CompTIA DataX Free Sample Questions

Recall, also known as Sensitivity or True Positive Rate, measures the proportion of actual positives that were correctly identified. In this scenario, the cost of missing a potential failure (a False Negative) is very high. Therefore, the primary goal is to maximize the number of true failures caught by the model, which is precisely what Recall measures. Accuracy would be misleadingly high due to the class imbalance. Precision focuses on the proportion of positive predictions that are actually correct, which is less critical here than catching all potential failures.

Analysis of Variance (ANOVA) is used to compare the means of three or more independent groups to determine if there is a statistically significant difference between them. A T-test is used for comparing the means of only two groups. A Chi-squared test is used for categorical data, not continuous data like test scores. Pearson correlation measures the linear relationship between two continuous variables, not differences in means across groups.

Docker is used to create containers, which package the model, its dependencies, and the API server into a single, isolated, and portable unit. This addresses the isolation and versioning requirement.

Kubernetes is a container orchestration platform that manages containerized applications (like those created with Docker) at scale. It handles auto-scaling, load balancing, and self-healing, which is essential for a high-traffic application.

This statement accurately describes the process of transfer learning. A model pre-trained on a large, general dataset (like ImageNet) has already learned useful features. This knowledge is transferred by using its weights as a starting point for training on a new, smaller, and more specific dataset, which is a highly effective technique when data is limited.

This solution is the most comprehensive. It uses a managed Spark service (e.g., AWS EMR, Databricks, GCP Dataproc) to handle the massive scale of batch training, which aligns with the team's need to minimize infrastructure overhead. It correctly uses the ALS algorithm, which is designed for large-scale collaborative filtering. It explicitly addresses the cold-start problem with a hybrid approach (content-based model). Finally, it uses a low-latency NoSQL database (like DynamoDB or Cassandra) for serving, which is a best practice for real-time recommendation systems.

t-SNE is a non-linear dimensionality reduction technique specifically designed for visualizing high-dimensional data in low-dimensional space (typically 2D or 3D). It excels at revealing the underlying structure of data, such as clusters, by preserving the local similarities between data points. PCA, in contrast, is a linear technique focused on maximizing variance and may not effectively separate clusters that are not linearly separable.

A right-skewed (or positively skewed) distribution has a long tail extending to the right. This is caused by a smaller number of high-value outliers pulling the mean to the right. In such a distribution, the typical relationship is Mean > Median > Mode. The median is less affected by outliers than the mean, so it remains closer to the bulk of the data.

Model drift, also known as concept drift, occurs when the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways. This causes the model, which was trained on historical data, to become less accurate as time passes. The gradual decrease in performance is a classic symptom of model drift, often caused by changes in fraudulent behavior patterns.

This represents a standard and effective pipeline for text classification. 1) Tokenization breaks the text into individual words (tokens). 2) Stop word removal eliminates common words ('the', 'a', 'is') that add little semantic value. 3) TF-IDF (Term Frequency-Inverse Document Frequency) vectorization converts the cleaned text into numerical vectors that represent the importance of each word in the context of the entire corpus, which is an ideal input for most machine learning classifiers.

This is the correct definition of the R-squared (coefficient of determination) value. It is a statistical measure that provides a 'goodness of fit' for the model, indicating how much of the variability in the outcome data can be explained by the model's inputs.