Free IBM A1000-041 - Assessment: Data Science Foundations - Level 1Practice Test

A data science team is beginning a new project to predict customer churn. According to the CRISP-DM methodology, what should be the first phase they focus on?

During the data preparation phase of a data science project, a data scientist discovers that 15% of values in a critical numerical feature are missing. The data appears to be missing at random. What is the most appropriate initial approach to handle this situation?

In the evaluation phase of the data science methodology, which metric would be MOST appropriate for assessing a highly imbalanced binary classification model where correctly identifying the minority class is critical?

A data science project has completed the modeling phase with satisfactory results. According to best practices, what is the primary purpose of the deployment phase?

A team is working on a data science project and needs to iterate between the data understanding and data preparation phases multiple times. Is this approach acceptable within the CRISP-DM framework?

When creating a visualization to show the distribution of a single continuous variable, which chart type is MOST appropriate?

A data analyst needs to visualize the relationship between two continuous numerical variables to identify potential correlations. Which visualization would be MOST effective?

A dataset contains sales data with extreme outliers due to a few very large corporate orders. When calculating a measure of central tendency that is robust to outliers, which metric should be used?

A data scientist is analyzing a dataset and calculates a correlation coefficient of -0.85 between two variables. What does this indicate?

When performing exploratory data analysis (EDA), a data scientist discovers that a categorical variable has 500 unique categories in a dataset of 10,000 rows. What concern does this raise?

A business stakeholder asks for a visualization to compare sales performance across 12 different product categories for the current quarter. Which visualization would be MOST appropriate?

During data analysis, a data scientist needs to identify which features have the strongest linear relationships with the target variable. Which statistical method or visualization would be MOST helpful?

In Python, which pandas method would you use to get a concise summary of a DataFrame including the data types, non-null counts, and memory usage?

A data scientist needs to filter a pandas DataFrame to include only rows where the 'age' column is greater than 25 AND the 'city' column equals 'Boston'. Which syntax is correct?

When working with NumPy arrays, which characteristic distinguishes them from Python lists and makes them more efficient for numerical computations?

A data scientist needs to handle missing values in a pandas DataFrame by replacing them with the mean of each column. Which method accomplishes this most efficiently?

Which Python library is specifically designed for creating static, animated, and interactive visualizations and is most commonly used in data science for plotting?

In supervised machine learning, what is the key difference between classification and regression problems?

A machine learning model performs extremely well on training data (99% accuracy) but poorly on test data (65% accuracy). What problem does this indicate, and what is the most appropriate solution?

In the context of machine learning model evaluation, what is the purpose of using cross-validation instead of a single train-test split?