CCNA Sfai Ai Fundamentals Questions — Page 2 of 2

MCQmedium

A company uses AI to automatically categorize customer support tickets into 'Billing', 'Technical', or 'General'. The model is trained on thousands of past tickets labeled by agents. What type of AI task is this?

A.Intent detection

B.Sentiment analysis

C.Named entity recognition

D.Text classification

AnswerD

Text classification assigns predefined labels to text, exactly matching the ticket categorization use case.

Why this answer

This is a text classification problem using supervised learning, where each ticket is assigned to a predefined category based on labeled training data.

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Multi-Selecteasy

A sales team wants to use AI to get product recommendations for customers. Which TWO types of machine learning could be used?

Select 2 answers

A.Generative AI

B.Reinforcement learning

C.Computer vision

D.Unsupervised learning

E.Supervised learning

AnswersD, E

Can cluster customers with similar behavior to recommend products others in the cluster bought.

Why this answer

Unsupervised learning can find patterns (e.g., customer segments) and supervised learning can predict likelihood to buy, both enabling recommendations.

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MCQhard

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?

A.The model has high precision but low recall

B.The model is overfitting to customers with long purchase histories

C.The model is underfitting

D.The training data includes future transactions (data leakage)

AnswerB

Overfitting can cause the model to rely on patterns that only appear in long-term data, leading to poor predictions for new customers with short histories.

Why this answer

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.

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Multi-Selectmedium

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?

Select 2 answers

A.Increase the model's temperature setting to encourage creativity

B.Disable all content filtering to avoid blocking useful output

C.Always have a human review the generated content before publishing

D.Ground the model with a knowledge base of verified facts

E.Use the model's output directly without checking

AnswersC, D

Human oversight catches hallucinations and inaccuracies.

Why this answer

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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MCQmedium

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?

A.Generative AI

B.Reinforcement learning

C.Unsupervised learning (clustering)

D.Supervised learning (classification)

AnswerD

Correct: supervised classification uses labeled conversion outcomes to predict new leads.

Why this answer

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

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MCQeasy

A company uses an AI model to classify customer support cases into categories. The model often misclassifies cases from a specific region, leading to longer resolution times. What is the most likely cause?

A.The model is underfitted

B.The model uses too many features

C.The model is overfitted

D.The training data lacks diversity for that region

AnswerD

Machine learning models learn from data; if a region is underrepresented, the model may not learn its patterns.

Why this answer

If the training data is not representative of all regions, the model will perform poorly on underrepresented groups.

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MCQhard

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?

A.Low recall for minority groups

B.Bias in training data

C.Hallucination in model predictions

D.Overfitting to historical data

AnswerB

If historical loan decisions were biased against minority-owned businesses, the model would learn and perpetuate that bias.

Why this answer

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.

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MCQmedium

A sales team wants to prioritize leads that are most likely to convert. They have historical data on lead attributes and conversion outcomes. Which AI technique should be used?

A.Unsupervised clustering to group leads by similarity

B.Supervised learning to build a lead scoring model

C.Natural language processing to analyze lead emails

D.Computer vision to analyze lead profile pictures

AnswerB

Supervised learning uses labeled historical data to predict a target outcome, perfect for lead scoring.

Why this answer

Lead scoring uses supervised learning on historical lead data to predict conversion probability.

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MCQhard

A financial services firm uses an AI model to approve loan applications. They discover the model denies loans at a higher rate for a protected demographic. What is the most likely root cause?

A.The model is overfitted

B.The training data contains historical bias

C.The model uses too few features

D.The model has low precision

AnswerB

If historical loan decisions were biased, the model will learn that bias.

Why this answer

Historical bias in training data can cause models to learn and perpetuate discrimination.

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