AI Associate Practice Test 43 — 15 Questions

Question 1

Which TWO statements correctly describe predictive AI compared to generative AI?

Accepted Answer

Predictive AI is used for forecasting numeric outcomes like churn probability. Predictive AI forecasts outcomes (churn, CLV) using historical data, while generative AI creates new content like text or images.

Answer

Generative AI is best suited for lead scoring

Answer

Both predictive and generative AI rely on the same algorithms

Answer

Predictive AI can generate realistic customer service responses

Question 2

A company wants to use AI to automatically route customer support emails to the appropriate department (billing, technical, sales). Which THREE AI capabilities are needed? (Choose three.)

Accepted Answer

Intent detection. Text classification categorizes emails, intent detection identifies the purpose, and entity extraction can identify account numbers or products to aid routing. Sentiment analysis is about emotion, not routing. Generative AI creates responses, not needed for routing.

Answer

Sentiment analysis

Answer

Generative AI

Question 3

A model predicts customer churn with 95% accuracy, but most customers who actually churn are not flagged by the model. Which metric should the team improve?

Accepted Answer

Recall. Recall (true positive rate) measures how many actual churners are correctly identified. High accuracy with low recall indicates the model is biased toward the majority class (non-churners). Precision focuses on how many flagged churners actually churn, F1 is a balance, and accuracy is already high.

Answer

Precision

Answer

Accuracy

Answer

F1 score

Question 4

A predictive model for opportunity scoring shows high precision but low recall. Which business impact is most likely?

Accepted Answer

The sales team misses some convertible opportunities. High precision means few false positives (won deals predicted correctly), but low recall means many actual wins are missed. The sales team will likely miss out on some convertible opportunities.

Answer

The model is overfitting to the training data

Answer

The sales team wastes time on many low-quality opportunities

Answer

The model overestimates the likelihood of conversion for most opportunities

Question 5

A financial services firm uses an AI model to approve small business loans. The model denies loans at a much higher rate for businesses owned by minorities, even when financial indicators are similar. What is the MOST likely cause?

Accepted Answer

Bias in training data. Bias in training data can cause the model to learn historical discrimination patterns. Overfitting would not cause systematic demographic differences. Hallucination is for generative AI. Low recall may indicate the model misses some positive cases but doesn't explain the demographic disparity.

Answer

Low recall for minority groups

Answer

Hallucination in model predictions

Answer

Overfitting to historical data

Question 6

A model trained on CRM data predicts customer lifetime value (CLV) with high accuracy, but when deployed, predictions are significantly off for new customer segments. What is the most likely cause?

Accepted Answer

The training data was not representative of the new segments. If the training data was not representative of the new segments, the model will not generalize, leading to poor out-of-sample performance.

Answer

Feature engineering was insufficient for the original segments

Answer

The model is overfitting to the training data

Answer

The model architecture is too simple

Question 7

A company is developing a sentiment analysis model for customer reviews. The team wants to ensure the model is fair and does not exhibit bias. Which TWO actions are MOST effective? (Choose two.)

Accepted Answer

Audit the training data for imbalanced representation across demographic groups. Auditing training data for bias and ensuring diverse representation helps mitigate bias. Using a complex model doesn't address bias; removing all negative examples would bias the model. Using historical data as-is may perpetuate existing biases.

Answer

Use a more complex model to capture nuanced patterns

Answer

Use only historical data as-is without modification

Answer

Remove all negative reviews from the training data to avoid negative bias

Question 8

A retail company wants to use AI to predict next month's sales for each product category. They have five years of monthly sales data. Which THREE factors are most critical for the accuracy of the predictive model? (Select THREE.)

Accepted Answer

Including seasonality and trend components from historical data. Historical patterns (seasonality), external factors (holidays), and data quality are key for time series forecasting. Model complexity and algorithm brand are less critical.

Answer

Choosing the most popular AI algorithm on the market

Answer

Using a deep learning model instead of a simpler model

Question 9

A company notices that Einstein Prediction Builder predictions for 'Churn' are less accurate than expected. Which action should the administrator take first to improve model performance?

Accepted Answer

Review the training data for missing values and ensure relevant fields are included in the model.. The first step in improving Einstein Prediction Builder model performance is to review the training data for missing values and ensure relevant fields are included. Missing values or irrelevant fields can introduce noise and bias, directly degrading predictive accuracy. Einstein Prediction Builder relies on high-quality, complete training data to learn meaningful patterns, so data quality issues must be addressed before any other tuning steps.

Answer

Enable field history tracking on all object fields used in the prediction.

Answer

Change the prediction outcome to a different field to see if accuracy improves.

Answer

Retrain the model with the same data but increase the number of training iterations.

Question 10

A company wants to generate personalized marketing email content for each customer, including product recommendations and tailored copy. Which AI approach is BEST?

Accepted Answer

Use predictive AI to score customers and generative AI to create email copy based on the scores. Generative AI can create original content, and predictive AI can recommend products. The combination of both can generate personalized copy with dynamic product suggestions. Using only predictive AI yields recommendations but not content; generative AI alone can create text but lacks recommendation logic.

Answer

Use an unsupervised learning model to cluster customers and send generic emails per cluster

Answer

Use only predictive AI to send the same email to all customers with top products

Answer

Use only generative AI to create emails without any customer data

Question 11

What is the term for when an AI model produces confident but incorrect information, often in generative AI?

Accepted Answer

Hallucination. Hallucination refers to when a generative AI model fabricates information that seems plausible but is false.

Answer

Bias

Answer

Overfitting

Answer

Underfitting

Question 12

A data scientist is building a churn prediction model. What THREE factors are most critical for model success?

Accepted Answer

Relevant features correlated with churn. Quality labeled historical data ensures the model learns from accurate patterns. Relevant features (e.g., usage frequency, support tickets) improve predictions. A representative dataset avoids bias and ensures generalization.

Answer

High number of model parameters

Answer

Large dataset size

Question 13

A company wants to use AI to reduce customer churn. Which TWO approaches are most appropriate? (Select 2)

Accepted Answer

Use predictive AI to score churn risk based on historical data. Predictive AI can forecast churn likelihood, and sentiment analysis can identify dissatisfied customers early.

Answer

Use generative AI to create personalized retention offers

Answer

Use computer vision to analyze customer photos

Answer

Use reinforcement learning to train a chatbot

Question 14

A data scientist evaluates a churn prediction model. On the test set, the model achieves 99% accuracy, but the business reports that the model rarely flags actual churners. Which metric should the data scientist focus on to improve the model?

Accepted Answer

Recall. When classes are imbalanced (few churners), accuracy can be misleading. Recall measures how many actual churners are identified, which is the key business concern.

Answer

F1 score

Answer

Accuracy

Answer

Precision

Question 15

A data scientist trains a model to predict customer churn. The model achieves 98% accuracy on training data but only 72% on test data. What issue is most likely occurring?

Accepted Answer

Overfitting. Overfitting occurs when a model learns training data too well, including noise, and fails to generalize to new data.

Answer

Data leakage

Answer

Underfitting

Answer

Bias in the training data