spring_26_182__exam1_makeup.pdf

You have visits and purchases aggregated by channel. The business asks for “conversion rate.” If a channel has 12,000 visits and 420 purchases, what is its conversion rate (as a decimal)?

Fill in the best CRISP-DM phase for each activity.   Defining the business success metric (e.g., reduce churn by 2 points) belongs to: Fixing missing values and parsing dates belongs to: Selecting features and training a regression model belongs to: Interpreting RMSE and whether it meets the business threshold belongs to:

You need to compute “days since last purchase.” purchase_date is currently a string like “2025-09-14”. Which of the following is the best first step?

Using the same model: revenue = 12,000 + 4.5 * ad_spend What is the best interpretation of the intercept (12,000)?

You load a CSV and run the head(3) method to see the top few rows of data. You see: df.head() #0  “$1,250.00” #1  “$980.50” #2  None #3  “$2,100.00” Which approach most directly converts revenue into a numeric column (float) with missing values preserved as NaN?

You’re predicting future customer_spend_next_month. Which feature is most likely problematic due to leakage?

A VP wants a table with: rows = region, columns = quarter, values = total revenue. Which of the following provides the best approach?

An analyst runs the following code: df * df and gets string repetition (e.g., “10” * “3.50” errors or weird output). What is the most likely root cause?

A 50.0 g ball traveling horizontally at 32.0 m/s rebounds off a wall in the opposite direction at 24.0 m/s. If the collision with the wall took 2.50×10-3 s, what was the force exerted on the ball?