AI Associate AI Fundamentals Practice Test 7 — 15 Questions

Question 1

A service organization wants to use Einstein Reply Recommendations to suggest responses to customer chats. The feature is enabled, but agents report that no recommendations appear. The admin has ensured the permission set is assigned and the chat data is flowing. What should the admin examine next?

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

Accepted Answer

The language settings for the chat channels.. Einstein Reply Recommendations require the chat channel language to be set to a supported language (e.g., English, Spanish, French, German, Portuguese, or Japanese). If the language setting is unsupported or mismatched, the model cannot generate suggestions, even if permissions and data flow are correct. This is a common misconfiguration because the feature silently fails when the language is not recognized.

Answer

The number of closed cases with successful chat histories.

Answer

The model training schedule.

Answer

The user's profile settings for chat.

Question 2

A sales manager wants to implement Einstein Automated Contacts to automatically create contacts from email interactions. The admin enables the feature and assigns the permission set. However, no contacts are being created automatically. What is the most likely reason?

Accepted Answer

The admin did not set up the contact creation criteria in the Einstein Automated Contacts settings.. Einstein Automated Contacts requires the admin to explicitly define the criteria that trigger automatic contact creation from email interactions. Simply enabling the feature and assigning the permission set does not configure the rules for when and how contacts should be created. Without these criteria, the system has no instructions to act upon, so no contacts are generated.

Answer

The users have not logged any emails to Salesforce.

Answer

The feature requires at least 500 emails to be processed before it starts creating contacts.

Answer

The users need to install a plugin for their email client.

Question 3

A company has been using Einstein Lead Scoring for six months. Recently, the lead score confidence has dropped from 85% to 60%. The admin reviews the model and finds that many leads have missing data in custom fields used by the model. The admin also notices that field history tracking is not enabled on the Lead object. The lead volume is adequate with over 10,000 leads. What should the admin do to improve the model's confidence?

Accepted Answer

Enable field history tracking on the Lead object and retrain the model.. Field history tracking is required for Einstein Lead Scoring to capture changes over time, and retraining after enabling it will incorporate historical data. Option A is incorrect because manually updating all leads is impractical and doesn't address the root cause. Option C is incorrect because the lead volume is already adequate. Option D is incorrect because disabling and re-enabling will reset the model but not fix the missing field history.

Answer

Disable and re-enable Einstein Lead Scoring.

Answer

Increase the lead volume to at least 50,000 records.

Answer

Manually update all leads with missing data to have complete records.

Question 4

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

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Unsupervised learning

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Reinforcement learning

Question 5

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

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The model is overfitting to noise in the training data

Answer

The model is underfitting and needs more features

Question 6

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

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Entity extraction

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Sentiment analysis

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 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

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Recall

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Accuracy

Question 9

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 10

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

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Concept drift

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Bias

Question 11

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?

Accepted Answer

Product recognition. Product recognition is a specific computer vision task that identifies objects (products) in images.

Answer

Object detection

Answer

Image classification

Answer

Text extraction (OCR)

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 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?

Accepted Answer

Replace missing values with a placeholder or impute them. Data quality is critical. Missing values must be handled (imputed or removed) to avoid garbage-in-garbage-out.

Answer

Delete all records with missing values

Answer

Ignore the missing values and train the model as is

Answer

Use a model that automatically handles missing data

Question 14

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?

Accepted Answer

Precision. Precision focuses on the proportion of positive identifications that are correct; improving precision reduces false positives.

Answer

Accuracy

Answer

F1 score

Answer

Recall

Question 15

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