AI Associate AI Fundamentals Practice Test 3 — 15 Questions

Question 1

A Salesforce admin is setting up Einstein Activity Capture to automatically log emails and events. They are concerned about data privacy and want to ensure that only relevant emails are captured. Which configuration option should they use to exclude personal emails from being logged?

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

Accepted Answer

Set up email domain filters to exclude specific domains from being captured.. Email domain filters in Einstein Activity Capture allow admins to specify which domains should be excluded from capture. By adding personal email domains like gmail.com or yahoo.com to the exclusion list, only emails from business domains are logged. This proactive filtering ensures privacy and relevance, aligning with the admin's concern. Other options like encryption or post-capture deletion do not prevent personal emails from being captured in the first place.

Answer

Enable Einstein Activity Capture's 'Private' mode, which only logs emails from internal users.

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Use Salesforce Shield to encrypt email content so that personal emails are unreadable.

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Configure a flow to delete personal emails after they are logged by Einstein Activity Capture.

Question 2

A retail company uses Einstein Prediction Service to forecast customer churn. To improve model accuracy, which data preparation step is most critical?

Accepted Answer

Clean the dataset by handling missing values and outliers.. Handling missing values and outliers is the most critical data preparation step for Einstein Prediction Service because the underlying gradient boosting models (like XGBoost) are sensitive to data quality issues. Missing values can introduce bias or cause the model to misinterpret patterns, while outliers can disproportionately influence split decisions, reducing predictive accuracy for churn scenarios.

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Select only the top three features based on correlation.

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Use a different algorithm like neural networks.

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Increase the dataset size by collecting more customer records.

Question 3

A sales manager wants to use Einstein Activity Capture to log emails automatically. Which prerequisite must be met?

Accepted Answer

The user's email must be hosted on a supported platform (Gmail, Outlook).. Einstein Activity Capture requires that user emails be hosted on a supported platform (Gmail or Outlook/Exchange) because the feature uses server-side synchronization via APIs (Google Workspace APIs or Microsoft Graph) to automatically log emails and events into Salesforce. Without a supported email host, the service cannot connect to the mail server to capture activity data.

Answer

The org must be on Enterprise Edition or higher.

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The user must have an Einstein AI license.

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The user must manually enable email logging in personal settings.

Question 4

A company uses Einstein Bots to handle customer service inquiries. The bot often fails to understand complex requests, leading to escalations. Which improvement strategy is most effective?

Accepted Answer

Train the bot with additional intents and example phrases for complex scenarios.. Training the bot with additional intents and example phrases directly addresses the root cause of the bot's failure: insufficient training data for complex scenarios. By expanding the training corpus, the natural language understanding (NLU) model can better recognize and classify nuanced user inputs, reducing misclassifications and unnecessary escalations.

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Route all complex requests directly to human agents without bot interaction.

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Increase the confidence threshold for intent matching to avoid misclassification.

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Reduce the number of dialogue options to simplify the bot's logic.

Question 5

A nonprofit uses Einstein Vision to classify images of disaster areas. What is the primary benefit of using AI for this task?

Accepted Answer

It reduces manual effort and speeds up damage assessment.. Einstein Vision automates the classification of disaster images, significantly reducing the manual effort required for damage assessment. By processing large volumes of images rapidly, it accelerates the time to insight, enabling faster response and resource allocation. This aligns with the core benefit of AI: augmenting human effort with speed and scale.

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It requires less training data than manual methods.

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It eliminates all classification errors.

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It can only classify images of specific disaster types.

Question 6

A marketing team wants to use Einstein Engagement Scoring to prioritize leads. What is the primary input for this AI feature?

Accepted Answer

Lead interaction history with emails and web activity.. Einstein Engagement Scoring is a predictive AI feature in Salesforce Marketing Cloud that analyzes a contact's behavioral engagement signals — specifically email opens, clicks, and web activity — to generate a score predicting how likely that person is to engage with future communications. The model is trained on historical interaction patterns, so the primary input is the lead's own engagement history with emails and web channels. This score is then used to prioritize leads for marketing outreach, not to predict conversion from sales data.

Answer

Historical conversion data from closed opportunities.

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Lead demographic information like industry and company size.

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Social media posts and mentions of the company.

Question 7

Which TWO actions are best practices when implementing Einstein Prediction Service?

Accepted Answer

Clean the data to handle missing values and outliers.. Option B is correct because data quality directly determines model accuracy in Einstein Prediction Service, so missing values must be imputed or removed and outliers handled before training to avoid skewed predictions. Option C is correct because feature selection is a core best practice: choosing fields that genuinely correlate with the outcome improves model performance and reduces noise, overfitting, and training time. Options A, D, and E are not best practices: correlated features should be identified and managed rather than blindly ignored, including every available field adds noise and can degrade the model, and default field mapping should always be reviewed to ensure the correct fields are mapped to the prediction target and predictors.

Answer

Ignore correlated features to simplify the model.

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Include all available fields in the dataset for maximum information.

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Use the default field mapping without review.

Question 8

Which THREE are valid considerations when deploying an Einstein Bot?

Accepted Answer

Defining the intents the bot should handle.. Option A is correct because an Einstein Bot must have its intents defined so the bot can recognize and route user utterances to the appropriate dialog and action; intents are the core NLP mapping that determines what the bot can handle. Option B is correct because Salesforce best practice is to build and test Einstein Bots in a sandbox (or a developer sandbox with the bot deployed) before activating in production, validating dialogs, actions, and channel behavior without affecting live users. Option D is correct because training the bot with sample user dialogs (utterances) improves the intent model's accuracy, and Einstein Bots require training utterances per intent to classify user input reliably. Option C is not a valid consideration because Einstein Bots can be deployed across multiple channels (e.g., Experience Cloud sites, Messaging, and enhanced channels), not limited to a single communication channel. Option E is not valid because Einstein Bots can escalate to a human agent through standard Omni-Channel routing and live agent transfer, not only via email.

Answer

Limiting the bot to a single communication channel.

Answer

Ensuring the bot can escalate to a human agent only via email.

Question 9

Which TWO data types can be used as input for Einstein Vision?

Accepted Answer

URLs pointing to images.. Einstein Vision accepts image data for training and prediction, and one supported input form is a URL pointing to an image (option B), which lets the service fetch the image directly from a publicly accessible web address. It also accepts raw image files in formats such as JPEG and PNG (option C), which are the standard image encodings supported by the Einstein Vision APIs for classification, object detection, and OCR. Video files (option A) are not a supported input type because Einstein Vision processes still images, not MP4 video streams. Text documents such as PDFs (option D) are not image inputs for Einstein Vision, and audio files like WAV (option E) are unrelated to image recognition and would instead belong to speech or audio services.

Answer

Video files (MP4).

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Text documents (PDF).

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Audio files (WAV).

Question 10

Based on the exhibit, what does the accuracy of 0.85 indicate?

Accepted Answer

85% of the model's predictions matched the actual outcomes.. Accuracy is defined as the ratio of correctly predicted instances (both true positives and true negatives) to the total number of predictions. An accuracy of 0.85 means that 85% of the model's predictions (whether 'won' or 'lost') matched the actual outcomes in the dataset. This is a standard classification metric that evaluates overall correctness, not just one class.

Answer

85% of the features are important for prediction.

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85% of predictions that the opportunity will be won are correct.

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85% of opportunities in the training data were won.

Question 11

Based on the exhibit, what is the primary issue with this Einstein Bot conversation?

Accepted Answer

The bot lacks alternative ways to identify the customer.. The exhibit shows the bot repeatedly asking for the customer's account number without offering alternative identification methods (e.g., email, phone number, or name). This is the primary issue because Einstein Bot's conversational design should include fallback paths to handle cases where the user cannot provide the requested information, ensuring a smooth user experience and reducing drop-offs.

Answer

The bot is confused about the user's intent.

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The bot is repeating itself excessively.

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The bot does not understand the initial intent.

Question 12

A sales team is using Einstein Lead Scoring, but the scores for new leads seem inconsistent and not reflecting recent conversion patterns. The admin checks the model and finds it was trained three months ago. Which action should the admin take to improve model accuracy?

Accepted Answer

Retrain the Einstein Lead Scoring model with the latest lead data.. The correct action is A: retrain the Einstein Lead Scoring model with the latest lead data. Einstein Lead Scoring relies on a machine learning model that must be periodically retrained so it reflects current conversion patterns; a model trained three months ago will produce stale, inconsistent scores as lead behavior and win rates change. Retraining uses recent lead and opportunity outcomes to recalibrate the scoring algorithm and restore accuracy. Option B is wrong because manually overriding scores does not improve the model and can corrupt the training signal. Option C is wrong because field history retention affects audit/history tracking, not model training data. Option D is wrong because field-level security controls user access, not the fields available to the scoring model.

Answer

Manually override the lead scores for a sample of leads.

Answer

Increase the field history retention period for lead fields.

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Adjust field-level security to allow the model to access more fields.

Question 13

Refer to the exhibit. An admin configures Einstein Next Best Action with the above JSON. The expected behavior is to recommend the top 5 actions for open leads with a score of at least 70. However, only 2 recommendations appear for some leads. Which is the most likely cause?

Accepted Answer

The scoreThreshold of 70 excludes many actions, so fewer than 5 meet the criteria.. The scoreThreshold of 70 filters out any actions with a score below 70. If fewer than 5 actions meet this threshold, the system returns only those that qualify, resulting in fewer than 5 recommendations. The maxRecommendations setting defines the upper limit, but the actual number returned is constrained by the scoreThreshold.

Answer

The filter on Lead Status is incorrectly excluding actions.

Answer

The recommendation strategy is misconfigured for this object.

Answer

The maxRecommendations is set to 2 instead of 5.

Question 14

A global manufacturing company uses Sales Cloud and has implemented Einstein Opportunity Scoring to prioritize deals. The scoring model was trained on historical data and initially performed well. Over the past month, the scores have become less accurate, with many high-scoring opportunities not closing and some low-scoring ones closing. The admin notices that the sales team has been using a new discounting strategy that heavily influences deal outcomes. The admin wants to improve model performance without manual intervention. Which action should the admin take?

Accepted Answer

Retrain the Einstein Opportunity Scoring model with the latest opportunity data including discount information.. Retraining the Einstein Opportunity Scoring model with the latest opportunity data, including discount information, allows the machine learning model to automatically learn the new patterns introduced by the sales team's discounting strategy. This aligns with the AI Associate principle that models must be retrained on current data to maintain accuracy when business processes change, without requiring manual intervention.

Answer

Manually adjust the field weights for discount-related fields in the model.

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Run a data quality report to identify and clean missing discount data.

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Create a custom field for discount percentage and add it to the model.

Question 15

A marketing manager uses Einstein recommendations on their website, but customers are receiving suggestions for products they already purchased. What is the most likely cause?

Accepted Answer

The recommendation model is not filtering out previously purchased items.. C is correct because the most likely cause is that the recommendation model is not configured to exclude previously purchased items. Einstein Recommendations uses customer purchase history to personalize suggestions, but if the model's filtering logic does not explicitly remove items the customer has already bought, those items will continue to appear in the recommendations. This is a common oversight in model configuration rather than a data sync or algorithm type issue.

Answer

The product catalog is not updated with purchase history.

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The model is using real-time browsing data that includes past purchases.

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The recommendations are based on collaborative filtering without personalization.