AI Associate Practice Test 5 — 25 Questions

Question 1

A marketing team uses Einstein Send Time Optimization to determine the best time to send emails. To ensure the company follows Salesforce's Trusted AI principle of Honesty, which THREE practices should be adopted?

Accepted Answer

Tell recipients that the email timing was chosen by an AI algorithm. Honesty means being transparent about AI limitations and capabilities. Providing documentation, clearly communicating that predictions are AI-based, and explaining the influencing factors align with honesty. Automating without review and overpromising accuracy are contrary.

Answer

Automate all email sends without human intervention to maintain consistency

Answer

Claim the model is 100% accurate in predicting optimal send times

Question 2

A financial services firm uses Einstein Discovery to predict loan default risk. To comply with data minimisation principles and avoid using sensitive PII unnecessarily, which TWO actions should the data science team take?

Accepted Answer

Remove demographic fields such as race and gender from the training dataset. Data minimisation means using only relevant fields. Removing unnecessary PII features and performing feature selection reduces risk. Auditing for bias and enabling toxicity detection are good practices but not directly about minimisation.

Answer

Include all available fields to maximize model accuracy

Answer

Use feature engineering to derive synthetic attributes that correlate with protected attributes

Answer

Enable Einstein Trust Layer's toxicity detection on the model inputs

Question 3

A customer service director wants to implement an AI-powered chat assistant that can answer common questions. To align with Salesforce's Trusted AI principle of Empathy, which THREE design choices should the director make?

Accepted Answer

Ensure the bot can detect user frustration and escalate to a human agent. Empathy means designing for human benefit, including user-centric design, fallback to humans, and considering emotional impact. Automating all responses and using a formal tone are less empathetic.

Answer

Automate 100% of interactions to provide instant responses

Answer

Design the bot to always provide a direct answer without asking clarifying questions

Question 4

A retail company uses Einstein Article Recommendations to suggest knowledge articles to customer service agents. To ensure compliance with the Salesforce Trusted AI principle of Safety, which THREE measures should the company implement?

Accepted Answer

Use grounding to connect the AI recommendations to the company's CRM data. Safety involves preventing harmful outputs. Human review, toxicity detection, and using grounding to ensure relevance all mitigate harmful recommendations. Hiding the model's confidence score reduces transparency, and logging without review does not prevent harm.

Answer

Log all recommendations without setting up any review process

Answer

Hide the model's confidence score from agents to avoid over-reliance

Question 5

A company uses Einstein Prediction Builder to forecast customer churn. The data science team discovers that the model is heavily influenced by a field containing the customer's income, which the company legally cannot use for automated decisions in certain jurisdictions. Which TWO steps should the team take to address this ethical and compliance issue?

Accepted Answer

Re-evaluate feature importance to ensure no other fields act as proxies for income. Removing the prohibited field and re-evaluating feature importance aligns with data minimisation and fairness. Auditing for bias is good but not sufficient if the field is still used. Retraining without removal does not address the legal issue.

Answer

Audit the model for bias against different income groups

Answer

Ignore the issue because the model is used internally

Answer

Retain the income field but add a note that it may not be used in certain regions

Question 6

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?

Accepted Answer

Supervised learning. Supervised learning uses labeled historical data (e.g., past response data) to predict a specific outcome (respond vs. not respond).

Answer

Generative AI

Answer

Unsupervised learning

Answer

Reinforcement learning

Question 7

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?

Accepted Answer

Overfitting. Overfitting: the model learns training data patterns too well, including noise, failing to generalize to new data.

Answer

Concept drift

Answer

Data leakage

Answer

Underfitting

Question 8

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 9

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 10

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?

Accepted Answer

Supervised learning. Supervised learning uses labeled historical data (won/lost outcomes) to predict future outcomes, making it the best fit for lead scoring.

Answer

Generative AI

Answer

Unsupervised learning

Answer

Reinforcement learning

Question 11

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?

Accepted Answer

There is concept drift because buying behaviors have changed. Concept drift occurs when the relationship between features and labels changes over time. Old data may no longer represent current sales patterns.

Answer

The features used are irrelevant to lead conversion

Answer

The model is overfitting to noise in the training data

Answer

The model is underfitting and needs more features

Question 12

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?

Accepted Answer

Predictive AI. Predictive AI uses historical data to forecast outcomes. Regression (a type of predictive AI) predicts a continuous value like revenue.

Answer

Unsupervised learning

Answer

Generative AI

Answer

Reinforcement learning

Question 13

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?

Accepted Answer

Text classification. Text classification assigns a category (e.g., billing, technical) to a text, enabling automated routing.

Answer

Intent detection

Answer

Entity extraction

Answer

Sentiment analysis

Question 14

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?

Accepted Answer

Recall. Recall (true positive rate) measures how many actual churners are correctly identified. High accuracy with low recall indicates the model is biased toward the majority class (non-churners). Precision focuses on how many flagged churners actually churn, F1 is a balance, and accuracy is already high.

Answer

Precision

Answer

Accuracy

Answer

F1 score

Question 15

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?

Accepted Answer

Precision. Precision measures the proportion of recommended items that are relevant (purchased). For product recommendations, precision is key.

Answer

F1 score

Answer

Recall

Answer

Accuracy

Question 16

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?

Accepted Answer

Bias in training data. Bias in training data leads to unfair outcomes for certain groups. This is a fairness and bias issue.

Answer

Low model accuracy

Answer

Model overfitting

Answer

Hallucination

Question 17

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?

Accepted Answer

Use unsupervised learning to cluster leads into segments and score based on cluster characteristics. Unsupervised learning can cluster leads based on similarities, providing initial scores without labeled outcomes.

Answer

Train a supervised model using assumptions as labels

Answer

Skip lead scoring until enough conversion data is collected

Answer

Use reinforcement learning to learn scoring from sales team feedback

Question 18

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

Accepted Answer

Computer vision with entity extraction. Computer vision can process images of documents, and entity extraction (often part of NLP) identifies specific data points. Sentiment analysis is for emotions, intent detection for purpose, and generative AI for content creation.

Answer

Intent detection

Answer

Generative AI to create invoices

Answer

Sentiment analysis

Question 19

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?

Accepted Answer

Hallucination. Hallucination occurs when generative models produce plausible-sounding but incorrect information.

Answer

Overfitting

Answer

Concept drift

Answer

Bias

Question 20

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?

Accepted Answer

Undersample the non-churn class. Oversampling minority class, undersampling majority class, and using appropriate metrics (like precision/recall) are common approaches. Using accuracy is not recommended. Training on original data without adjustment will produce a biased model.

Answer

Train the model on the original dataset without changes

Answer

Use accuracy as the primary metric