AI Associate AI Fundamentals Practice Test 8 — 15 Questions

Question 1

A company is building a sentiment analysis model for customer reviews. They want to measure its performance. Which TWO metrics are most appropriate for evaluating a classification model?

Accepted Answer

Recall. Precision and recall are key classification metrics. Accuracy can be misleading if classes are imbalanced. F1 is composite; but the question asks for two metrics, precision and recall are fundamental.

Answer

F1 score

Answer

R-squared

Answer

Mean squared error

Question 2

A data scientist is building a churn prediction model. The dataset has 95% non-churn and 5% churn. Which THREE actions should the data scientist take to address the class imbalance?

Accepted Answer

Undersample the non-churn class. Oversampling minority class, undersampling majority class, and using appropriate metrics (like precision/recall) are common approaches. Using accuracy is not recommended. Training on original data without adjustment will produce a biased model.

Answer

Train the model on the original dataset without changes

Answer

Use accuracy as the primary metric

Question 3

A company uses a generative AI model to create marketing copy. They want to ensure the output is accurate and not misleading. Which TWO practices should they implement?

Accepted Answer

Always have a human review the generated content before publishing. Human review catches errors (hallucinations). Grounding the model in verified data reduces false information. Avoid over-reliance on the model without verification.

Answer

Increase the model's temperature setting to encourage creativity

Answer

Disable all content filtering to avoid blocking useful output

Answer

Use the model's output directly without checking

Question 4

A sales operations manager wants to predict which leads are most likely to convert to deals. The CRM has historical data on thousands of leads with outcomes (converted or not). Which type of machine learning should they use?

Accepted Answer

Supervised learning. Supervised learning uses labeled data (past leads with known outcomes) to predict future lead conversion. Unsupervised learning finds patterns without labels, reinforcement learning learns from rewards, and deep learning is a subset of supervised/unsupervised but not the most specific answer here.

Answer

Unsupervised learning

Answer

Deep learning

Answer

Reinforcement learning

Question 5

A company uses an AI model to classify customer support cases into categories (billing, technical, general). The model performs well on training data but poorly on new cases. Which issue is MOST likely occurring?

Accepted Answer

Overfitting. Overfitting means the model memorized the training data and fails to generalize to new, unseen data. Underfitting would show poor performance on both training and test data. Data leakage occurs when future information leaks into training. Bias in data is a different issue.

Answer

Underfitting

Answer

Data leakage

Answer

Bias in training data

Question 6

A customer service team wants to automatically detect the intent of incoming chat messages (e.g., complaint, inquiry, purchase). Which AI technique is BEST suited for this task?

Accepted Answer

Natural language processing (NLP). Natural language processing (NLP) deals with understanding human language, including intent detection. Computer vision is for images, reinforcement learning for decision-making, and generative AI for content creation.

Answer

Generative AI

Answer

Reinforcement learning

Answer

Computer vision

Question 7

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 8

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 9

A company wants to generate personalized product recommendations for each customer based on their purchase history and browsing behavior. Which approach is MOST appropriate?

Accepted Answer

Train a supervised learning model on historical purchase data to predict the next product a customer will buy. Supervised learning can predict what a customer might buy next based on labeled data (past purchases). Unsupervised learning can cluster customers but doesn't directly generate recommendations. Reinforcement learning is for dynamic environments, and generative AI creates content, not recommendations.

Answer

Use a generative AI model to create new product descriptions for each customer

Answer

Deploy a reinforcement learning agent that explores different recommendations in real-time

Answer

Use an unsupervised learning algorithm to cluster customers and recommend popular items in each cluster

Question 10

What is the primary difference between narrow AI and general AI?

Accepted Answer

Narrow AI is designed for a specific task, while general AI can understand and reason across a wide range of tasks. Narrow AI excels at specific tasks, while general AI would possess human-like cognitive abilities across many domains. General AI does not yet exist. The other options confuse definitions.

Answer

Narrow AI is rule-based, while general AI uses machine learning

Answer

Narrow AI requires labeled data, while general AI can learn without data

Answer

Narrow AI can perform any intellectual task, while general AI is limited to one task

Question 11

A team trains a model to predict customer lifetime value (CLV) using CRM data. The model's predictions are way off for new customers who have only been with the company for a month. Which factor is MOST likely contributing to this issue?

Accepted Answer

The model is overfitting to customers with long purchase histories. The model likely overfits to patterns from customers with longer histories, and new customers have limited features. Underfitting would cause overall poor performance. Data leakage might give unrealistic information. High precision doesn't cause inaccurate predictions.

Answer

The model has high precision but low recall

Answer

The model is underfitting

Answer

The training data includes future transactions (data leakage)

Question 12

A company wants to use AI to automatically extract invoice numbers, dates, and totals from scanned invoices. Which AI capability is MOST relevant?

Accepted Answer

Computer vision with entity extraction. Computer vision can process images of documents, and entity extraction (often part of NLP) identifies specific data points. Sentiment analysis is for emotions, intent detection for purpose, and generative AI for content creation.

Answer

Intent detection

Answer

Generative AI to create invoices

Answer

Sentiment analysis

Question 13

A data scientist notices that a churn prediction model has high variance: small changes in training data cause large changes in predictions. Which technique is BEST to address this?

Accepted Answer

Apply regularization techniques. High variance indicates overfitting. Regularization (e.g., L1/L2) reduces model complexity and variance. Adding more features would increase variance, increasing training data can help but is not always feasible, and reducing training data would worsen the problem.

Answer

Reduce the amount of training data

Answer

Add more features to the model

Answer

Increase the number of training epochs

Question 14

Which statement best describes 'inference' in the context of machine learning?

Accepted Answer

The process of using a trained model to make predictions on new data. Inference is the process of using a trained model to make predictions on new data. Training is the learning phase, and evaluation is assessing performance. Data collection is separate.

Answer

The process of training a model on labeled data

Answer

The process of collecting and preparing data

Answer

The process of evaluating a model's accuracy

Question 15

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