DY0-001測試引擎 - DY0-001考試大綱
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最新的 CompTIA Data+ DY0-001 免費考試真題 (Q63-Q68):
問題 #63
A data scientist has built a model that provides the likelihood of an error occurring in a factory. The historical accuracy of the model is 90%. At a specific factory, the model is reporting a likelihood score of 0.90. Which of the following explains a confidence score of 0.90?
- A. Running this model 100 times on a factory, it is expected the model will predict 90 out of 100 factory errors.
- B. Running this model on 100 samples of factories, a certain model performance is expected for 90 out of the 100 samples.
- C. Running this model for all known factory issues, it is expected the model will identify 90 out of 100 known factory issues.
- D. Running this model 100 times within a factory it is expected the model will predict error 90 out of 100 times the model is ran.
答案:D
解題說明:
A confidence score of 0.90 is a probabilistic estimate, interpreted as the model assigning a 90% chance of an error on that particular factory instance, which in the long run corresponds to predicting "error" in about 90 out of every 100 identical runs.
問題 #64
A data scientist uses a large data set to build multiple linear regression models to predict the likely market value of a real estate property. The selected new model has an RMSE of 995 on the holdout set and an adjusted R2 of .75. The benchmark model has an RMSE of 1,000 on the holdout set. Which of the following is the best business statement regarding the new model?
- A. The model's adjusted R2 is too low for the real estate industry.
- B. The model should be deployed because it has a lower RMSE.
- C. The model's adjusted R2 is exceptionally strong for such a complex relationship.
- D. The model fails to improve meaningfully on the benchmark model.
答案:D
解題說明:
Although the new model's RMSE is technically lower (995 vs. 1,000), the five‐point improvement on holdout data is negligible in most real-estate contexts and unlikely to produce meaningful business value over the existing benchmark.
問題 #65
A data scientist is performing a linear regression and wants to construct a model that explains the most variation in the data. Which of the following should the data scientist maximize when evaluating the regression performance metrics?
- A. R²
- B. p value
- C. Accuracy
- D. AUC
答案:A
解題說明:
# R² (coefficient of determination) quantifies how much of the variance in the dependent variable is explained by the model. A higher R² means a better fit to the data, making it the metric to maximize for explanatory power in regression analysis.
Why the other options are incorrect:
* A: Accuracy is used in classification, not regression.
* C: p-values test statistical significance of coefficients, not overall model fit.
* D: AUC (Area Under the Curve) applies to classification models, not regression.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 3.2:"R² is a regression performance metric indicating the proportion of variance explained by the independent variables."
問題 #66
Which of the following distribution methods or models can most effectively represent the actual arrival times of a bus that runs on an hourly schedule?
- A. Binomial
- B. Normal
- C. Poisson
- D. Exponential
答案:B
解題說明:
Scheduled buses tend to arrive around a fixed time with random delays that cluster symmetrically around the hour. A normal distribution effectively models those continuous, bell-shaped deviations from the exact schedule.
問題 #67
An analyst is examining data from an array of temperature sensors and sees that one sensor consistently returns values that are much higher than the values from the other sensors. Which of the following terms best describes this type of error?
- A. Synthetic
- B. Systematic
- C. Idiosyncratic
- D. Heteroskedastic
答案:B
解題說明:
A sensor that consistently reads higher than the others exhibits a repeatable bias, which is characteristic of a systematic error.
問題 #68
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