Free Practice Questions for CompTIA DY0-001 Exam

→ Extrapolated data refers to making predictions about data points that fall outside the observed range or distribution. Since the sample data (80%) is heavily skewed toward a small subset of professions and locations, predicting results for the remaining, unrepresented professions and regions involves extrapolation.

Why the other options are incorrect:

A: Interpolation occurs within the bounds of observed data — not the issue here.

C: In-sample data refers to training data, which is overrepresented in this case.

D: Out-of-sample data is a concern in generalization but extrapolation is more specific here.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 3.2:“Extrapolation introduces risk when models are used outside the range of data they were trained on, especially if certain subgroups are underrepresented.”

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→ DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm that does not require specifying the number of clusters in advance. It identifies clusters of arbitrary shape and separates noise/outliers based on density thresholds.

Why other options are incorrect:

B: k-NN is a supervised classification algorithm, not used for clustering.

C: k-means requires predefining the number of clusters (k).

D: Logistic regression is a classification model, not for clustering.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 4.2:“DBSCAN detects clusters based on data density without the need for a predefined k value and handles outliers effectively.”

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→ Bacterial growth under ideal conditions follows exponential behavior: the population doubles at regular intervals. This results in a rapid increase that aligns with the formula: N(t) = N₀e^(rt), where N₀ is the initial population and r is the growth rate.

Why the other options are incorrect:

A: Linear models show constant growth, not doubling.

B: Logarithmic models show slowing growth — opposite of exponential.

C: Polynomial growth is slower than exponential and not suitable for biological doubling.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 1.3:“Exponential growth occurs when the rate of increase is proportional to the current value, common in population dynamics.”

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→ XGBoost (Extreme Gradient Boosting) is a high-performance, scalable ensemble algorithm that builds decision trees in sequence and adjusts to errors iteratively. It also supports incremental training, making it adaptive to changing data patterns — ideal for dynamically updated credit information.

Why the other options are incorrect:

A: Decision trees are static once trained and don’t adapt unless retrained.

B: Random forest is an ensemble of trees but lacks the adaptive boosting component.

C: LDA is a linear classification technique — not suited for adapting to changing data distributions.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 4.3:“XGBoost is highly efficient and supports iterative learning, making it well-suited for data environments that evolve over time.”

Applied Machine Learning Guide, Chapter 8:“XGBoost adapts to changes by refining errors across iterations, providing robustness in dynamic systems.”

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→ 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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