CompTIA Updated DY0-001 Exam Questions and Answers by milena

→ Greedy algorithms make decisions based on what appears to be the best (most optimal) choice at that current moment — i.e., a locally optimal decision — without regard to whether this choice will yield the globally optimal solution.

Examples in machine learning:

Decision Tree algorithms (e.g., CART) use greedy approaches by selecting the best split at each node based on information gain or Gini index.

Why the other options are incorrect:

A: This refers to Bayesian updating, not greedy behavior.

B: That describes exhaustive search, not greediness.

C: That aligns more with probabilistic or generative models, not greedy strategies.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 4.2 (Model Selection Methods):“Greedy algorithms make locally optimal decisions at each step. Decision trees, for instance, use greedy splitting based on current best criteria.”

Elements of Statistical Learning, Chapter 9:“Greedy methods make stepwise decisions that maximize immediate gains — they are fast, but may miss the global optimum.”

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→ In ridge regression, the model minimizes the sum of squared residuals (errors), with an added penalty term on the magnitude of coefficients (L2 regularization). The residual component specifically is represented by:

→ e² (squared error)

Thus, ridge regression minimizes:

Minimize: ∑(yᵢ − ŷᵢ)² + λ∑(β²)

Why the other options are incorrect:

A: |e| corresponds to L1 loss (used in Lasso).

B: e represents the error term itself, not its minimized quantity.

D: Zero error is ideal but practically unachievable and not the actual loss function being minimized.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 1.4:“Ridge regression minimizes the squared error term with an L2 penalty.”

Introduction to Statistical Learning, Chapter 6:“Ridge regression uses squared error loss, which emphasizes larger deviations more heavily than linear loss.”

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→ 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.”

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→ Kurtosis is a statistical measure that describes the "tailedness" or extremity of values in a distribution. The observation that smaller events decrease while extreme events increase indicates a rise in heavy tails — a textbook sign of increasing kurtosis. This reflects a distribution becoming more prone to extreme values (e.g., more impactful blackouts).

Why the other options are incorrect:

A: “Scale axis magnitudes” is not a statistical metric but refers to plotting.

C: Skewness measures asymmetry, not the frequency of extreme values.

D: Normality checks whether a distribution follows the normal distribution, not its tail behavior.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 1.3:“Kurtosis measures the presence of outliers and extreme values in a distribution — higher kurtosis suggests more frequent extreme events.”

Applied Statistical Analysis, Chapter 4:“Kurtosis provides insight into the likelihood of extreme deviations and is useful in risk and reliability analysis.”

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