AI Associate AI Fundamentals Practice Test 2 — 15 Questions

Question 1

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 2

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 3

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 4

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 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 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 7

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 8

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 9

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 10

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 11

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 12

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 13

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 14

A sales team uses an AI model to prioritize leads. The model's predictions are not improving despite adding more data. Which THREE factors could explain this? (Choose three.)

Accepted Answer

The new data is noisy or of low quality. Poor data quality, irrelevant features, and model underfitting can all cause lack of improvement. Adding more data helps only if it's high-quality; more features can help if relevant, but not necessarily. Overfitting would show good training performance.

Answer

The model is already overfitting to the training data

Answer

The team has not added enough features to the model

Question 15

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