AI Associate AI Fundamentals Practice Test 5 — 15 Questions

Question 1

A retail company implements an AI chatbot to recommend products. After launch, they notice the chatbot frequently suggests expensive items to budget-conscious customers. Which AI bias is most likely occurring?

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

Accepted Answer

Sample bias (biased training data). The chatbot's frequent suggestion of expensive items to budget-conscious customers indicates that the training data was biased toward high-cost products, leading the model to learn and replicate that preference. This is a classic case of sample bias (biased training data), where the dataset does not accurately represent the target user population, causing systematic errors in the model's recommendations.

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Confirmation bias

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Anchoring bias

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Overconfidence bias

Question 2

What is the primary purpose of Einstein Studio in the Salesforce AI ecosystem?

Accepted Answer

Train, test, and deploy custom AI models using AutoML. Einstein Studio is specifically designed to allow users to train, test, and deploy custom AI models using AutoML, without requiring deep data science expertise. It provides a no-code interface for building models on Salesforce Data Cloud data, enabling predictive scoring and recommendations directly within the Salesforce ecosystem.

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Create and manage prompt templates

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Unify customer data from multiple sources

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Deploy pre-built Einstein GPT models

Question 3

A developer is implementing retrieval augmented generation (RAG) for a customer service bot. Which component is essential for supplying real-time data to the prompt?

Accepted Answer

A vector database for embedding and retrieval. The correct option is D, a vector database for embedding and retrieval, because RAG requires an external retrieval mechanism that stores document embeddings and performs similarity search to fetch relevant, up-to-date context that is injected into the prompt at inference time. This retrieval step is what supplies real-time or frequently changing data to the model, rather than relying solely on the model's pretrained parameters. A fine-tuned LLM (A) bakes knowledge into weights and cannot dynamically fetch live data, a prompt template in Prompt Builder (B) only structures the input and does not retrieve information, and a static knowledge base (C) is not updated in real time and lacks the embedding-based semantic retrieval needed for RAG.

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A fine-tuned large language model

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A prompt template in Prompt Builder

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A static knowledge base

Question 4

Which TWO of the following are ethical considerations when deploying AI in Salesforce?

Accepted Answer

Providing transparency in AI-generated outcomes. Transparency in AI-generated outcomes is a core ethical principle, especially in Salesforce's Einstein platform, where users must understand how predictions (e.g., lead scoring or opportunity insights) are made. Salesforce provides tools like 'Why This Prediction?' to explain model outputs, ensuring trust and accountability. Without transparency, users cannot validate or challenge AI decisions, leading to potential bias or misuse.

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Maximizing model complexity for better accuracy

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Using only historical data without review for biases

Question 5

Which THREE factors can affect the accuracy of an Einstein GPT response?

Accepted Answer

Model temperature setting. The model temperature setting directly controls the randomness of the output. A higher temperature (e.g., 0.9) produces more creative but potentially less accurate responses, while a lower temperature (e.g., 0.1) makes the model more deterministic and factual. This parameter is a core hyperparameter in large language models like those powering Einstein GPT, and it significantly influences response accuracy.

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Brightness of the user interface

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Length of the response generated

Question 6

Which TWO capabilities are available in Einstein GPT for Sales?

Accepted Answer

Summarizing call transcripts. Option D is correct because Einstein GPT for Sales includes the Sales Summaries / call summary capability, which uses generative AI to summarize call transcripts and surface key points and next steps for sellers. Option E is correct because Einstein GPT for Sales provides Sales Email, which generates personalized email drafts grounded in CRM data such as the recipient's account, opportunity, and prior activity. Option A is not a listed Einstein GPT for Sales capability; sentiment analysis of emails is not one of its generative features. Option B is not correct because automated lead enrichment is a separate data-enrichment function, not an Einstein GPT for Sales capability. Option C is not correct because generating Apex code is a developer-focused capability (e.g., Einstein for Developers), not part of Einstein GPT for Sales.

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Sentiment analysis of customer emails

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Automated lead enrichment

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Generating Apex code

Question 7

Refer to the exhibit. A developer configured a grounding policy for Einstein GPT. What is the effect of the fallbackBehavior set to 'USE_MODEL_KNOWLEDGE'?

Accepted Answer

The AI will rely on its pre-trained knowledge when no grounding data meets the relevance threshold. When fallbackBehavior is set to 'USE_MODEL_KNOWLEDGE', the Einstein GPT grounding policy instructs the AI to fall back to its pre-trained (model) knowledge if the retrieved grounding data does not meet the configured relevance threshold. This ensures the AI still generates a response based on its internal training rather than returning no answer or ignoring the policy entirely.

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The AI will return all retrieved grounding data in the response

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The AI will not generate a response if no grounding data is found

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The AI will ignore the grounding policy and only use model knowledge

Question 8

Refer to the exhibit. A data scientist built a model using training data where 80% of leads were won. The model achieved 80% accuracy. What is the main issue with this evaluation?

Accepted Answer

Accuracy is not a reliable metric because the data is imbalanced. Correct: Accuracy is misleading due to class imbalance; a model that always predicts 'won' would get 80% accuracy. Option A: Data size is fine. Option B: XGBoost is good for tabular data. Option D: Confidence score not provided.

Answer

The model lacks confidence scoring

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The algorithm choice (XGBoost) is inappropriate

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The training data size is insufficient

Question 9

A sales manager wants to automatically prioritize leads based on their likelihood to convert. Which Einstein feature should the admin enable?

Accepted Answer

Einstein Lead Scoring. Einstein Lead Scoring is the purpose-built Salesforce feature that uses AI to score leads based on historical conversion patterns, surfacing a score and key positive/negative factors directly on the lead record. Enabling it lets the sales manager prioritize leads by likelihood to convert without building a custom model.

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Einstein Activity Capture

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Einstein Prediction Builder

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Einstein Bot

Question 10

A company wants to use Einstein Discovery to analyze sales data and automatically uncover key drivers of deal closure. What must the admin provide to create a story?

Accepted Answer

At least one numeric field to predict. Einstein Discovery requires at least one numeric field as the prediction target (e.g., deal amount, probability score) to train its regression or classification model. Without a numeric field to predict, the story cannot define what outcome the AI should analyze or uncover key drivers for.

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A date field for time series analysis

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A foreign key to relate objects

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A text field for sentiment analysis

Question 11

A service team trains an Einstein Bot on historical chat transcripts. After deployment, the bot frequently fails to understand customer intents. Which action is most likely to improve performance?

Accepted Answer

Add more diverse training phrases per intent. Adding more diverse training phrases per intent directly addresses the root cause of the bot's failure to understand customer intents: insufficient coverage of the varied ways customers express the same goal. Einstein Bot uses natural language understanding (NLU) models that rely on example phrases to learn intent patterns; increasing the diversity of these phrases improves the model's ability to generalize to unseen utterances, reducing misclassification.

Answer

Increase the confidence threshold to 90%

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Use a hierarchical intent structure

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Reduce the number of intents to two

Question 12

A data scientist notices that an Einstein Discovery model predicts a low probability of conversion for all leads in a new campaign, even though the campaign targets high-value accounts. Which initial diagnostic step should be taken?

Accepted Answer

Compare the feature distributions of the training and campaign data. The most likely cause of a model predicting low conversion for all leads in a new campaign is a shift in feature distributions between the training data and the campaign data (covariate shift). Checking these distributions is the standard initial diagnostic step to identify if the model is encountering data it was not trained on, which would invalidate its predictions. This aligns with best practices for model monitoring and data validation in Einstein Discovery.

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Retrain the model with the new campaign data included

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Check the Einstein model recipe for incorrect filters

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Increase the prediction confidence threshold

Question 13

During an AI ethics review, a stakeholder asks how Salesforce ensures that Einstein models do not discriminate based on protected attributes. Which mechanism addresses this concern?

Accepted Answer

Use the bias detection and fairness reporting built into Einstein Prediction Builder. Salesforce Einstein Prediction Builder includes built-in bias detection and fairness reporting that automatically checks models for discrimination based on protected attributes. This feature analyzes model predictions against demographic groups and generates fairness metrics, allowing stakeholders to identify and mitigate bias directly within the platform.

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Remove all protected attribute fields from the training dataset

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Rely on model accuracy metrics to ensure equal treatment

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Allow admins to manually override model predictions for certain groups

Question 14

A company wants to use Einstein to predict the optimal discount amount for each deal. Which type of machine learning problem does this represent?

Accepted Answer

Regression. Predicting a continuous numerical value, such as the optimal discount amount for a deal, is a regression problem. In the context of Einstein, this would use a regression model to learn from historical deal data and output a specific discount percentage or dollar amount, rather than a category or cluster.

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Reinforcement learning

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Classification

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Clustering

Question 15

Which TWO of the following are common causes of model drift in Einstein Discovery?

Accepted Answer

Seasonal patterns that affect the target variable. Seasonal patterns (Option B) cause model drift because the relationship between input features and the target variable changes predictably over time, such as higher sales during holidays. Einstein Discovery models trained on historical data may fail to generalize if the seasonal cycle is not captured or if the model is not retrained to account for these recurring shifts, leading to degraded prediction accuracy.

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Improved data quality after cleaning

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Increased model complexity

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Reduced size of the training dataset