DASCA Updated SDS Exam Questions and Answers by fred

In hypothesis testing, two main types of errors are defined:

Type I Error (Option A): Occurs when the null hypothesis (H₀) is true, but we incorrectly reject it. This is known as a false positive. Example: Concluding a drug is effective when it is not.

Type II Error (Option B): Occurs when the null hypothesis (H₀) is false, but we fail to reject it. This is a false negative. Example: Concluding a drug has no effect when it actually does.

Logical Error / Hypothesis Error (Options C and D): Not standard terms in statistical hypothesis testing.

Thus, the “wrong negation of a true null hypothesis” refers to a Type I Error (false positive).

The monetization exercise is a critical phase in Big Data Business Model Maturity where organizations turn insights into new revenue opportunities.

Option A: Correct. Monetization often uncovers new product or service opportunities by leveraging analytics.

Option B: Correct. The first step is analyzing product usage patterns and customer behaviors to identify hidden value.

Option C: Correct. Insights help in building secondary recommendations or complementary products/services (cross-sell, upsell opportunities).

Thus, all three statements are valid, making the correct answer Option E (All of the above).

ARIMA stands for AutoRegressive Integrated Moving Average, one of the most widely used models for time series forecasting.

AutoRegressive (AR): Model uses past values of the variable to predict future values.

Integrated (I): Differencing is applied to make the time series stationary.

Moving Average (MA): Model incorporates past forecast errors into predictions.

Option B: Correct — autoregressive + moving average is part of ARIMA’s name.

Options A, C, D: Incorrect because these terms are not recognized statistical modeling frameworks.

Option E: Incorrect, since only B is valid.

Thus, the correct answer is Option B.

Normal Curve Equivalent (NCE) scores are standardized scores designed to:

Range between 1 and 99.

Be comparable to percentile ranks but with the advantage of equal-interval properties like Z-scores.

This makes NCE scores useful in educational assessments, survey analysis, and statistical modeling.

Option A (Trend analysis): Incorrect. Not related to score standardization.

Option B (Correct): NCE fits the definition perfectly.

Option C (HCE) & D (MCE): Not recognized standard measures in statistics.

Option E: Incorrect, since Option B is valid.

Thus, the correct answer is Option B: Normal Curve Equivalent (NCE).