IBM A1000-041 - Assessment: Data Science Foundations - Level 1 Intermediate Practice Exam: Medium Difficulty 2025

A data science team is working on a customer churn prediction project. They have completed the business understanding phase and are now collecting data from multiple sources including CRM systems, transaction logs, and customer service records. According to the CRISP-DM methodology, what should be their primary focus in the next phase?

A data analyst needs to visualize the relationship between customer age (continuous variable) and purchase frequency (continuous variable) for 5000 customers, while also showing the density of data points. Which combination of visualization techniques would be most appropriate?

During the model evaluation phase, a data scientist finds that their classification model achieves 95% accuracy on the test dataset. However, the dataset is highly imbalanced with only 5% positive cases. What should be the data scientist's primary concern and next step?

A data analyst is preparing a DataFrame in Python with sales data. They need to group the data by 'region' and 'product_category', then calculate both the sum of sales and the count of transactions for each group. Which approach accomplishes this most efficiently?

A retail company wants to visualize monthly sales trends over 3 years across 4 different product categories on a single chart. They need to identify seasonal patterns and compare category performance. Which visualization type would be most effective?

A data scientist is preprocessing text data for sentiment analysis. They need to convert a list of customer reviews into a numerical format that can be used by machine learning algorithms while preserving word frequency information. Which technique should they apply?

During data preparation, a data scientist discovers that the 'income' feature has 15% missing values. The data appears to be missing not at random (MNAR), as higher-income individuals are less likely to report. The feature is important for the predictive model. What is the most appropriate strategy?

A Python data scientist needs to filter a DataFrame to include only rows where the 'age' is between 25 and 45, the 'income' is greater than 50000, and the 'region' is either 'North' or 'South'. Which code accomplishes this correctly?

A machine learning model is being trained to predict house prices. After initial training, the model shows 95% accuracy on training data but only 65% on test data. What problem is most likely occurring, and what is the best initial approach to address it?

A data analyst needs to create a visualization showing the distribution of customer satisfaction scores (1-5 scale) across three different service channels. They want to compare not just the median values but also the spread and potential outliers. Which visualization would best serve this purpose?