AI Associate AI Fundamentals — Timed 25 Questions

Question 1

A marketing manager wants to predict which customers are most likely to respond to a new email campaign. Which type of machine learning is most appropriate?

Accepted Answer

Supervised learning. Supervised learning uses labeled historical data (e.g., past response data) to predict a specific outcome (respond vs. not respond).

Answer

Generative AI

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Unsupervised learning

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Reinforcement learning

Question 2

A sales team notices that their lead scoring model assigns high scores to leads that rarely convert. The model was trained on data from the past 5 years. What is the most likely cause?

Accepted Answer

There is concept drift because buying behaviors have changed. Concept drift occurs when the relationship between features and labels changes over time. Old data may no longer represent current sales patterns.

Answer

The features used are irrelevant to lead conversion

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The model is overfitting to noise in the training data

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The model is underfitting and needs more features

Question 3

A customer support center wants to automatically route incoming cases to the appropriate department based on the issue description. Which NLP task is most relevant?

Accepted Answer

Text classification. Text classification assigns a category (e.g., billing, technical) to a text, enabling automated routing.

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Intent detection

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Entity extraction

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Sentiment analysis

Question 4

A data scientist is training a model to predict churn. The model achieves 99% accuracy on training data but only 60% on test data. Which issue is most likely occurring?

Accepted Answer

Overfitting. Overfitting: the model learns training data patterns too well, including noise, failing to generalize to new data.

Answer

Concept drift

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Data leakage

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Underfitting

Question 5

A company’s AI model recommends products to customers. The team wants to measure how often the recommended products are actually purchased. Which metric is most appropriate?

Accepted Answer

Precision. Precision measures the proportion of recommended items that are relevant (purchased). For product recommendations, precision is key.

Answer

F1 score

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Recall

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Accuracy

Question 6

A financial services firm uses an AI model to approve loan applications. They discover that the model denies loans at a higher rate for a certain demographic group, even when financial indicators are similar. What is the primary ethical concern?

Accepted Answer

Bias in training data. Bias in training data leads to unfair outcomes for certain groups. This is a fairness and bias issue.

Answer

Low model accuracy

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Model overfitting

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Hallucination

Question 7

A company uses generative AI to create personalized email content for each customer. They notice that occasionally the AI produces content that is factually incorrect. What is this phenomenon called?

Accepted Answer

Hallucination. Hallucination occurs when generative models produce plausible-sounding but incorrect information.

Answer

Overfitting

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Concept drift

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Bias

Question 8

A retailer wants to use computer vision to automatically identify products from images uploaded by customers for a return process. Which computer vision task is required?

Accepted Answer

Product recognition. Product recognition is a specific computer vision task that identifies objects (products) in images.

Answer

Object detection

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Image classification

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Text extraction (OCR)

Question 9

A CRM team wants to predict the expected revenue from each opportunity. The data includes opportunity amount, close date, stage, and historical win rates. Which type of AI is best suited?

Accepted Answer

Predictive AI. Predictive AI uses historical data to forecast outcomes. Regression (a type of predictive AI) predicts a continuous value like revenue.

Answer

Unsupervised learning

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Generative AI

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Reinforcement learning

Question 10

A data analyst is preparing data for a machine learning model. They notice that many records have missing values for the 'industry' field. What is the best first step?

Accepted Answer

Replace missing values with a placeholder or impute them. Data quality is critical. Missing values must be handled (imputed or removed) to avoid garbage-in-garbage-out.

Answer

Delete all records with missing values

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Ignore the missing values and train the model as is

Answer

Use a model that automatically handles missing data

Question 11

A company uses an NLP model to detect customer intent from chat messages. The model correctly identifies 'billing question' 90% of the time for actual billing questions, but also flags many non-billing messages as billing (false positives). Which metric should the team prioritize to reduce false alarms?

Accepted Answer

Precision. Precision focuses on the proportion of positive identifications that are correct; improving precision reduces false positives.

Answer

Accuracy

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F1 score

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Recall

Question 12

A sales director wants to implement lead scoring but has no historical data on which leads converted. What approach can the team use to start?

Accepted Answer

Use unsupervised learning to cluster leads into segments and score based on cluster characteristics. Unsupervised learning can cluster leads based on similarities, providing initial scores without labeled outcomes.

Answer

Train a supervised model using assumptions as labels

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Skip lead scoring until enough conversion data is collected

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Use reinforcement learning to learn scoring from sales team feedback

Question 13

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

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R-squared

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Mean squared error

Question 14

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

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Use accuracy as the primary metric

Question 15

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.

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Increase the model's temperature setting to encourage creativity

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Disable all content filtering to avoid blocking useful output

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Use the model's output directly without checking

Question 16

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

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Deep learning

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Reinforcement learning

Question 17

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

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Data leakage

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Bias in training data

Question 18

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

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Reinforcement learning

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Computer vision

Question 19

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

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Accuracy

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F1 score

Question 20

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

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Hallucination in model predictions

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Overfitting to historical data