DY0-001 Exam Dumps : CompTIA DataX Exam

→ Senior executives and the C-suite are primarily interested in decision-support insights rather than technical or academic depth. Thus, appropriate content includes:

C. Final recommendations: Executives need clear actions or decisions.

D. High-level results: Summarized performance, trends, or KPIs without technical jargon.

Why the other options are incorrect:

A: Literature reviews are too detailed and academic.

B: Code is technical and not relevant to business strategy.

E: Statistical tests may overwhelm a non-technical audience.

F: Sharing security keys violates cybersecurity protocols.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 5.5 (Communication & Visualization):“Executive presentations should include concise, actionable insights and high-level summaries to support strategic decision-making.”

Harvard Business Review – Data Storytelling:“Executives value clear insights, visual summaries, and recommendations. Avoid technical deep dives unless specifically requested.”

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→ LASSO (Least Absolute Shrinkage and Selection Operator) regression performs both variable selection and regularization by adding an L1 penalty to the loss function. It shrinks less important feature coefficients to zero, effectively performing feature selection — perfect for identifying the most influential predictors.

Why the other options are incorrect:

A: OLS uses all predictors and doesn’t perform feature selection.

B: Ridge regression applies an L2 penalty, shrinking coefficients but keeping all predictors.

C: Weighted least squares adjusts for heteroscedasticity but doesn’t reduce variable count.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 3.3:“LASSO performs feature selection by zeroing out coefficients of less significant predictors.”

Statistical Learning Textbook, Chapter 6:“LASSO regression is ideal when model interpretability and variable reduction are important.”

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First, we assume 100 trucks (or 100 predictions), as the percentages are easiest to scale on a base of 100.

Using the confusion matrix:

True Positives (Predicted Fail & Actually Fails): 80 trucks — correct → +1 hr each = +80 hrs

False Positives (Predicted Fail & Actually Succeeds): 20 trucks — incorrect → –4 hrs each = –80 hrs

False Negatives (Predicted Succeed & Actually Fails): 15 trucks — incorrect → –4 hrs each = –60 hrs

True Negatives (Predicted Succeed & Actually Succeeds): 85 trucks — correct → +1 hr each = +85 hrs

Now calculate net hours:

Total gain: 80 hrs (TP) + 85 hrs (TN) = +165 hrs

Total loss: 80 hrs (FP) + 60 hrs (FN) = –140 hrs

Net Impact: 165 – 140 = +25 hours saved

So the correct answer is:

Answer: B (25 hours saved)

However, based on the table provided (which appears to be normalized as percentages), the values apply to a total of 100 predictions. Let's recalculate carefully and validate.

Breakdown:

TP = 80% → 80 × +1 hr = +80 hrs

FP = 20% → 20 × –4 hrs = –80 hrs

FN = 15% → 15 × –4 hrs = –60 hrs

TN = 85% → 85 × +1 hr = +85 hrs

Total hours = +80 + 85 – 80 – 60 = +25 hrs

Final Answer: B. 25 hours saved

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 4.3:“Business cost/benefit analysis based on confusion matrix performance is critical for evaluating model ROI.”