DY0-001 Exam Dumps : CompTIA DataX Exam

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.”

→ Non-technical business stakeholders value outcome-oriented visuals (charts, dashboards) and the purpose/justification for the modeling work. These artifacts directly communicate impact without overwhelming technical complexity.

Why the other options are incorrect:

C & D: Too technical for a non-technical audience.

E: Useful, but may be too detailed depending on the level of abstraction desired.

F: Data dictionary is better suited for technical handoff — not executive review.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 5.5:“Business-oriented presentations should emphasize clear visualizations, insights, and executive summaries of model goals.”

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→ Euclidean distance is the most intuitive distance metric. It measures the shortest "straight-line" distance between two points in Euclidean space. This is typically used in KNN and clustering when features are continuous and appropriately scaled.

Why the other options are incorrect:

A: "Radial" isn’t a standard distance metric; may refer vaguely to radial basis functions.

C: Cosine measures the angle (orientation) between vectors — not straight-line distance.

D: Manhattan distance sums the absolute differences across dimensions — visualized as block-like (taxicab) paths, not direct lines.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 4.4:“Euclidean distance is the default metric in KNN for measuring straight-line proximity in feature space.”

Data Mining Techniques, Chapter 3:“Euclidean distance represents the shortest path between two points and is widely used in distance-based learning algorithms.”

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