AI Associate Practice Test 41 — 15 Questions

Question 1

Which THREE of the following are required when setting up a data stream from Salesforce to Data Cloud?

Accepted Answer

Data Stream object definition. A is correct because a Data Stream object definition is required to specify the schema and fields for the data being ingested from Salesforce into Data Cloud. Without this definition, Data Cloud cannot interpret the structure of the incoming records, making it impossible to map or transform the data.

Answer

Data Transform

Answer

Data Action

Question 2

Which statement best describes 'inference' in the context of machine learning?

Accepted Answer

The process of using a trained model to make predictions on new data. Inference is the process of using a trained model to make predictions on new data. Training is the learning phase, and evaluation is assessing performance. Data collection is separate.

Answer

The process of training a model on labeled data

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The process of collecting and preparing data

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The process of evaluating a model's accuracy

Question 3

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

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Ignore the missing values and train the model as is

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Use a model that automatically handles missing data

Question 4

A company wants to use AI to analyze customer feedback from surveys and social media. Which TWO capabilities are most relevant?

Accepted Answer

Sentiment analysis. Sentiment analysis detects overall sentiment (positive/negative), and text classification can categorize feedback by topic. Entity extraction might be useful but is less central for broad feedback analysis.

Answer

Speech recognition

Answer

Image recognition

Answer

Predictive lead scoring

Question 5

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 6

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

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Unsupervised learning

Answer

Reinforcement learning

Question 7

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 8

A sales operations team wants to automatically categorize incoming support cases into predefined categories (e.g., Billing, Technical, General). The team has thousands of historical cases with correct category labels. Which AI approach should they use?

Accepted Answer

Text classification. Text classification is a supervised NLP task that assigns predefined labels to text. Historical labeled cases provide the training data for a classifier.

Answer

Topic modeling (unsupervised)

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Sentiment analysis

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Named entity recognition

Question 9

You are an admin at a financial services firm. The firm wants to use Einstein Next Best Action to offer personalized product recommendations to customers on its service portal. The data includes customer profiles, transaction history, and support case history. The Einstein Next Best Action strategy is configured with a recommendation that shows a 'Savings Account' offer to customers who have a checking account. However, the recommendation is not appearing for any customers. You check the Data Flow and see that the 'Account' object data is flowing correctly. The recommendation's filter condition is: AND( Has_Checking_Account__c = true, Age__c > 18 ). You verify that many customers meet these conditions. What is the most likely reason the recommendation is not appearing?

Accepted Answer

The recommendation is not activated or published. The most likely reason the recommendation is not appearing is that it has not been activated or published. In Einstein Next Best Action, recommendations must be explicitly activated or published to become available for serving to customers; configuration alone does not make them live. Since the data flow is correct and the filter conditions are valid, the missing activation step is the typical cause of a recommendation not showing.

Answer

The 'Account' object is not supported by Einstein Next Best Action

Answer

The customer data is not being refreshed in real time

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The filter condition is syntactically incorrect

Question 10

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

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Natural language generation to create product descriptions

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Computer vision to analyze product images

Question 11

A company uses an AI model to predict customer churn. The model's predictions are used to automatically assign discounts to high-risk customers. A customer complains about receiving a discount offer they did not request. Which ethical concern is most relevant?

Accepted Answer

Lack of transparency of AI decisions to customers. Transparency about AI decisions is key. Customers should be informed when an AI system is making decisions that affect them.

Answer

Bias in training data

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Model overfitting

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Data quality

Question 12

A sales operations manager wants to predict which leads are most likely to convert to deals. The CRM has historical data on thousands of leads with outcomes (converted or not). Which type of machine learning should they use?

Accepted Answer

Supervised learning. Supervised learning uses labeled data (past leads with known outcomes) to predict future lead conversion. Unsupervised learning finds patterns without labels, reinforcement learning learns from rewards, and deep learning is a subset of supervised/unsupervised but not the most specific answer here.

Answer

Unsupervised learning

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Deep learning

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Reinforcement learning

Question 13

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

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Overfitting

Answer

Bias

Question 14

A company wants to provide personalized product recommendations on their community site built with Experience Cloud. Which Einstein feature should they use?

Accepted Answer

Einstein Recommendation Builder. Einstein Recommendation Builder enables product and content recommendations for Experience Cloud sites.

Answer

Einstein Prediction Builder

Answer

Einstein Vision

Answer

Einstein Next Best Action

Question 15

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)