Free IBM A1000-120 - Assessment: Data Science FoundationsPractice Test

A data science team is beginning a new project to predict customer churn. What should be the first step in the data science methodology?

A data scientist needs to handle missing values in a dataset where 40% of the values in a critical numerical column are missing. The data appears to be missing at random. What is the most appropriate approach?

In a data science project lifecycle, what is the primary purpose of the data preparation phase?

A dataset contains both categorical and numerical features. Before applying K-means clustering, what data preprocessing step is most critical?

What does the Central Limit Theorem state about the distribution of sample means?

A data scientist is working with a highly imbalanced dataset where the positive class represents only 2% of the data. Which evaluation metric would be most appropriate for assessing model performance?

What is the key difference between supervised and unsupervised learning?

A data scientist observes that their model performs extremely well on training data (99% accuracy) but poorly on test data (65% accuracy). What problem is the model experiencing?

In hypothesis testing, a researcher sets alpha (significance level) at 0.05 and obtains a p-value of 0.03. What should the researcher conclude?

Which visualization type is most appropriate for showing the distribution of a single continuous variable and identifying potential outliers?

A company wants to understand which customer segments exist in their database based on purchasing behavior, demographics, and engagement metrics. No predefined categories exist. Which type of machine learning approach is most suitable?

What is the purpose of cross-validation in machine learning?

In a correlation analysis, two variables have a Pearson correlation coefficient of -0.85. What does this indicate?

A data scientist needs to reduce the dimensionality of a dataset with 100 features to visualize it in 2D while preserving as much variance as possible. Which technique is most appropriate?

A data science team is working with sensitive customer data including personal identifiable information (PII). What is the most important consideration from a data ethics and governance perspective?

In a linear regression model, what does the coefficient of determination (R-squared) represent?

A dataset contains a categorical variable 'Country' with 50 unique values. The data scientist needs to use this feature in a machine learning model. What is a potential problem with using one-hot encoding for this variable?

In a confusion matrix for a binary classification problem, what does the term 'False Negative' represent?

A data scientist is building a recommendation system for an e-commerce platform. The system needs to suggest products based on user behavior patterns and preferences learned from historical data. Which approach combines both user-item interactions and item characteristics?

When performing feature engineering, a data scientist creates interaction terms between two continuous variables. What is the primary purpose of this technique?