Free CompTIA DY0-001 Practice Exam with Questions & Answers | Set: 3

→ Creating an endpoint allows other systems or departments to access the trained model via HTTP requests. This typically involves exposing the model as a RESTful API, allowing it to be queried by web-based systems.

Why the other options are incorrect:

A: Distributed computing refers to computation, not access over HTTP.

B: Containers are useful for deployment, but the endpoint enables access.

D: FTP is used for file transfer, not model inference via HTTP.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 5.4:“Endpoints are used to expose models to external consumers over HTTP protocols, often using REST APIs.”

ML Deployment Best Practices, Chapter 3:“RESTful endpoints provide real-time access to model predictions and are key for multi-team collaboration.”

→ Scatter plots are used to examine relationships and trends between two numeric variables. They are especially effective at revealing linear (or nonlinear) patterns, clusters, and outliers.

Why the other options are incorrect:

A: Violin plots show distribution and density, not relationships.

B: Box plots compare distributions across groups but don’t reveal linearity.

D: Q-Q plots test normality, not variable relationships.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 1.2:“Scatter plots are commonly used during EDA to identify correlations, linearity, and outliers between two continuous variables.”

Data Science Fundamentals, Chapter 2 – EDA Techniques:“To assess linear trends and relationships, scatter plots provide a direct visual assessment between variables.”

The scenario provided describes a modeling problem with the following characteristics:

A single continuous predictor variable (independent variable).

A continuous real-number dependent variable.

The relationship between the variables appears strong and linear, as observed from the scatter plot.

The predictor variable is normally distributed with minimal outliers.

The goal is to maintain interpretability in the model.

Based on the above, the most appropriate modeling technique is:

Linear Regression: This is a statistical method used to model the linear relationship between a continuous dependent variable and one or more independent variables. In simple linear regression, a straight line (y = mx + b) represents the relationship, where the slope and intercept can be easily interpreted. This method is preferred when the relationship is linear, the assumptions of normality and homoscedasticity are satisfied, and interpretability is required.

Why the other options are incorrect:

A. Logistic Regression: This is used when the dependent variable is categorical (e.g., binary classification), not continuous. Therefore, not suitable for this case.

B. Exponential Regression: Applied when the data shows an exponential growth or decay pattern, which is not implied here.

D. Probit Regression: Similar to logistic regression but based on a normal cumulative distribution. Used for categorical outcomes, not continuous variables.

Exact Extract and Official References:

CompTIA DataX (DY0-001) Official Study Guide, Domain: Modeling, Analysis, and Outcomes:

“Linear regression is the most interpretable form of regression modeling. It assumes a linear relationship between independent and dependent variables and is ideal for inferential modeling when interpretability is important.” (Section 3.1, Model Selection Criteria)

Data Science Fundamentals, by CompTIA and DS Institute:

"Linear regression is a robust and interpretable statistical method used for modeling continuous outcomes. It provides coefficients which help in understanding the strength and direction of the relationship." (Chapter 4, Regression Techniques)

→ A Normal distribution is appropriate for modeling variables that cluster around a central mean and have natural variability — such as bus arrival times around a scheduled time. Even though the bus is scheduled hourly, real-world factors (traffic, weather, etc.) will cause actual arrival times to vary normally around the scheduled mean.

Why the other options are incorrect:

A: Binomial is for discrete yes/no trials, not continuous time modeling.

B: Exponential models time between events, typically memoryless — not suitable for arrival distributions with a known mean and variance.

D: Poisson models event counts per time interval, not the timing of continuous events like arrival times.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 1.3:“Normal distributions are appropriate for modeling real-world continuous variables that fluctuate around a central tendency, such as scheduled processes.”

Statistics for Data Science, Chapter 4 – Distributions:“Arrival times of periodic services often approximate a normal distribution when influenced by continuous variation.”

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→ R² (coefficient of determination) quantifies how much of the variance in the dependent variable is explained by the model. A higher R² means a better fit to the data, making it the metric to maximize for explanatory power in regression analysis.

Why the other options are incorrect:

A: Accuracy is used in classification, not regression.

C: p-values test statistical significance of coefficients, not overall model fit.

D: AUC (Area Under the Curve) applies to classification models, not regression.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 3.2:“R² is a regression performance metric indicating the proportion of variance explained by the independent variables.”