IBM A1000-041 - Assessment: Data Science Foundations - Level 1 Practice Exam 2025: Latest Questions

A retail team says, “We want to use data science to reduce customer churn.” In the Data Science Methodology, which step should come next after establishing this business goal?

You are exploring a dataset and want to understand the distribution of a single continuous variable (e.g., customer age) and identify potential skew. Which visualization is most appropriate?

In pandas, you read a CSV into a DataFrame named df. You need the first 5 rows to quickly verify columns and sample values. Which command is best?

A model predicts whether an email is spam (spam vs not spam). This is an example of which type of machine learning problem?

A team is building a churn model. They have a column called "cancellation_reason" that is filled in only after the customer cancels. Including it greatly improves training accuracy. What is the most likely issue?

You create a scatter plot of advertising spend (x) vs sales (y) and see a few extreme points far from the rest. What is the best next step before fitting a linear regression model?

You have a pandas DataFrame df with a column "region" and you need the mean of "revenue" per region. Which approach is most appropriate?

A dataset has 95% 'not fraud' and 5% 'fraud'. A model achieves 95% accuracy by predicting 'not fraud' for every case. Which metric is generally more informative for evaluating fraud detection performance?

You split your data into train and test sets, then scale features using the mean and standard deviation computed on the full dataset before training. Test performance looks unusually high. What is the primary problem and best fix?

A data scientist builds a model to predict customer lifetime value. They use one-hot encoding for a categorical feature "plan_type". In production, a new plan type appears that was not seen during training, causing the pipeline to fail. Which is the best mitigation approach?

A retail team is defining a data science project to reduce customer churn. They have a clear business goal but are unsure what to do next in the methodology. What is the BEST next step?

You created a scatter plot of house size vs. sale price and notice a few extreme points far from the main cluster. Before fitting a predictive model, what is the MOST appropriate action?

In Python with pandas, you need to select the values in the 'age' column where the 'country' column equals 'CA'. Which snippet correctly performs this filter?

A telecom analyst evaluates a churn classifier and finds accuracy is 95%, but only 2% of customers churn. What metric is MOST useful to understand how well the model identifies churners?

A team is building a model using historical loan applications. They notice the dataset includes a column 'loan_approved' and another column 'approval_reason_code' that is only filled in after a decision is made. What is the PRIMARY risk if 'approval_reason_code' is used as a feature?

A dataset contains 'income' with many missing values. You want a quick, interpretable baseline approach before trying advanced methods. Which approach is MOST appropriate as an initial strategy?

A notebook takes too long to compute a grouped summary. Which pandas approach is generally MOST efficient and idiomatic to compute average sales by 'region'?

A marketing analyst wants to segment customers into groups based on purchase behavior without labeled outcomes. Which approach is MOST appropriate?

You are comparing two classifiers on the same dataset. Model A has higher accuracy, but Model B has higher recall for the positive (rare) class. The business cost of missing a positive case is very high. What is the BEST choice and rationale?

A data scientist builds a preprocessing pipeline where feature scaling is performed before splitting into training and test sets. The test performance looks unusually strong and drops sharply in production. What is the MOST likely cause?

A retailer wants to predict whether a customer will churn (Yes/No). The dataset has many more non-churners than churners. Which evaluation metric is MOST appropriate to understand how well the model identifies churners?

During exploratory data analysis, you suspect a numeric feature contains extreme outliers that distort the distribution. Which visualization is BEST to quickly confirm the presence of outliers and compare spread?

You are creating a reproducible machine learning workflow in Python. You split data into train/test, then scale numeric features. A teammate scales the entire dataset before splitting. What is the primary issue with scaling before the split?

A project team is in the Data Science Methodology phase where they translate a business objective into a data science problem and define how success will be measured. Which activity BEST fits this phase?

A data scientist trains a decision tree model that performs extremely well on training data but poorly on a held-out test set. Which action is MOST likely to reduce this problem?