AI Associate AI Fundamentals Practice Test 2 — 15 Questions

Question 1

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.

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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 2

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 3

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 4

A company deploys Einstein Recommendation Builder on its e-commerce site. The recommendations are not personalized. What is the most likely cause?

Accepted Answer

The model has not been trained with enough user behavior data.. Einstein Recommendation Builder relies on user interaction data to personalize. If insufficient data exists, recommendations become generic. Option A is correct. Option B is wrong because real-time sync is not required. Option C is wrong because the builder can work without a data scientist. Option D is wrong because the model can recommend products beyond categories.

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The company did not hire a data scientist to tune the model.

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The recommendation engine is not syncing in real-time with the website.

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The product catalog is too large for the model to process.

Question 5

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 analyzes lead interactions (email opens, clicks, web visits) to calculate engagement scores. Option A is correct. Option B is wrong because demographic data is not the primary input. Option C is wrong because historical conversion data is used for predictive scoring, not engagement. Option D is wrong because social media data is not a direct input.

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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 6

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

Accepted Answer

Clean the data to handle missing values and outliers.. Data quality directly impacts the accuracy and reliability of Einstein Prediction Service models. Cleaning data to handle missing values and outliers ensures that the training data is representative and reduces the risk of skewed predictions, which is a fundamental prerequisite for any machine learning model within Salesforce's AI framework.

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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 7

Which THREE are valid considerations when deploying an Einstein Bot?

Accepted Answer

Defining the intents the bot should handle.. Defining intents is a core requirement for an Einstein Bot because intents represent the specific goals or tasks users want to accomplish, such as checking an order status or resetting a password. The bot uses Natural Language Processing (NLP) to map user utterances to these intents, enabling it to route conversations appropriately. Without clearly defined intents, the bot cannot accurately understand or respond to user requests.

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Limiting the bot to a single communication channel.

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

Question 8

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

Accepted Answer

URLs pointing to images.. Einstein Vision is designed to analyze and classify visual content, specifically images. It accepts image files (JPEG, PNG) and URLs pointing to images as input, allowing the model to process the visual data for object detection, classification, or other vision tasks. Video, text, and audio files are not supported because the service is not built for temporal or non-visual data.

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Video files (MP4).

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Text documents (PDF).

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Audio files (WAV).

Question 9

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.

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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 10

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.

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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 11

A company wants to use Einstein Activity Capture to log emails and events automatically. Which two considerations should the admin evaluate before enabling this feature?

Accepted Answer

Users must link their email client (Gmail or Outlook).. Einstein Activity Capture requires users to link their email client (Gmail or Outlook) to Salesforce via OAuth 2.0 authentication. This linkage allows the feature to automatically log emails and events from the connected email and calendar systems without manual user intervention.

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The feature is only available for Sales Cloud.

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All lead and contact fields must be visible to users.

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Users must install a browser plugin.

Question 12

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.

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The recommendation strategy is misconfigured for this object.

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The maxRecommendations is set to 2 instead of 5.

Question 13

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 14

A nonprofit organization uses Salesforce to manage donor relationships. They have implemented Einstein Prediction Builder to predict which donors are likely to upgrade their donation level in the next 90 days. The model was built using a custom object "Donation" with fields like Amount, Frequency, and Campaign. After deployment, the predictions seem random and do not correlate with donor engagement. The admin suspects the model is not trained on enough records. The organization has 500 donors with at least two donations each. What should the admin do to improve the model?

Accepted Answer

Ensure that at least 500 records exist where the donor actually upgraded, and retrain the model.. Einstein Prediction Builder requires a minimum number of positive outcome records (upgrade events) to train a reliable model. With only 500 donors and likely far fewer upgrades, the model lacks sufficient signal. Ensuring at least 500 actual upgrade records provides the necessary positive examples for the algorithm to learn meaningful patterns, reducing randomness in predictions.

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Increase the prediction window from 90 to 180 days to capture more upgrade events.

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Use a different field as the prediction outcome, such as 'donation amount increase'.

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Add more fields to the model, such as donor age and geographic location.

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.

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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.