AI Associate Practice Test 9 — 10 Questions

Question 1

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The model has detected data drift, indicating the training data may no longer represent current patterns..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 This is the admin's primary concern because a model trained on outdated patterns will produce unreliable predictions, even if its accuracy or AUC were initially high.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.

Answer

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

Answer

The model uses too few features to be reliable.

Question 2

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.

Answer

Flow

Answer

Quick Actions

Answer

Process Builder

Question 3

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.

Answer

Remove the field from the model.

Answer

Increase the data refresh frequency.

Answer

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

Question 4

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

Answer

Map lead fields to Einstein fields

Answer

Grant the admin the 'Manage Einstein' permission

Question 5

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.

Answer

Include at least 10 different action types per strategy

Answer

Use only historical data from the last 30 days

Question 6

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.

Answer

Storage efficiency

Answer

Encryption strength

Answer

Consistency with external benchmarks

Question 7

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.

Answer

Use data partitioning to improve query performance.

Answer

Implement role-based access control on datasets.

Answer

Regularly run data profiling to check completeness.

Question 8

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.

Answer

Train a separate model per customer segment and then merge.

Answer

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

Answer

Build an ensemble of models and average their outputs.

Question 9

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.

Answer

The model was not retrained after the last data load.

Answer

The training data contains predominantly neutral examples.

Answer

The field mapping for the sentiment field is incorrect.

Question 10

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.

Answer

Einstein Prediction Builder

Answer

Einstein Discovery

Answer

Einstein Opportunity Scoring