation to assess stability of PCA-
regression is to maximize variance retention while ensuring reproducibility and WGU
documentation
RATIONALE: For WGU D600 Task 3, using cross-validation to assess stability of PCA-regression
pipeline in pca fundamentals & dimensionality reduction scenario 1-6618 requires careful
implementation. The correct approach (B) ensures methodological soundness: Correct principle for
using cross-validation to assess stability of PCA-regression is to maximize variance retention while
ensuring reproducibility and WGU documentation. This aligns with PCA theory (orthogonal
transformation maximizing variance) and WGU rubric expectations for Python modeling, regression
report completeness, reproducibility, and ethical business reporting. Incorrect options introduce
data leakage, scaling errors, or insufficient documentation, which would fail Task 3 evaluation.
Question 2. When implementing interpreting p-values and confidence intervals after
dimensionality reduction in pca fundamentals & dimensionality reduction scenario 2-5824 in
Python, which approach is most appropriate?
A. Select arbitrary number of components without explained variance analysis or validation
B. Use raw unscaled data directly in PCA without checking variance or outliers
C. Fit PCA on entire dataset including test data and report optimistic performance
D. Correct principle for interpreting p-values and confidence intervals after dimensi is to
maximize variance retention while ensuring reproducibility and WGU documentation
CORRECT ANSWER: D. Correct principle for interpreting p-values and confidence intervals
after dimensi is to maximize variance retention while ensuring reproducibility and WGU
documentation
RATIONALE: For WGU D600 Task 3, interpreting p-values and confidence intervals after dimensionality
reduction in pca fundamentals & dimensionality reduction scenario 2-5824 requires careful
implementation. The correct approach (D) ensures methodological soundness: Correct principle for
interpreting p-values and confidence intervals after dimensi is to maximize variance retention while
ensuring reproducibility and WGU documentation. This aligns with PCA theory (orthogonal
transformation maximizing variance) and WGU rubric expectations for Python modeling, regression
report completeness, reproducibility, and ethical business reporting. Incorrect options introduce
data leakage, scaling errors, or insufficient documentation, which would fail Task 3 evaluation.
, WGU D600 Task 3 - PCA Fundamentals & Dimensionality Reduction
Question 3. A student analyzes handling the difference between fit(), transform(), and
fit_transform() in PCA workflow in pca fundamentals & dimensionality reduction scenario
3-5724 and observes unexpected results. What is the most likely cause?
A. Ignore WGU D600 report requirements and omit code, visualizations, and assumptions
documentation
B. Fit PCA only on X_train to prevent data leakage, then transform X_train and X_test
separately
C. Use raw unscaled data directly in PCA without checking variance or outliers
D. Fit PCA on entire dataset including test data and report optimistic performance
CORRECT ANSWER: B. Fit PCA only on X_train to prevent data leakage, then transform X_train
and X_test separately
RATIONALE: For WGU D600 Task 3, handling the difference between fit(), transform(), and
fit_transform() in PCA workflow in pca fundamentals & dimensionality reduction scenario 3-5724
requires careful implementation. The correct approach (B) ensures methodological soundness: Fit PCA
only on X_train to prevent data leakage, then transform X_train and X_test separately. This aligns
with PCA theory (orthogonal transformation maximizing variance) and WGU rubric expectations for
Python modeling, regression report completeness, reproducibility, and ethical business reporting.
Incorrect options introduce data leakage, scaling errors, or insufficient documentation, which would
fail Task 3 evaluation.
Question 4. Which Python code snippet correctly implements using explained_variance_ratio_
to select number of components for 95% variance retention in pca fundamentals &
dimensionality reduction scenario 4-8812?
A. Plot cumulative explained variance and scree plot to identify elbow at 90-95% threshold
B. Use raw unscaled data directly in PCA without checking variance or outliers
C. Fit PCA on entire dataset including test data and report optimistic performance
D. Ignore WGU D600 report requirements and omit code, visualizations, and assumptions
documentation
CORRECT ANSWER: A. Plot cumulative explained variance and scree plot to identify elbow at
90-95% threshold
RATIONALE: For WGU D600 Task 3, using explained_variance_ratio_ to select number of components for
95% variance retention in pca fundamentals & dimensionality reduction scenario 4-8812 requires
careful implementation. The correct approach (A) ensures methodological soundness: Plot cumulative
explained variance and scree plot to identify elbow at 90-95% threshold. This aligns with PCA theory
(orthogonal transformation maximizing variance) and WGU rubric expectations for Python modeling,
regression report completeness, reproducibility, and ethical business reporting. Incorrect options
introduce data leakage, scaling errors, or insufficient documentation, which would fail Task 3
evaluation.
Question 5. What is the key advantage of distinguishing between loadings, scores, and
eigenvectors in PCA output in pca fundamentals & dimensionality reduction scenario 5-8936
compared to alternative methods?
A. Ignore WGU D600 report requirements and omit code, visualizations, and assumptions
documentation
B. Fit PCA on entire dataset including test data and report optimistic performance
C. Correct principle for distinguishing between loadings, scores, and eigenvectors in is to
WGU D600 Task 3: Principal Component Analysis (PCA) | Complete Regression Report, Python Modeling & Results
Part I · Exam Questions (1-200)
1. PCA Fundamentals & Dimensionality Reduction (22 questions) .......4
2. Data Preprocessing, Scaling & Assumptions (22 questions) . . . . . . . 11
3. Python Implementation: sklearn.decomposition.PCA (22 questions) . . . . . . . 18
4. Eigenvalues, Explained Variance & Scree Plots (22 questions) . . . . . . . 26
5. Regression Modeling After PCA & Multicollinearity (22 questions) . . . . . . . 33
6. Model Evaluation, Cross-Validation & Metrics (22 questions) . . . . . . . 40
7. WGU D600 Task Requirements, Report Structure & Documentation (22 questions) . . . . . . . 48
8. Visualization: Biplots, Loadings & Score Plots (23 questions) . . . . . . . 55
9. Advanced Topics, Limitations & Business Implications (23 questions) . . . . . . . 63
Instructions:
• This practice test contains 200 original multiple-choice questions designed for WGU D600 Task 3 mastery.
• Covers PCA fundamentals, Python implementation (sklearn), regression modeling, report documentation per WGU rubric.
• Pace yourself: 2 minutes per question recommended. Read the last line of the question first.
• All questions are original and not from actual WGU assessment. Use for study and concept reinforcement.
• Correct answers and detailed rationales follow each item immediately for active learning.
,SECTION ONE: QUESTIONS 1-200
Choose the single best answer. Correct answers and rationales follow each item.
PCA Fundamentals & Dimensionality Reduction
Question 1. In the context of WGU D600 Task 3, what is the primary purpose of using cross-
validation to assess stability of PCA-regression pipeline in pca fundamentals &
dimensionality reduction scenario 1-6618?
A. Fit PCA on entire dataset including test data and report optimistic performance
B. Correct principle for using cross-validation to assess stability of PCA-regression is to
maximize variance retention while ensuring reproducibility and WGU documentation
C. Use raw unscaled data directly in PCA without checking variance or outliers
D. Ignore WGU D600 report requirements and omit code, visualizations, and assumptions
documentation
CORRECT ANSWER: B. Correct principle for using cross-validation to assess stability of PCA-
regression is to maximize variance retention while ensuring reproducibility and WGU
documentation
RATIONALE: For WGU D600 Task 3, using cross-validation to assess stability of PCA-regression
pipeline in pca fundamentals & dimensionality reduction scenario 1-6618 requires careful
implementation. The correct approach (B) ensures methodological soundness: Correct principle for
using cross-validation to assess stability of PCA-regression is to maximize variance retention while
ensuring reproducibility and WGU documentation. This aligns with PCA theory (orthogonal
transformation maximizing variance) and WGU rubric expectations for Python modeling, regression
report completeness, reproducibility, and ethical business reporting. Incorrect options introduce
data leakage, scaling errors, or insufficient documentation, which would fail Task 3 evaluation.
Question 2. When implementing interpreting p-values and confidence intervals after
dimensionality reduction in pca fundamentals & dimensionality reduction scenario 2-5824 in
Python, which approach is most appropriate?
A. Select arbitrary number of components without explained variance analysis or validation
B. Use raw unscaled data directly in PCA without checking variance or outliers
C. Fit PCA on entire dataset including test data and report optimistic performance
D. Correct principle for interpreting p-values and confidence intervals after dimensi is to
maximize variance retention while ensuring reproducibility and WGU documentation
CORRECT ANSWER: D. Correct principle for interpreting p-values and confidence intervals
after dimensi is to maximize variance retention while ensuring reproducibility and WGU
documentation
RATIONALE: For WGU D600 Task 3, interpreting p-values and confidence intervals after dimensionality
reduction in pca fundamentals & dimensionality reduction scenario 2-5824 requires careful
implementation. The correct approach (D) ensures methodological soundness: Correct principle for
interpreting p-values and confidence intervals after dimensi is to maximize variance retention while
ensuring reproducibility and WGU documentation. This aligns with PCA theory (orthogonal
transformation maximizing variance) and WGU rubric expectations for Python modeling, regression
report completeness, reproducibility, and ethical business reporting. Incorrect options introduce
data leakage, scaling errors, or insufficient documentation, which would fail Task 3 evaluation.
, WGU D600 Task 3 - PCA Fundamentals & Dimensionality Reduction
Question 3. A student analyzes handling the difference between fit(), transform(), and
fit_transform() in PCA workflow in pca fundamentals & dimensionality reduction scenario
3-5724 and observes unexpected results. What is the most likely cause?
A. Ignore WGU D600 report requirements and omit code, visualizations, and assumptions
documentation
B. Fit PCA only on X_train to prevent data leakage, then transform X_train and X_test
separately
C. Use raw unscaled data directly in PCA without checking variance or outliers
D. Fit PCA on entire dataset including test data and report optimistic performance
CORRECT ANSWER: B. Fit PCA only on X_train to prevent data leakage, then transform X_train
and X_test separately
RATIONALE: For WGU D600 Task 3, handling the difference between fit(), transform(), and
fit_transform() in PCA workflow in pca fundamentals & dimensionality reduction scenario 3-5724
requires careful implementation. The correct approach (B) ensures methodological soundness: Fit PCA
only on X_train to prevent data leakage, then transform X_train and X_test separately. This aligns
with PCA theory (orthogonal transformation maximizing variance) and WGU rubric expectations for
Python modeling, regression report completeness, reproducibility, and ethical business reporting.
Incorrect options introduce data leakage, scaling errors, or insufficient documentation, which would
fail Task 3 evaluation.
Question 4. Which Python code snippet correctly implements using explained_variance_ratio_
to select number of components for 95% variance retention in pca fundamentals &
dimensionality reduction scenario 4-8812?
A. Plot cumulative explained variance and scree plot to identify elbow at 90-95% threshold
B. Use raw unscaled data directly in PCA without checking variance or outliers
C. Fit PCA on entire dataset including test data and report optimistic performance
D. Ignore WGU D600 report requirements and omit code, visualizations, and assumptions
documentation
CORRECT ANSWER: A. Plot cumulative explained variance and scree plot to identify elbow at
90-95% threshold
RATIONALE: For WGU D600 Task 3, using explained_variance_ratio_ to select number of components for
95% variance retention in pca fundamentals & dimensionality reduction scenario 4-8812 requires
careful implementation. The correct approach (A) ensures methodological soundness: Plot cumulative
explained variance and scree plot to identify elbow at 90-95% threshold. This aligns with PCA theory
(orthogonal transformation maximizing variance) and WGU rubric expectations for Python modeling,
regression report completeness, reproducibility, and ethical business reporting. Incorrect options
introduce data leakage, scaling errors, or insufficient documentation, which would fail Task 3
evaluation.
Question 5. What is the key advantage of distinguishing between loadings, scores, and
eigenvectors in PCA output in pca fundamentals & dimensionality reduction scenario 5-8936
compared to alternative methods?
A. Ignore WGU D600 report requirements and omit code, visualizations, and assumptions
documentation
B. Fit PCA on entire dataset including test data and report optimistic performance
C. Correct principle for distinguishing between loadings, scores, and eigenvectors in is to
