Free CompTIA DataAI Certification Exam DY0-001 Exam Questions

Generating embeddings transforms each sentence into a dense numerical vector in a semantic space, where conceptually similar sentences lie close together, enabling straightforward similarity calculations (e.g., cosine similarity) to group or identify related sentences.

Examining a correlation matrix helps identify predictors that are highly correlated with each other, which can inflate variance in coefficient estimates and degrade model reliability - i.e., multicollinearity.

Parquet is a columnar storage format that automatically includes schema (data types), uses efficient compression to minimize file size, and enables very fast reads for analytic workloads.

Back propagation computes the gradient of the loss (error) with respect to each weight by propagating the error signal backward through the network, then uses those gradients to adjust weights and biases.

Classifying an actual cat (positive instance) as a dog (negative prediction) is a false negative, which corresponds to a Type II error.

Ridge regression extends ordinary least squares by adding an L2 penalty on the coefficients, but it still minimizes the sum of squared residuals (e) as its loss term.

LASSO regression retains the ordinary least squares loss by minimizing the sum of squared residuals (e), with an added L1 penalty on the coefficients, but the residual term itself remains squared.

RESTful APIs use standard HTTP methods and lightweight data formats (typically JSON), making them easy for diverse teams to integrate with minimal effort and without heavy tooling.

Increasing the number of nodes in your cluster directly expands the total available memory across the distributed system, alleviating memoryconstraint errors without changing your code or deployment paradigm. Containerization or edge deployments don't inherently provide more memory, and migrating to the cloud alone doesn't guarantee additional nodes unless you explicitly scale out.