CompTIA DY0-001 Exam With Confidence Using Practice Dumps

→ The provided equation represents an autoregressive model of order 1 (AR(1)). It describes Xt as a function of its immediately prior value (Xt−1) plus white noise.

Key identifiers:

No differencing (so not ARIMA).

No moving average term (so not ARMA).

No seasonal component (so not SARIMA).

Why the other options are incorrect:

A: ARIMA(1,1,1) includes integration and MA terms, which are absent here.

B: ARMA(1,1) includes both AR and MA terms, but only AR is present.

C: SARIMA involves seasonal and differencing components — not applicable here.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 3.5:“AR(p) models describe a variable as dependent on its previous values with no differencing or moving average.”

Time Series Analysis Textbook, Chapter 4:“Xt = ϕXt-1 + εt describes an AR(1) process when εt is white noise.”

—

→ In real-time systems, low latency (short run time) is critical. While the Artificial Neural Network provides the highest accuracy, its 12-minute runtime makes it unsuitable for real-time inference. Random forest is the fastest but offers the lowest accuracy.

XGBoost provides an excellent balance between runtime (5 minutes) and accuracy (90%). It’s well-optimized for performance and scalability, and thus is a strong candidate for real-time classification when balancing both efficiency and predictive quality.

Why the other options are less ideal:

B: Random forest is faster but significantly less accurate.

C: Decision trees have longer run time than XGBoost with only a 2% accuracy improvement.

D: Artificial neural network has the highest accuracy but is too slow for real-time applications.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 4.3:“In real-time applications, model selection involves a trade-off between accuracy and inference speed. XGBoost offers competitive accuracy with efficient runtime.”

Machine Learning Systems Design Guide, Chapter 7:“XGBoost is well-suited for real-time systems due to its balance of model complexity and fast prediction times.”

—

→ 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.”

—