AI Associate AI Fundamentals Practice Test 10 — 15 Questions

Question 1

A company wants to use AI to analyze customer feedback from surveys and social media. Which TWO capabilities are most relevant?

Accepted Answer

Sentiment analysis. Sentiment analysis detects overall sentiment (positive/negative), and text classification can categorize feedback by topic. Entity extraction might be useful but is less central for broad feedback analysis.

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Speech recognition

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Image recognition

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Predictive lead scoring

Question 2

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.

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High number of model parameters

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Large dataset size

Question 3

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.

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Generative AI is best suited for lead scoring

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Both predictive and generative AI rely on the same algorithms

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Predictive AI can generate realistic customer service responses

Question 4

Which type of machine learning is used when a model is trained on historical sales data that includes both input features and the known outcome (e.g., closed won/lost) to predict whether a new lead will convert?

Accepted Answer

Supervised learning. Supervised learning uses labeled training data where the correct output is provided. Lead scoring with historical outcomes is a classic supervised learning task.

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Semi-supervised learning

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Reinforcement learning

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Unsupervised learning

Question 5

A sales operations team wants to automatically categorize incoming support cases into predefined categories (e.g., Billing, Technical, General). The team has thousands of historical cases with correct category labels. Which AI approach should they use?

Accepted Answer

Text classification. Text classification is a supervised NLP task that assigns predefined labels to text. Historical labeled cases provide the training data for a classifier.

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Topic modeling (unsupervised)

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Sentiment analysis

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Named entity recognition

Question 6

A data scientist trains a churn prediction model on CRM data that includes customer tenure, support ticket count, and last purchase date. The model achieves 95% accuracy on training data but only 60% on a holdout validation set. What is the most likely issue?

Accepted Answer

The model is overfitting the training data. Large gap between training accuracy and validation accuracy is a classic sign of overfitting. The model memorized training data and fails to generalize.

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The validation set contains data leakage

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The model needs more features

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The model is underfitting the training data

Question 7

A company wants to use AI to automatically extract key information (e.g., invoice number, date, total amount) from scanned PDF invoices. Which AI capability should they use?

Accepted Answer

Computer vision with optical character recognition (OCR). Document scanning combined with OCR and NLP entity extraction can parse structured fields from documents. This falls under computer vision (OCR) and NLP.

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Generative AI for text generation

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Predictive analytics

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Sentiment analysis

Question 8

A marketing team wants to recommend products to customers based on their past purchases and browsing behavior. Which type of AI is most appropriate?

Accepted Answer

Predictive AI. Product recommendation is a predictive AI task that uses historical data to predict which products a customer is likely to purchase.

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Generative AI

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Reinforcement learning

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Unsupervised learning

Question 9

A customer service chatbot misinterprets user requests and often provides irrelevant answers. The development team wants to improve the chatbot's understanding of user intent. Which NLP component should they focus on?

Accepted Answer

Intent detection. Intent detection is the NLP task that identifies what the user wants to do. Improving it directly addresses misinterpretation of requests.

Answer

Text generation

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Sentiment analysis

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Named entity recognition

Question 10

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

Accepted Answer

Many high-quality leads are not followed up. High precision means most leads flagged as hot are indeed hot, but low recall means many actual hot leads are missed, causing lost opportunities.

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The model is overfitted to training data

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Sales team spends time on many leads that never convert

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The model has high bias

Question 11

Which type of AI is designed to perform only a specific task, such as playing chess or recommending products?

Accepted Answer

Narrow AI. Narrow AI (weak AI) is specialized for one task and cannot generalize beyond its training.

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Super AI

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General AI

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Strong AI

Question 12

A company uses an AI model to predict customer churn. The model's predictions are used to automatically assign discounts to high-risk customers. A customer complains about receiving a discount offer they did not request. Which ethical concern is most relevant?

Accepted Answer

Lack of transparency of AI decisions to customers. Transparency about AI decisions is key. Customers should be informed when an AI system is making decisions that affect them.

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Bias in training data

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Model overfitting

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Data quality

Question 13

A sales director wants to use AI to prioritize leads that are most likely to convert. The company has historical data on leads that includes whether they converted (yes/no) and various attributes. Which machine learning type should be used?

Accepted Answer

Supervised learning (classification). Lead scoring is a binary classification problem (convert or not) using historical labeled data, which is supervised learning.

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Generative AI

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Reinforcement learning

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Unsupervised learning (clustering)

Question 14

A data scientist notices that a sentiment analysis model performs well on general product reviews but fails to correctly classify negative sentiment in industry-specific jargon (e.g., 'the API is flaky'). The most likely cause is:

Accepted Answer

The training data lacked examples of industry-specific language. The model was likely trained on general data and does not generalize to domain-specific language. This is a domain adaptation issue, which can be due to training data not being representative.

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The model is overfitted to training data

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The model architecture is too simple

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Sentiment analysis cannot handle jargon

Question 15

What does the term 'hallucination' refer to in the context of generative AI?

Accepted Answer

The model produces outputs that are not grounded in the training data or reality. Hallucination is when a generative AI model produces content that is factually incorrect or nonsensical but presented as if it were true.

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The model only works when the user prompts it with specific keywords

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The model has a bug in the training algorithm

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The model generates creative but accurate content