CompTIA Updated DY0-001 Exam Questions and Answers by livia

→ Naive Bayes is a probabilistic classification algorithm based on Bayes’ theorem. It is lightweight, fast, and effective for text-based classification problems like spam detection. It also performs well with small or simple training sets.

Why the other options are incorrect:

A: Logistic regression is also probabilistic but requires more feature preprocessing.

B: Random forest is accurate but computationally heavier.

D: Linear regression is for continuous targets — not suitable for classification.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 4.1:“Naive Bayes classifiers are ideal for spam detection and similar applications due to their efficiency and probabilistic nature.”

Text Classification Techniques, Chapter 4:“Naive Bayes requires minimal training and works well with high-dimensional, sparse data such as email content.”

→ Line plots are ideal for visualizing data trends over continuous time. In this case, plotting the total daily measurements across a two-year period is a time series task, and a line plot shows progression and pattern over time clearly.

Why the other options are incorrect:

A: Scatter plots are better for relationship exploration, not time trends.

C: Histograms display distribution — not suitable for continuous time trends.

D: Box plots show spread and outliers — not temporal behavior.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 1.2:“Use line plots for visualizing temporal trends in time-series data.”

Time Series Visualization Guide, Chapter 2:“Line plots are effective for showing cumulative or aggregated values over time.”

—

→ When generating synthetic data, the key concern is ensuring it accurately reflects the characteristics of the real-world population. A non-representative synthetic dataset may lead to biased models and invalid conclusions.

Why the other options are incorrect:

A: Resource usage is a technical concern but not as critical as representativeness.

B: Feature set can often be replicated or engineered — quality matters more.

C: Synthetic datasets can be scaled up easily — representativeness is harder to validate.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 5.4:“Synthetic data must maintain representational fidelity to the original population in order to be useful for modeling or validation.”

—

→ A one-feature model is likely to be overly simplistic and may not capture the true complexity of the target variable. This leads to underfitting, which is associated with high bias — the model consistently misses the mark regardless of the data.

Why the other options are incorrect:

B: High dimensionality is not a concern in this case — the model has too few features.

C: Variance refers to overfitting — more common in overly complex models.

D: Probability is a modeling technique, not a source of error.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 4.2:“Models with insufficient features tend to underfit and exhibit high bias due to their inability to represent complex relationships.”

Bias-Variance Tradeoff – Data Science Textbook:“A high-bias model makes strong assumptions and is typically too simple to capture the underlying patterns in data.”