AI Associate AI Fundamentals Practice Test 11 — 15 Questions

Question 1

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.)

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Split the data into training and test sets. Splitting data into train/test sets allows evaluation of generalization. Ensuring balanced representation of categories prevents bias towards majority classes.

Answer

Train the model until it achieves 100% accuracy on the training set

Answer

Use only the most recent 1,000 emails for training to reflect current trends

Answer

Remove all emails that contain spelling errors

Question 2

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.)

Accepted Answer

Use F1 score as the evaluation metric. Class imbalance can be addressed by resampling (oversampling minority, undersampling majority), using class weights, or using metrics like F1 that consider precision and recall.

Answer

Use a larger dataset even if it is more imbalanced

Answer

Use accuracy as the primary evaluation metric

Question 3

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.)

Accepted Answer

Including seasonality and trend components from historical data. Historical patterns (seasonality), external factors (holidays), and data quality are key for time series forecasting. Model complexity and algorithm brand are less critical.

Answer

Choosing the most popular AI algorithm on the market

Answer

Using a deep learning model instead of a simpler model

Question 4

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?

Accepted Answer

Supervised learning. Supervised learning uses labeled data (historical outcomes) to predict future outcomes, making it ideal for lead scoring.

Answer

Unsupervised learning

Answer

Generative AI

Answer

Reinforcement learning

Question 5

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?

Accepted Answer

The training data lacks diversity for that region. If the training data is not representative of all regions, the model will perform poorly on underrepresented groups.

Answer

The model is underfitted

Answer

The model uses too many features

Answer

The model is overfitted

Question 6

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?

Accepted Answer

Predictive AI for opportunity scoring. Predictive AI uses historical data to forecast outcomes, making it ideal for opportunity scoring.

Answer

Generative AI to create new sales content

Answer

Unsupervised learning to cluster opportunities

Answer

Reinforcement learning for sales strategy

Question 7

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?

Accepted Answer

Overfitting. Overfitting occurs when a model learns training data too well, including noise, and fails to generalize to new data.

Answer

Data leakage

Answer

Underfitting

Answer

Bias in the training data

Question 8

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?

Accepted Answer

Text classification. Text classification categorizes text into predefined classes, enabling automated routing.

Answer

Sentiment analysis

Answer

Entity extraction

Answer

Intent detection

Question 9

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?

Accepted Answer

Supervised learning. Supervised learning can use user purchase history as labels to predict what a specific user might buy, enabling personalization.

Answer

Generative AI

Answer

Unsupervised learning

Answer

Reinforcement learning

Question 10

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

Accepted Answer

Many false negatives for high-value customers. Low recall means many high-value customers are missed, leading to lost opportunities to retain them.

Answer

The model is overfitted

Answer

Many false positives for high-value customers

Answer

High accuracy on low-value customers

Question 11

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?

Accepted Answer

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

Answer

The model is overfitted

Answer

The model uses too few features

Answer

The model has low precision

Question 12

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

Accepted Answer

Hallucination. Hallucination is when an AI generates incorrect or nonsensical information.

Answer

Data leakage

Answer

Overfitting

Answer

Bias

Question 13

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?

Accepted Answer

Add blurry images to the training data. Augmenting training data with blurry images helps the model learn to handle various quality levels.

Answer

Decrease the learning rate

Answer

Use a larger batch size

Answer

Increase the model's number of layers

Question 14

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?

Accepted Answer

The training data is predominantly English. If the training data lacks Spanish examples, the model cannot learn Spanish patterns.

Answer

The model is overfitted to English

Answer

The inference threshold is too high

Answer

The model uses the wrong algorithm

Question 15

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

Accepted Answer

Narrow AI. Narrow AI, also called weak AI, is specialized for one task.

Answer

Super AI

Answer

Reactive AI

Answer

General AI