AI Associate AI Fundamentals Practice Test 5 — 15 Questions

Question 1

An admin sets up Einstein Sentiment scoring for case comments. After a week, they notice that most comments are scored as 'Neutral' even when customer sentiment is clearly negative. What should the admin check first?

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

Accepted Answer

Retrain the sentiment model with industry-specific training data. Einstein Sentiment is a pre-trained model that may not accurately interpret sentiment in industry-specific contexts. Retraining the model with domain-specific training data (e.g., using Salesforce's Intent and Sentiment API with custom datasets) adjusts the model to recognize sentiment nuances in that industry, improving accuracy beyond the generic baseline.

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Increase the model confidence threshold to reduce false positives

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Switch to Einstein Bot for sentiment analysis

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Verify that case comments contain at least 50 words each

Question 2

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 3

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 4

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

Question 5

Which THREE of the following are best practices for training an Einstein Bot?

Accepted Answer

Test the bot with sample conversations before deployment. Testing an Einstein Bot with sample conversations before deployment allows you to validate the bot's intent recognition, dialog flow, and response accuracy in a controlled environment. This practice helps identify and fix issues with phrase matching, slot filling, and escalation paths, ensuring the bot performs reliably in production.

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Train with only one intent to avoid confusion

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Use exact match phrases only

Question 6

Which TWO of the following are key principles of trustworthy AI according to Salesforce's AI ethics guidelines?

Accepted Answer

Transparency. Transparency is a core principle of trustworthy AI because it requires that AI systems operate in a way that is open and understandable, allowing stakeholders to see how decisions are made. Salesforce's AI ethics guidelines emphasize transparency to ensure that users can trust the system's outputs and that any biases or limitations are visible.

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Profitability

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Complexity

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Accountability

Question 7

Refer to the exhibit. The prediction API returns a probability of 0.85 for the label 'High Value'. What does this value represent?

Accepted Answer

The confidence score that this lead is 'High Value'. The prediction API returns a probability of 0.85 for the label 'High Value'. In machine learning classification, this output represents the model's confidence score—the estimated probability that the input instance belongs to the specified class. It is not a direct measure of conversion likelihood, model accuracy, or F1 score; it is the raw posterior probability assigned by the model to the 'High Value' label.

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The likelihood that this lead will convert

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The F1 score of the model for this prediction

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The model's accuracy on the training set

Question 8

Refer to the exhibit. A bot developer sees this error during Einstein Bot deployment. What is the correct action to resolve the issue?

Accepted Answer

Add at least 5 training phrases to the 'OrderStatus' intent. The error indicates that the 'OrderStatus' intent has fewer than the required minimum number of training phrases. Einstein Bot requires at least 5 training phrases per intent to ensure reliable natural language processing (NLP) model training. Adding at least 5 training phrases to the 'OrderStatus' intent satisfies this requirement and resolves the deployment error.

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Reduce the minimum required phrases in bot settings

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Remove the 'OrderStatus' intent from the bot

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Increase the confidence threshold for all intents

Question 9

Refer to the exhibit. An admin runs a preprocess script before training an Einstein model. Why is normalization applied to the 'AnnualRevenue' and 'NumberOfEmployees' columns?

Accepted Answer

To ensure both features contribute equally to the model. Normalization scales features like 'AnnualRevenue' and 'NumberOfEmployees' to a comparable range (e.g., 0–1 or with zero mean and unit variance). Without normalization, a feature with larger numeric values (e.g., revenue in millions) would dominate distance-based calculations in models like k-nearest neighbors or gradient descent, causing the model to undervalue the smaller-scale feature. By normalizing, both features contribute equally to the model's learning process, which is essential for many machine learning algorithms used in Einstein.

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To detect outliers in the data

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To remove rows with missing values

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To reduce the number of features from 30 to 2

Question 10

A sales team wants to use Einstein Lead Scoring to prioritize leads. What is the primary benefit of using Einstein Lead Scoring over manual scoring?

Accepted Answer

It uses historical data to predict which leads are most likely to convert.. Einstein Lead Scoring uses historical data and machine learning models to analyze patterns from past leads and their conversion outcomes. This allows it to assign a predictive score to each new lead, indicating the likelihood of conversion, which is more accurate and data-driven than manual scoring based on subjective criteria.

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It automatically sends personalized emails to leads.

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It ensures all leads are contacted within 24 hours.

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It replaces the need for any manual lead qualification process.

Question 11

A company notices that Einstein Prediction Builder predictions for 'Churn' are less accurate than expected. Which action should the administrator take first to improve model performance?

Accepted Answer

Review the training data for missing values and ensure relevant fields are included in the model.. The first step in improving Einstein Prediction Builder model performance is to review the training data for missing values and ensure relevant fields are included. Missing values or irrelevant fields can introduce noise and bias, directly degrading predictive accuracy. Einstein Prediction Builder relies on high-quality, complete training data to learn meaningful patterns, so data quality issues must be addressed before any other tuning steps.

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Enable field history tracking on all object fields used in the prediction.

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Change the prediction outcome to a different field to see if accuracy improves.

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Retrain the model with the same data but increase the number of training iterations.

Question 12

A company wants to use Einstein Bots to handle customer support queries. Which preparation is most important before deploying the bot?

Accepted Answer

Ensure the knowledge base articles are well-organized and cover common issues.. Einstein Bots rely on a well-organized knowledge base to retrieve accurate answers for customer queries. Without properly structured articles covering common issues, the bot cannot effectively resolve tickets, leading to poor deflection rates and user frustration. This preparation directly impacts the bot's ability to understand and respond to natural language inputs using Salesforce's NLP and article matching.

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Train all agents on how to monitor the bot.

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Set up a new email channel for the bot.

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Create a custom object to store bot conversations.

Question 13

A financial services company is deploying Einstein AI and must comply with regulations requiring explainable decisions. Which Einstein capability allows them to understand why an AI model made a specific prediction?

Accepted Answer

Einstein Trust Layer with model explainability features.. The Einstein Trust Layer includes model explainability features that provide insights into why a specific prediction was made, such as highlighting the key input features that influenced the outcome. This directly addresses regulatory requirements for explainable AI decisions by offering transparency into the model's reasoning process.

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Data Mask to protect sensitive information.

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Salesforce Shield with encryption and monitoring.

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Field Audit Trail to track changes to data.

Question 14

A company wants to deploy an Einstein AI model that uses sensitive customer data. Which practice should they follow to comply with data privacy regulations?

Accepted Answer

Use Einstein Trust Layer features to mask personally identifiable information (PII) in the model.. The Einstein Trust Layer provides built-in capabilities to automatically mask or redact personally identifiable information (PII) before data is sent to the underlying AI model, ensuring compliance with data privacy regulations like GDPR and CCPA without requiring manual data handling. This feature operates at the platform level, intercepting data in transit and applying masking rules based on predefined patterns, so sensitive customer data is never exposed to the model or stored in its training logs.

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Store all sensitive data in an external data lake and connect via APIs.

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Obtain explicit consent from data subjects before using their data in AI models.

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Limit the data used for training to only essential fields.

Question 15

Which TWO actions are recommended when preparing data for an Einstein Prediction Builder model?

Accepted Answer

Ensure the data set contains at least 500 records with the outcome field populated.. Einstein Prediction Builder requires a minimum of 500 records with the outcome field populated to ensure statistical significance and reliable model training. Fewer records can lead to overfitting or insufficient pattern recognition, making the model less accurate.

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Include all available fields on the object, even if unrelated.

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Use external data sources and upload CSV files without any preprocessing.

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Include data from the last two days only for the most current trends.