AI Associate Practice Test 33 — 15 Questions

Question 1

A data scientist evaluates a churn prediction model. On the test set, the model achieves 99% accuracy, but the business reports that the model rarely flags actual churners. Which metric should the data scientist focus on to improve the model?

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

Accepted Answer

Recall. Recall measures the proportion of actual churners that the model correctly identifies, which is exactly what the business complaint describes — the model misses real churners (false negatives). Since the dataset is likely imbalanced (few churners), 99% accuracy can be achieved by predicting 'no churn' for everyone, making recall the metric that exposes this failure. Optimizing recall directly addresses the goal of flagging more true churners.

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F1 score

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Accuracy

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Precision

Question 2

A service agent needs quick access to relevant knowledge articles while handling a case. Which Einstein feature can suggest the most relevant articles automatically?

Accepted Answer

Einstein Article Recommendations. Einstein Article Recommendations (C) is the correct feature because it uses AI to automatically surface the most relevant knowledge articles based on the context of the case, such as subject, description, and product. This directly addresses the need for quick access to relevant articles without manual search, leveraging Salesforce's predictive AI to match case data with article content.

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Einstein Next Best Action

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Einstein Case Classification

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Einstein Service GPT

Question 3

A system administrator receives an error when running a Data Cloud data transform: 'Row-level security settings are preventing access to the source data.' The admin has appropriate permissions. What is the most likely cause?

Accepted Answer

The source object has sharing rules that restrict access for the data transform's running user.. Data Cloud data transforms run under a specific running user context, and row-level security (RLS) settings on the source object can restrict that user's access to rows, even if the admin has broad permissions. The error indicates that the running user lacks visibility to certain source data rows due to sharing rules or RLS policies, which is a common cause of this specific error message.

Answer

The target object has field-level security.

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The data stream is scheduled during maintenance.

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The data transform is set to run as the admin's default user.

Question 4

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. Supervised learning is the correct technique because the problem involves predicting a binary outcome (conversion or not) based on historical labeled data (lead attributes and conversion outcomes). A supervised learning model, such as logistic regression or a decision tree, can be trained on past leads to score new leads by their likelihood to convert. This is a classic classification problem.

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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 5

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. A large gap between high training accuracy (95%) and much lower validation accuracy (60%) is the textbook signature of overfitting: the model has memorized training-specific noise rather than learning generalizable patterns. The fix typically involves regularization, more data, or simpler models. Underfitting would show low accuracy on both sets.

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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 6

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. When a model performs well overall but consistently misclassifies cases from a specific region, the most likely cause is that the training data lacks sufficient representative examples from that region. The model has not learned the patterns, language, or characteristics unique to that region's support cases, leading to systematic errors. This is a data diversity/representation problem, not a model architecture issue.

Answer

The model is underfitted

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The model uses too many features

Answer

The model is overfitted

Question 7

A Salesforce admin is configuring Einstein Case Classification to automatically set the Type field on new support cases. The admin wants to understand what the model actually does when a case is created. Which statement best describes how Einstein Case Classification operates in this scenario?

Accepted Answer

It analyzes the case subject and description text, then predicts the most likely Type value based on patterns learned from previously closed cases.. Einstein Case Classification learns from historical closed cases where users previously set the Type field, correlating case text with the chosen picklist value. On new cases it predicts the most likely Type. Understanding that it is a supervised text-classification model, not a rules engine or Knowledge lookup, is essential before enabling it for support operations.

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It queries the Salesforce Knowledge base and copies the article category into the case Type field.

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It runs a keyword-matching rule engine that assigns a Type whenever a predefined word appears in the case description.

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It sends each new case to an external Einstein Discovery model that returns a numeric score between 0 and 1.

Question 8

Which Einstein feature helps agents by suggesting relevant knowledge articles while working on a case?

Accepted Answer

Einstein Article Recommendations. Einstein Article Recommendations is specifically designed to analyze case details and suggest relevant knowledge articles to agents in real time. It uses natural language processing and machine learning to match case context with the most helpful articles from the knowledge base. This directly improves agent efficiency and resolution times.

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Einstein Search

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Einstein Case Classification

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Einstein Reply Recommendations

Question 9

A company's Einstein Sentiment model is used to flag negative customer feedback. The model was trained on English reviews only. When deployed globally, it misclassifies positive reviews in Spanish as negative. What is the primary ethical concern?

Accepted Answer

The model is unfair to Spanish-speaking customers.. The primary ethical concern is fairness: the model was trained exclusively on English reviews, so it systematically misclassifies Spanish positive feedback as negative. This creates a disparate impact on Spanish-speaking customers, violating the principle of algorithmic fairness. The issue is not just low accuracy but an unjust bias that disadvantages a specific linguistic group.

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The model is not interpretable.

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The model has low accuracy for Spanish reviews.

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The model violates privacy regulations.

Question 10

A data analyst wants to understand why a particular opportunity win rate dropped last quarter. They need automated statistical analysis with natural language explanations and improvement suggestions. Which tool should they use?

Accepted Answer

Einstein Discovery. Einstein Discovery provides automated statistical analysis, stories, and improvement suggestions.

Answer

Einstein Prediction Builder

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Einstein Conversation Insights

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Einstein Next Best Action

Question 11

A data scientist is building a churn prediction model. The dataset has 95% non-churn and 5% churn. Which THREE actions should the data scientist take to address the class imbalance?

Accepted Answer

Undersample the non-churn class. Option C (undersample the non-churn class) is correct because reducing the majority non-churn examples brings the class distribution closer to balanced, so the learning algorithm is no longer overwhelmed by the 95% majority and can better learn churn patterns. Option D (use precision, recall, or F1 score instead of accuracy) is correct because with a 95/5 split, a trivial model predicting 'non-churn' for every record achieves 95% accuracy while catching zero churners, so precision, recall, and F1 (and often AUC-PR) reflect real performance on the minority class. Option E (oversample the churn class) is correct because duplicating or synthetically generating minority churn examples (e.g., via SMOTE) increases the effective weight of the 5% class and reduces the bias toward the majority. Option A is not appropriate because training unchanged on a 95/5 dataset typically yields a model that ignores the minority class. Option B is not appropriate because accuracy is misleading under severe class imbalance and would reward the majority-class-only predictor.

Answer

Train the model on the original dataset without changes

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Use accuracy as the primary metric

Question 12

Which method is most suitable for ingesting streaming data from IoT sensors into a data lake?

Accepted Answer

Real-time streaming with Apache Kafka.. Apache Kafka is the most suitable option because it is a distributed streaming platform designed for high-throughput, fault-tolerant, real-time data ingestion. IoT sensors generate continuous, high-velocity data streams, and Kafka's publish-subscribe model allows data to be ingested into a data lake with low latency, ensuring near-real-time availability for analytics.

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Copying data via FTP.

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Batch ingestion every 24 hours.

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Manual upload via web interface.

Question 13

Which Einstein feature provides AI-powered call recording analysis that tracks keywords, talk-time metrics, and next steps?

Accepted Answer

Einstein Conversation Insights. Einstein Conversation Insights is the Salesforce feature specifically designed to analyze call recordings and transcripts using AI. It automatically extracts keywords, tracks talk-time metrics (such as agent versus customer talk ratio), and identifies next steps or action items from the conversation. This aligns with the question's requirement for AI-powered call recording analysis that monitors these exact elements.

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Einstein Email Insights

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Einstein Activity Capture

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Einstein Voice

Question 14

A nonprofit organization uses Salesforce Nonprofit Cloud with Einstein Discovery to analyze donation patterns. They have activated a story that predicts which donors are most likely to churn (stop donating) in the next three months. The story shows a top influence called 'DonationFrequency' with a negative correlation: donors who donate less than once per quarter are 40% more likely to churn. The director of development wants to use this insight to create a retention campaign. However, the story also includes a field called 'LastDonationAmount' which has a small positive influence. The development team wants to ensure the predictions are actionable. What should the administrator do to maximize the effectiveness of the Einstein Discovery story for this retention campaign?

Accepted Answer

Create a segment of donors with low donation frequency and use that as the target for the retention campaign.. The most actionable insight from the Einstein Discovery story is the strong negative correlation of 'DonationFrequency' with churn. By creating a segment of donors with low donation frequency, the administrator can directly target the highest-risk group for a retention campaign, making the prediction actionable without altering the model or its output. This approach leverages the story's findings as-is, which is the intended use of Einstein Discovery insights.

Answer

Retrain the prediction model using only 'DonationFrequency' and 'LastDonationAmount' as predictors.

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Delete the 'LastDonationAmount' influence from the story to simplify the output.

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Adjust the influence weight of 'DonationFrequency' to be higher in the story settings.

Question 15

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.

Answer

Einstein Prediction Builder

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Einstein Article Recommendations

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Einstein Next Best Action