AI Associate Practice Test 21 — 15 Questions

Question 1

A sales team wants to compare their own forecast amounts with an AI-generated prediction based on historical data and trends. Which Salesforce feature provides this comparison?

Accepted Answer

Einstein Forecasting. Einstein Forecasting uses AI to generate predictive forecasts based on historical opportunity data and trends, and it allows users to compare their own forecast amounts side-by-side with the AI-generated predictions. This is exactly the comparison the sales team needs.

Answer

Einstein Opportunity Scoring

Answer

Einstein Lead Scoring

Answer

Einstein Prediction Builder

Question 2

A service manager wants to automatically classify incoming cases into Type, Priority, and Reason fields to reduce manual data entry. Which Einstein feature best meets this requirement?

Accepted Answer

Einstein Case Classification. Einstein Case Classification is specifically designed to automatically predict and populate fields like Type, Priority, and Reason for incoming cases using machine learning models trained on historical case data. This reduces manual data entry by suggesting or auto-filling these fields based on the case's subject, description, and other attributes.

Answer

Einstein Discovery

Answer

Einstein Next Best Action

Answer

Einstein Article Recommendations

Question 3

A sales leader wants to see an AI-generated forecast that compares the predicted revenue to the reps' commit amounts. Which feature provides this comparison?

Accepted Answer

Einstein Forecasting. Einstein Forecasting is the Salesforce feature that generates AI-powered predictions for revenue and compares them to reps' commit amounts, providing a forecast comparison view. It uses historical data and machine learning to predict future revenue and highlights gaps between predictions and committed deals.

Answer

Einstein Discovery

Answer

Einstein GPT

Answer

Einstein Opportunity Scoring

Question 4

An administrator wants to build a custom AI model that predicts whether a support case will be escalated based on historical case data. The prediction must be binary (escalated or not). Which tool should they use?

Accepted Answer

Einstein Prediction Builder. Einstein Prediction Builder is the correct tool because it is specifically designed to create custom binary classification models using historical Salesforce data, such as predicting case escalation (yes/no). It requires no data science expertise and automates model training, evaluation, and deployment directly within the Salesforce platform.

Answer

Einstein Discovery

Answer

Einstein Next Best Action

Answer

Einstein Vision

Question 5

A company uses Einstein Prediction Builder to predict which leads will convert. They have a binary outcome field 'Converted__c' which is true for 8% of leads. After training, the model shows high accuracy (95%) but very low precision for the positive class. What is the most likely cause?

Accepted Answer

The data is imbalanced favoring the negative class. The dataset is imbalanced: only 8% of leads are positive (Converted__c = true), while 92% are negative. In such a scenario, a model can achieve 95% accuracy by simply predicting the majority class (negative) for all leads, but this yields very low precision for the positive class because it rarely predicts positive correctly. Einstein Prediction Builder, like most ML models, is sensitive to class imbalance, and without techniques like oversampling or threshold tuning, the model will favor the majority class.

Answer

The prediction field is not a binary field

Answer

The prediction score field is not configured correctly

Answer

The dataset is too small for training

Question 6

A sales team uses Einstein Lead Scoring and notices that the model gives disproportionately low scores to leads from a certain demographic group. The team suspects historical bias in the training data. Which THREE steps should they take to address this bias?

Accepted Answer

Investigate the historical data to identify and correct labeling errors or sampling bias. Option A is correct because bias in Einstein Lead Scoring typically originates in the training data, so investigating historical records to find and fix mislabeled outcomes or sampling bias directly addresses the root cause. Option B is correct because auditing the model for disparate impact quantifies whether the affected demographic receives systematically lower scores, providing the evidence needed to confirm and monitor the bias. Option E is correct because adding features that are not correlated with the protected attribute gives the model more legitimate predictive signal, reducing its reliance on biased proxies. Option C is not appropriate because manually inflating scores by a fixed percentage is an arbitrary post-hoc adjustment that masks the underlying bias and can distort rankings without fixing the model. Option D is not appropriate because abandoning Einstein Lead Scoring entirely discards a useful tool rather than remediating the data and model, and manual scoring does not inherently eliminate bias.

Answer

Manually increase scores for the affected group by a fixed percentage

Answer

Remove the model and rely on manual lead scoring

Question 7

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.

Answer

Train all agents on how to monitor the bot.

Answer

Set up a new email channel for the bot.

Answer

Create a custom object to store bot conversations.

Question 8

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. A large gap between training accuracy (99%) and test accuracy (65%) is the classic signature of overfitting: the model has memorized training data, including noise, rather than learning generalizable patterns. This typically results from excessive model complexity (too many parameters, deep trees) or insufficient regularization (no dropout, L1/L2, or early stopping). The fix is to reduce capacity or add regularization, not to add complexity.

Answer

Underfitting due to insufficient model complexity

Answer

Label noise in the training data

Answer

Data leakage from the test set into training

Question 9

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.

Answer

Remove all protected attribute fields from the training dataset

Answer

Rely on model accuracy metrics to ensure equal treatment

Answer

Allow admins to manually override model predictions for certain groups

Question 10

Which TWO actions help ensure transparency in AI systems according to Salesforce's ethical AI guidelines?

Accepted Answer

Documenting model assumptions and limitations.. Option D is correct because documenting model assumptions and limitations directly supports transparency by making the model's scope, intended use, and known constraints visible to stakeholders, which aligns with Salesforce's ethical AI guidance on openness about how models work and where they may fail. Option E is correct because providing plain-language explanations of model predictions makes the system's reasoning accessible to non-technical users, enabling them to understand and challenge outcomes, which is a core transparency practice. Option A is not correct because restricting access to model outputs reduces visibility and accountability rather than increasing transparency. Option B is not correct because using complex deep learning models without explanation is the opposite of transparency, since it obscures how predictions are generated. Option C is not correct because automatically retraining models weekly is an operational lifecycle choice and does not by itself make the model's assumptions, limitations, or predictions understandable to stakeholders.

Answer

Limiting access to model outputs to only a few people.

Answer

Using complex deep learning models without explanation.

Answer

Automatically retraining models weekly.

Question 11

Which Einstein feature provides automated statistical analysis of Salesforce data, generates natural language stories, and suggests improvement actions?

Accepted Answer

Einstein Discovery. Einstein Discovery is the Salesforce feature that performs automated statistical analysis, generates natural language stories explaining the data, and suggests improvement actions. It uses machine learning to uncover patterns and drivers in Salesforce data and presents them in an intuitive narrative format.

Answer

Einstein Forecast

Answer

Einstein Prediction Builder

Answer

Einstein Next Best Action

Question 12

A sales rep wants to quickly generate a summary of a phone call recorded in Salesforce for their records. Which Einstein feature can automatically generate call summaries from recorded conversations?

Accepted Answer

Einstein Conversation Insights. Einstein Conversation Insights is the correct feature because it uses natural language processing (NLP) to automatically analyze recorded phone calls and generate concise summaries, key topics, and action items. This allows sales reps to quickly capture call outcomes without manual note-taking, directly within Salesforce.

Answer

Sales GPT

Answer

Einstein Activity Capture

Answer

Einstein Email Insights

Question 13

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. Option A is correct because biased historical lending data can cause the AI to systematically discriminate against protected demographic groups, so the bank must audit and remediate training data to ensure fair and equitable loan decisions. Option D is correct because loan denials are adverse actions, and customers need transparency and explanations (e.g., reason codes) to understand and potentially contest the AI's decision, which also supports regulatory compliance such as ECOA/Regulation B adverse action notice requirements. Option B is not necessarily an ethical consideration; more data can improve predictions but does not by itself address fairness, privacy, or accountability. Option C is not ethically required and could remove human oversight and accountability, which is generally undesirable in high-stakes lending decisions. Option E is not ethically sufficient because maximizing accuracy alone can worsen bias and ignores fairness, explainability, and customer rights.

Answer

Use as many data points as possible to improve predictions

Answer

Replace all human loan officers with AI

Answer

Maximize model accuracy at all costs

Question 14

Which TWO data preparation steps are critical for ensuring high-quality training data?

Accepted Answer

Removing duplicate records.. Option B (Removing duplicate records) is correct because duplicate rows bias the training set toward repeated examples, causing overfitting and skewing learned distributions, so deduplication is a critical data-cleaning step for high-quality training data. Option D (Handling missing values appropriately) is correct because nulls, NaNs, or empty fields break many algorithms and distort statistics; imputing, dropping, or flagging missing entries ensures the model learns from complete, reliable signals. Option A is wrong because adding noise increases dataset size but degrades label and feature integrity rather than improving quality. Option C is wrong because normalization is a feature-scaling step that helps optimization and convergence but is not strictly critical for data quality, and some algorithms (e.g., tree-based models) do not require it. Option E is wrong because unlabeled data is valid and valuable for semi-supervised and unsupervised training, so restricting to labeled data is not a critical quality requirement.

Answer

Increasing dataset size by adding noise.

Answer

Normalizing all features.

Answer

Using only labeled data.

Question 15

A sales operations manager wants to improve the accuracy of Einstein Opportunity Scoring. Which TWO actions should they take? (Choose two.)

Accepted Answer

Ensure that historical opportunity data includes both won and lost records. Option A is correct because Einstein Opportunity Scoring trains its model on historical opportunity outcomes, so the dataset must contain a balanced mix of both won and lost opportunities to learn the patterns that distinguish success from failure. Option D is correct because choosing only the most relevant fields as model factors reduces noise and prevents irrelevant or redundant attributes from degrading the model's predictive accuracy. Option B is incorrect because Einstein Discovery is a separate analytics and prediction tool and does not itself improve Opportunity Scoring accuracy. Option C is incorrect because duplicating records artificially inflates the dataset without adding genuine outcome information, which can bias the model. Option E is incorrect because manually overriding scores does not improve the underlying model's accuracy and is not a recommended tuning action.

Answer

Use Einstein Discovery to analyze the same data

Answer

Increase the number of records by duplicating existing opportunities

Answer

Manually override scores for high-value opportunities