AI Fundamentals

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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 company wants to generate personalized product recommendations for each customer based on their purchase history and browsing behavior. Which approach is MOST appropriate?

What is the primary difference between narrow AI and general AI?

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 company wants to use AI to automatically extract invoice numbers, dates, and totals from scanned invoices. Which AI capability is MOST relevant?

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?

Which statement best describes 'inference' in the context of machine learning?

A company wants to generate personalized marketing email content for each customer, including product recommendations and tailored copy. Which AI approach is BEST?

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.)

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.)

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.)

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Which type of machine learning is used to predict customer churn based on historical labeled data?

A data scientist trains a lead scoring model that achieves 99% accuracy on training data but only 65% accuracy on a held-out test set. What is the most likely issue?

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?

What is the primary difference between narrow AI and general AI?

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

Which of the following is an ethical concern when using AI to make decisions about customers?

A retailer wants to recommend products to customers based on their purchase history and browsing behavior. Which AI approach is most suitable?

A model trained on CRM data predicts customer lifetime value (CLV) with high accuracy, but when deployed, predictions are significantly off for new customer segments. What is the most likely cause?

A company wants to automatically extract key information like order numbers and dates from customer emails. Which NLP technique should be used?

What is the term for when an AI model produces confident but incorrect information, often in generative AI?

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

A data scientist is building a churn prediction model. What THREE factors are most critical for model success?

Which TWO statements correctly describe predictive AI compared to generative AI?

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?

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?

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?

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?

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

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?

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

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

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?

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 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:

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

A company is deploying an AI model to automatically classify customer emails into categories (Complaint, Inquiry, Feedback). They have 10,000 labeled emails. Which TWO actions are essential to ensure the model's accuracy? (Select TWO.)

A financial services firm wants to use AI to detect fraudulent transactions. They have a dataset with 1% fraudulent and 99% legitimate transactions. Which THREE actions should they take to address class imbalance? (Select THREE.)

A retail company wants to use AI to predict next month's sales for each product category. They have five years of monthly sales data. Which THREE factors are most critical for the accuracy of the predictive model? (Select THREE.)

A marketing team wants to use AI to predict which leads are most likely to convert. The CRM contains historical lead data with conversion outcomes. Which type of machine learning should be used?

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 sales manager wants to predict which deals are likely to close this quarter. The CRM has rich historical data on won/lost opportunities, deal amount, and sales stage. Which AI approach is best suited for this task?

A data scientist trains a model to predict customer churn. The model achieves 98% accuracy on training data but only 72% on test data. What issue is most likely occurring?

A customer service team wants to automatically route incoming emails to the appropriate department based on content. Which NLP capability is essential for this task?

An e-commerce company uses AI to provide product recommendations. The model suggests popular items but fails to personalize for individual users. Which type of learning could improve personalization?

A model predicts customer lifetime value with high precision but low recall on high-value customers. What is the business impact?

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 generative AI chatbot sometimes produces factually incorrect responses about a company's products. What is this phenomenon called?

A company uses computer vision to scan receipts for expense reporting. The model performs well on high-resolution scans but poorly on blurry photos. Which improvement is most effective?

A company uses an NLP model to detect intent in customer messages. The model works well for English but fails for Spanish messages. What is the most likely cause?

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

A company wants to use AI to reduce customer churn. Which TWO approaches are most appropriate? (Select 2)

Which THREE factors are most important for ensuring the accuracy of an AI model in a CRM context? (Select 3)

A company is deploying an AI chatbot for customer service. Which THREE ethical considerations should be addressed? (Select 3)

A sales operations manager wants to use AI to predict which leads are most likely to convert. The CRM has historical data on past leads, including whether they were won or lost, along with demographic and behavioral attributes. Which machine learning type should be used?

A data scientist evaluates a churn prediction model. On the test set, the model achieves 99% accuracy, but the business reports that the model rarely flags actual churners. Which metric should the data scientist focus on to improve the model?

A customer support team wants to automatically categorize incoming cases into predefined categories such as Billing, Technical, or Account. Which NLP task is most appropriate?

A CRM administrator is planning to implement predictive AI for lead scoring. Which TWO actions should be taken to ensure data quality?

A company uses AI to generate personalized email content for marketing campaigns. They notice the AI occasionally produces factually incorrect statements. Which THREE actions should they take to mitigate this?

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

A data scientist trains a model to predict customer churn. The model performs well on training data but poorly on test data. Which TWO issues are most likely?

A company wants to use AI to automatically extract key information (e.g., invoice number, date, total amount) from scanned invoices. Which THREE technologies should be combined?

A bank is implementing an AI system to approve small business loans. Which TWO ethical considerations should be addressed?