AI Associate AI Fundamentals — Timed 15 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

Answer

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 Old data may no longer represent current sales patterns.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.

Answer

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

Answer

Data leakage

Answer

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

Answer

Generative AI

Answer

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

Answer

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

Answer

R-squared

Answer

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.

Answer

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