AI Associate Practice Test 4 — 25 Questions

Question 1

An admin wants to create a custom AI model to predict lead conversion using Einstein Prediction Builder. Which TWO items must they select when creating the model? (Choose two)

Accepted Answer

Data set (records to train on). The data set defines the records (e.g., leads, opportunities) that the model will use for training. Without specifying which records to train on, the model has no source of historical data to learn patterns from. Einstein Prediction Builder requires you to select a data set (such as a report or object) to provide the training examples.

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Model algorithm type

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Prediction explanation settings

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Features (input fields)

Question 2

A company wants to use Einstein Relationship Intelligence to analyze email and calendar data for opportunity insights. Which two conditions must be met? (Select two answers.)

Accepted Answer

Users must have the Einstein Relationship Intelligence permission set.. The Einstein Relationship Intelligence permission set grants users access to the feature's insights and data processing capabilities. Without this permission set, users cannot view or interact with Einstein Relationship Intelligence data, even if the feature is enabled at the org level.

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Exchange or Gmail must be used for email.

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The feature is only available in Unlimited Edition.

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All users must opt in to email logging.

Question 3

Refer to the exhibit. An admin created a prediction using Einstein Prediction Builder. The prediction is configured to calculate a score on the Lead object. What does the JSON indicate about the model?

Accepted Answer

It predicts a binary outcome (e.g., convert or not). Einstein Prediction Builder for the Lead object, when configured to predict a binary outcome like 'convert or not,' outputs a JSON payload containing a 'probability' field (e.g., 0.85) and a 'predictedValue' field (e.g., 'Converted' or 'Not Converted'). The JSON shown indicates a classification model that assigns a probability to one of two discrete classes, which is the hallmark of binary classification. The presence of a 'predictedValue' field with a categorical label confirms it is not a regression (numeric) or multi-class text prediction.

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It predicts a numeric value

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It predicts the value of a text field

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It is currently retraining the model

Question 4

An AI system used for medical diagnosis has been shown to have lower accuracy for certain ethnic groups. The development team is considering releasing it anyway because most patients are from the majority group. Which ethical principle is most compromised?

Accepted Answer

Fairness. The scenario describes an AI system that performs worse for certain ethnic groups, yet the team plans to release it anyway because the majority group is unaffected. This directly violates the principle of fairness, which requires that AI systems do not discriminate or perpetuate bias against any group. Releasing a model with known accuracy disparities without mitigation prioritizes overall performance over equitable treatment, compromising fairness.

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Transparency

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Accountability

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Privacy

Question 5

A company is training a customer service chatbot using historical conversation logs. Which TWO data preparation practices should be followed to ensure data quality?

Accepted Answer

Include answers with varied phrasing to enhance language variety. Training a chatbot on varied phrasing (e.g., synonyms, different sentence structures) improves its ability to understand and generate natural language responses. This practice enhances the model's robustness and generalization, preventing overfitting to a narrow set of expressions and ensuring it can handle the diverse ways customers phrase their queries.

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Exclude all user identifiers to protect privacy

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Include only successful interactions that were resolved

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Filter only English conversations for consistency

Question 6

Refer to the exhibit. A Salesforce admin evaluates an Einstein Prediction Builder model for customer churn. What should be the admin's primary concern based on the exhibit?

Accepted Answer

The model has detected data drift, indicating the training data may no longer represent current patterns.. The exhibit shows a data drift alert from Einstein Prediction Builder, which indicates that the statistical properties of the input data have changed over time. This is the admin's primary concern because a model trained on outdated patterns will produce unreliable predictions, even if its accuracy or AUC were initially high. Data drift directly undermines the model's validity in production.

Answer

The model accuracy is too low for production use.

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The AUC is low, so the model is not better than random.

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The model uses too few features to be reliable.

Question 7

To recommend the most relevant action for a service agent during a call, which feature should be used?

Accepted Answer

Einstein Next Best Action. Einstein Next Best Action is the correct feature because it uses AI to analyze the call context in real time and recommend the most relevant action for a service agent, such as offering a specific discount or knowledge article. Unlike static automation tools, it leverages predictive models to adapt recommendations based on customer data and conversation sentiment, ensuring the agent takes the optimal next step.

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Flow

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Quick Actions

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Process Builder

Question 8

A retail company uses Einstein Next Best Action with customer data from Data Cloud. The recommendations are not personalized. The admin checks the data quality dashboard and finds that the 'Customer_Profile' object has 40% records with missing 'PreferredChannel' field. What is the best course of action?

Accepted Answer

Impute the missing values using the mode of the field.. Imputing missing values using the mode (most frequent value) of the 'PreferredChannel' field is a standard data preprocessing technique that preserves the dataset size and statistical distribution. In Einstein Next Best Action, missing categorical data can degrade model personalization, and mode imputation is a simple, effective way to handle this without losing records or altering the model structure.

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Remove the field from the model.

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Increase the data refresh frequency.

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Train the model with only records that have non-null PreferredChannel.

Question 9

Refer to the exhibit. A Salesforce admin sees this error when trying to enable Einstein Lead Scoring. What should the admin do to resolve the issue?

Accepted Answer

Add more lead records with associated activities until reaching at least 100. Einstein Lead Scoring requires a minimum of 100 lead records with associated activities (e.g., emails, events, tasks) to generate a predictive model. The error indicates insufficient data, so adding more leads with activities meets the threshold for model training.

Answer

Enable Einstein features in the org

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Map lead fields to Einstein fields

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Grant the admin the 'Manage Einstein' permission

Question 10

Which THREE actions are recommended when preparing data for Einstein Next Best Action? (Choose 3)

Accepted Answer

Provide data on which actions were offered and whether they were accepted. Einstein Next Best Action (NBA) requires historical interaction data showing which actions were offered and whether they were accepted to train the predictive model. This feedback loop enables the AI to learn which actions are most effective for specific customer contexts, directly improving recommendation accuracy.

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Include at least 10 different action types per strategy

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Use only historical data from the last 30 days

Question 11

Which TWO of the following are common dimensions of data quality that must be addressed for AI training?

Accepted Answer

Accuracy of values. Accuracy of values (Option B) is a fundamental dimension of data quality because AI models learn patterns from training data; if the data contains incorrect values, the model will learn and propagate those errors, leading to unreliable predictions. For example, in a dataset of customer ages, a single erroneous entry of '200' can skew the model's understanding of age distributions, directly impacting model performance.

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Storage efficiency

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Encryption strength

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Consistency with external benchmarks

Question 12

An organization uses Salesforce Data Cloud to unify customer data from multiple sources. They want to ensure that data lineage is tracked for AI models. Which practice supports data lineage?

Accepted Answer

Maintain metadata that records source, transformations, and dependencies.. Maintaining metadata that records source, transformations, and dependencies is the correct practice because data lineage for AI models requires a complete audit trail of where data originated, how it was transformed, and its dependencies. In Salesforce Data Cloud, this metadata is captured through the Data Catalog and Data Lineage feature, which tracks the flow of data from source objects through calculated insights and segments to AI model inputs, ensuring transparency and reproducibility.

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Use data partitioning to improve query performance.

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Implement role-based access control on datasets.

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Regularly run data profiling to check completeness.

Question 13

An organization is preparing data for Einstein Next Best Action. They have multiple action types (discounts, product suggestions, content). Which data model approach best ensures accurate recommendations?

Accepted Answer

Use a single model that includes all action types in the training data.. Einstein Next Best Action is designed to learn from all action types simultaneously within a single model. By including all action types (discounts, product suggestions, content) in the training data, the model can capture cross-action patterns and relative effectiveness, leading to more accurate and contextually relevant recommendations. A unified model avoids fragmentation and ensures consistent scoring across actions.

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Train a separate model per customer segment and then merge.

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Create a separate model for each action type and combine results manually.

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Build an ensemble of models and average their outputs.

Question 14

An admin is troubleshooting Einstein Sentiment. The model returns high confidence but wrong sentiment (e.g., positive reviews labeled negative). What is the most likely issue?

Accepted Answer

The training data has incorrect labels for sentiment.. If the training data contains incorrect labels for sentiment, the model learns from erroneous ground truth, leading to high confidence in wrong predictions. In Einstein Sentiment, the model's accuracy depends directly on the quality and correctness of the labeled training data; mislabeled examples cause the classifier to associate features with the wrong sentiment class, resulting in confident but incorrect outputs.

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The model was not retrained after the last data load.

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The training data contains predominantly neutral examples.

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The field mapping for the sentiment field is incorrect.

Question 15

An admin wants to compare the AI-generated forecast with a rep's commit forecast to identify gaps. Which feature should they use?

Accepted Answer

Einstein Forecasting. Einstein Forecasting is the correct feature because it directly compares AI-generated forecasts with a rep's commit forecast to identify gaps. It uses historical data and predictive models to generate a baseline forecast, which can be overlaid with the rep's manual commit to highlight discrepancies for coaching and adjustment.

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Einstein Prediction Builder

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Einstein Discovery

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Einstein Opportunity Scoring

Question 16

A financial services firm uses an AI model to approve loan applications. They discover the model denies loans at a higher rate for a protected demographic. What is the most likely root cause?

Accepted Answer

The training data contains historical bias. Historical bias in training data can cause models to learn and perpetuate discrimination.

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The model is overfitted

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The model uses too few features

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The model has low precision

Question 17

A company uses an AI model to classify customer support cases into categories (billing, technical, general). The model performs well on training data but poorly on new cases. Which issue is MOST likely occurring?

Accepted Answer

Overfitting. Overfitting means the model memorized the training data and fails to generalize to new, unseen data. Underfitting would show poor performance on both training and test data. Data leakage occurs when future information leaks into training. Bias in data is a different issue.

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Underfitting

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Data leakage

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Bias in training data

Question 18

A data scientist needs to prepare data for Einstein Discovery. The dataset includes a field 'Customer_Status__c' with values 'Active', 'Inactive', and 'Churned'. How should this field be treated?

Accepted Answer

Keep as a text field and let Einstein Discovery handle it as a categorical predictor.. Einstein Discovery natively supports text fields as categorical predictors, automatically encoding them for model training. The platform handles string values like 'Active', 'Inactive', and 'Churned' without requiring manual transformation, preserving the semantic meaning and cardinality of the data.

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Create separate boolean fields for each value to improve model accuracy.

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Remove the field because text fields cannot be used in Einstein Discovery.

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Convert to numeric values 1, 2, 3 to preserve order.

Question 19

A team is building a pipeline to train a model daily. The source data arrives in CSV files but needs to be converted to Parquet for efficiency. Which pipeline step should perform this conversion?

Accepted Answer

Data ingestion step. The data ingestion step is responsible for bringing raw data into the pipeline, including format conversions like CSV to Parquet. Converting to Parquet at ingestion improves storage efficiency and query performance for downstream processing, as Parquet uses columnar storage and compression.

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Feature engineering step

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Model deployment step

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Data validation step

Question 20

A company uses Einstein Forecasting. Their sales reps' committed forecasts are consistently lower than the AI-predicted forecast. The manager wants to understand why. What is the BEST first step to investigate the discrepancy?

Accepted Answer

Review the AI forecast explanation to see which factors (e.g., deal stage, historical win rates) are driving the higher prediction.. Einstein Forecasting provides an AI-predicted forecast based on historical data and deals. Comparing the rep commit to the AI forecast, and analyzing the key drivers behind the AI prediction (such as deal stage, age, amount) helps identify why the AI expects more.

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Disable Einstein Forecasting and revert to manager rollups only.

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Adjust the historical date range in forecast settings to exclude past low-performing quarters.

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Run a report on closed won opportunities to see if the AI overestimates.