AI Associate Practice Test 44 — 15 Questions

Question 1

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

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Natural language processing to analyze lead emails

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Computer vision to analyze lead profile pictures

Question 2

A sales operations manager wants to use Einstein GPT for Sales to improve rep productivity. Which THREE tasks can Sales GPT perform?

Accepted Answer

Summarize sales calls. Einstein GPT for Sales includes a call summarization feature that uses generative AI to automatically create concise summaries of sales calls from transcripts. This directly improves rep productivity by saving time on manual note-taking and capturing key action items.

Answer

Create sales dashboards

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Predict lead scores

Question 3

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?

Accepted Answer

Intent detection. Intent detection is the NLP task that identifies what the user wants to do. Improving it directly addresses misinterpretation of requests.

Answer

Text generation

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Sentiment analysis

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Named entity recognition

Question 4

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

Accepted Answer

Many high-quality leads are not followed up. High precision means most leads flagged as hot are indeed hot, but low recall means many actual hot leads are missed, causing lost opportunities.

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The model is overfitted to training data

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Sales team spends time on many leads that never convert

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The model has high bias

Question 5

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.)

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

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

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Remove all emails that contain spelling errors

Question 6

A company wants to automatically categorize incoming support cases into Type, Priority, and Reason fields. They have historical data with these fields populated. Which Einstein feature should they use?

Accepted Answer

Einstein Case Classification. Einstein Case Classification is the correct feature because it is specifically designed to automatically categorize incoming support cases into fields like Type, Priority, and Reason using historical data. It uses machine learning models trained on past case records to predict the most likely values for these fields, enabling automated routing and prioritization without manual rules.

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Einstein Prediction Builder

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Einstein Article Recommendations

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Einstein Next Best Action

Question 7

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

Accepted Answer

The model produces outputs that are not grounded in the training data or reality. Hallucination is when a generative AI model produces content that is factually incorrect or nonsensical but presented as if it were true.

Answer

The model only works when the user prompts it with specific keywords

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The model has a bug in the training algorithm

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The model generates creative but accurate content

Question 8

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?

Accepted Answer

The model is overfitting the training data. Large gap between training accuracy and validation accuracy is a classic sign of overfitting. The model memorized training data and fails to generalize.

Answer

The validation set contains data leakage

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The model needs more features

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The model is underfitting the training data

Question 9

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.

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Super AI

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Reactive AI

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General AI

Question 10

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?

Accepted Answer

Supervised learning (classification). Lead scoring is a binary classification problem (convert or not) using historical labeled data, which is supervised learning.

Answer

Generative AI

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Reinforcement learning

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Unsupervised learning (clustering)

Question 11

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.

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The model is underfitted

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The model uses too many features

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The model is overfitted

Question 12

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?

Accepted Answer

Implement retrieval-augmented generation (RAG) to ground outputs in verified data. Grounding the model with retrieval, adding a human review step, and adjusting temperature can reduce hallucinations. Training on more data alone may not fix the issue.

Answer

Train the model on more unlabeled emails from the internet

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Remove all safety guardrails to allow more creative content

Question 13

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?

Accepted Answer

Optical Character Recognition (OCR). Computer vision reads the document, OCR converts image to text, and NLP extracts entities. Generative AI is not needed.

Answer

Generative AI for text generation

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Sentiment analysis

Question 14

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

Accepted Answer

Ensure training data does not contain historical biases against certain demographics. Bias in training data can lead to unfair denials; transparency ensures customers understand AI decisions. Accuracy is important but not primarily an ethical issue.

Answer

Use as many data points as possible to improve predictions

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Replace all human loan officers with AI

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Maximize model accuracy at all costs

Question 15

A sales manager wants to use Einstein Activity Capture to log emails automatically. Which prerequisite must be met?

Accepted Answer

The user's email must be hosted on a supported platform (Gmail, Outlook).. Einstein Activity Capture requires that user emails be hosted on a supported platform (Gmail or Outlook/Exchange) because the feature uses server-side synchronization via APIs (Google Workspace APIs or Microsoft Graph) to automatically log emails and events into Salesforce. Without a supported email host, the service cannot connect to the mail server to capture activity data.

Answer

The org must be on Enterprise Edition or higher.

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The user must have an Einstein AI license.

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The user must manually enable email logging in personal settings.