AI Associate AI Fundamentals Practice Test 9 — 15 Questions

Question 1

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.)

Accepted Answer

Audit the training data for imbalanced representation across demographic groups. Auditing training data for bias and ensuring diverse representation helps mitigate bias. Using a complex model doesn't address bias; removing all negative examples would bias the model. Using historical data as-is may perpetuate existing biases.

Answer

Use a more complex model to capture nuanced patterns

Answer

Use only historical data as-is without modification

Answer

Remove all negative reviews from the training data to avoid negative bias

Question 2

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.)

Accepted Answer

The new data is noisy or of low quality. Poor data quality, irrelevant features, and model underfitting can all cause lack of improvement. Adding more data helps only if it's high-quality; more features can help if relevant, but not necessarily. Overfitting would show good training performance.

Answer

The model is already overfitting to the training data

Answer

The team has not added enough features to the model

Question 3

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.)

Accepted Answer

Intent detection. Text classification categorizes emails, intent detection identifies the purpose, and entity extraction can identify account numbers or products to aid routing. Sentiment analysis is about emotion, not routing. Generative AI creates responses, not needed for routing.

Answer

Sentiment analysis

Answer

Generative AI

Question 4

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?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. RAG (Retrieval-Augmented Generation) allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining. The other options either require expensive retraining for each update or lack document grounding.

Answer

Use a larger foundation model with a longer context window and paste all documents into each prompt

Answer

Train a custom model from scratch on the policy documents each month

Answer

Fine-tune a base LLM on the policy documents monthly

Question 5

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

Accepted Answer

Supervised learning. Supervised learning uses labeled data (historical churn outcomes) to train a model to predict future churn.

Answer

Reinforcement learning

Answer

Unsupervised learning

Answer

Self-supervised learning

Question 6

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?

Accepted Answer

Overfitting due to model complexity or insufficient regularization. Overfitting occurs when the model memorizes training data noise instead of learning generalizable patterns, leading to poor test performance.

Answer

Underfitting due to insufficient model complexity

Answer

Label noise in the training data

Answer

Data leakage from the test set into training

Question 7

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?

Accepted Answer

Supervised learning to build a lead scoring model. Lead scoring uses supervised learning on historical lead data to predict conversion probability.

Answer

Unsupervised clustering to group leads by similarity

Answer

Natural language processing to analyze lead emails

Answer

Computer vision to analyze lead profile pictures

Question 8

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

Accepted Answer

Narrow AI is task-specific; general AI can perform any intellectual task a human can. Narrow AI is designed for specific tasks, while general AI (AGI) would have human-like broad capabilities, which does not yet exist.

Answer

Narrow AI can perform any intellectual task; general AI is limited to one task

Answer

Narrow AI uses machine learning; general AI uses deep learning

Answer

General AI is already deployed in CRM systems; narrow AI is experimental

Question 9

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?

Accepted Answer

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

Answer

Intent detection

Answer

Sentiment analysis

Answer

Named entity recognition

Question 10

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

Accepted Answer

The sales team misses some convertible opportunities. High precision means few false positives (won deals predicted correctly), but low recall means many actual wins are missed. The sales team will likely miss out on some convertible opportunities.

Answer

The model is overfitting to the training data

Answer

The sales team wastes time on many low-quality opportunities

Answer

The model overestimates the likelihood of conversion for most opportunities

Question 11

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

Accepted Answer

Bias in training data may lead to unfair treatment of certain customer groups. Bias in training data can lead to unfair or discriminatory outcomes, a key ethical concern in AI.

Answer

AI models always improve over time without intervention

Answer

AI models require no human oversight

Answer

Deep learning is too complex for business use

Question 12

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

Accepted Answer

Supervised learning to predict purchase probability for each product. Product recommendations are typically handled by supervised or unsupervised learning, but the most common approach in CRM is collaborative filtering or similar supervised/unsupervised models. However, given options, supervised learning on historical interactions is typical.

Answer

Reinforcement learning with real-time rewards

Answer

Natural language generation to create product descriptions

Answer

Computer vision to analyze product images

Question 13

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?

Accepted Answer

The training data was not representative of the new segments. If the training data was not representative of the new segments, the model will not generalize, leading to poor out-of-sample performance.

Answer

Feature engineering was insufficient for the original segments

Answer

The model is overfitting to the training data

Answer

The model architecture is too simple

Question 14

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

Accepted Answer

Entity extraction. Entity extraction (named entity recognition) identifies specific pieces of information such as dates, order numbers, and names from text.

Answer

Sentiment analysis

Answer

Text classification

Answer

Text summarization

Question 15

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

Accepted Answer

Hallucination. Hallucination refers to when a generative AI model fabricates information that seems plausible but is false.

Answer

Bias

Answer

Overfitting

Answer

Underfitting