Pass the CompTIA Data+ DY0-001 Questions and answers with ExamsMirror

→ Embeddings (e.g., word2vec, sentence transformers) are vector representations of text that capture semantic similarity. They allow comparison of conceptual meaning between sentences in a high-dimensional space, which is essential for tasks like semantic similarity or clustering.

Why the other options are incorrect:

B: Extrapolation predicts values beyond a dataset’s range — not relevant here.

C: Sampling reduces data volume but doesn’t aid in similarity analysis.

D: One-hot encoding captures presence of words but lacks semantic understanding.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 6.3:“Embeddings transform text into numeric vectors, enabling similarity computation and semantic analysis.”

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→ Backpropagation (short for “backward propagation of errors”) is the fundamental algorithm for training neural networks. It involves computing the error at the output and propagating it backward through the network to update weights and biases via gradient descent.

Why the other options are incorrect:

A: Convolutions are specific to CNNs and are not propagated in this manner.

B: Accuracy is an evaluation metric, not used in weight updates.

C: Nodes are structural elements, not passed backward.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 4.3:“Backpropagation passes the error backward from the output layer to the input layer to adjust weights using gradient-based optimization.”

Deep Learning Textbook, Chapter 6:“The backpropagation algorithm is essential for computing gradients of the loss function with respect to each weight.”

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→ The Traveling Salesman Problem (TSP) is a classic example of a constrained optimization problem. The goal is to find the shortest possible route that visits a set of locations once and returns to the origin point — under constraints such as distance, order, and time.

Why the other options are incorrect:

A: The cold start problem is related to recommender systems, not optimization.

C: Calculating a local maximum is part of optimization but not necessarily constrained.

D: Gradient descent is an optimization method, but not itself a problem with constraints.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 3.4:“Constrained optimization involves solving problems under defined limitations — e.g., distance or time constraints in routing.”

Optimization Techniques in Data Science, Chapter 6:“TSP is a benchmark in combinatorial optimization, representing a multi-variable problem with strict constraints.”

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→ 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.”

→ 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.”

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→ Principal Component Analysis (PCA) is an unsupervised technique used to reduce the dimensionality of large datasets by transforming correlated features into a smaller set of uncorrelated components (principal components) while retaining the most variance.

Why the other options are incorrect:

B: Classification is a predictive modeling task; PCA is not inherently predictive.

C: Regression models numerical relationships; PCA does not predict outcomes.

D: Recommendation systems use collaborative or content filtering, not PCA directly.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 3.3:“PCA is primarily used for reducing the number of variables while preserving data structure and minimizing information loss.”

Pattern Recognition and Machine Learning, Chapter 12:“PCA identifies principal axes of variation and is widely used in preprocessing for dimensionality reduction.”

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→ LASSO (Least Absolute Shrinkage and Selection Operator) regression minimizes the squared residuals (e²), just like OLS, but adds an L1 penalty to encourage sparsity in the coefficients. Thus, the residual component minimized is still the sum of squared errors.

Why the other options are incorrect:

A: |e| is absolute error, not used in standard LASSO objective.

B: e is the error term, but minimization applies to its squared version.

C: Minimizing to exactly 0 is idealistic but not realistic.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 3.3:“LASSO minimizes squared errors with an additional L1 regularization term.”

Elements of Statistical Learning, Chapter 6:“LASSO regression uses the same residual sum of squares (e²) as OLS for error measurement, with an added constraint.”

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→ Decision trees are supervised classification models that can be used to predict a categorical target variable (e.g., Habitat: Land or Sea) based on input features (e.g., Wrapper color, Wrapper shape, Animal type). They are interpretable, require minimal preprocessing, and are ideal for structured categorical data like this.

Why the other options are incorrect:

A: ARIMA (AutoRegressive Integrated Moving Average) is used for time-series forecasting, not classification.

B: Linear regression is used for predicting continuous numeric values, not categorical variables like "Land" or "Sea".

C: Association rules (like in market basket analysis) are used to discover relationships or co-occurrence among variables, not to build predictive models.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 4.1 & 4.2:“Decision trees are powerful classifiers for categorical output variables and allow for interpretable models based on feature splits.”

Machine Learning Textbook, Chapter 6:“Decision trees are ideal for early-stage model prototyping when the output is categorical and the data structure is tabular.”

→ Multicollinearity occurs when independent variables are highly correlated with each other. This can distort coefficient estimates and reduce model interpretability. A correlation matrix is the primary tool used to detect it.

Why the other options are incorrect:

A & C: Under/oversampling relate to class imbalance, not variable correlation.

D: Overfitting is related to model complexity, not directly observable via a correlation matrix.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 3.2:“Correlation matrices are used to detect multicollinearity — high correlations among predictors that may destabilize models.”

→ Categorical variables must be transformed into numerical form for most machine learning models. Two standard approaches:

One-hot encoding: Converts each category into a separate binary column (useful for nominal variables).

Label encoding: Converts categories into integers (useful for ordinal or tree-based models).

Why other options are incorrect:

A & E: Normalization and scaling are used for continuous variables, not categorical.

C: Linearization refers to transforming relationships, not categorical conversion.

F: Pivoting rearranges data structure but doesn’t encode categories.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 3.3:“Label encoding and one-hot encoding are common transformations applied to categorical variables to enable model compatibility.”

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