AI Associate Flashcards — Free Salesforce AI Associate AI Associate Study Cards

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.

Users must grant access to their email and calendar via OAuth

Einstein Activity Capture requires users to explicitly grant access to their email and calendar via OAuth (Option D). This OAuth-based authentication allows Salesforce to securely sync emails and meetings from the user's email provider (e.g., Gmail, Outlook) into Salesforce records. Without this explicit consent, the feature cannot access or log the user's activity data.

Check the training data for representation and bias.

The correct first step is to check the training data for representation and bias because the model's tendency to recommend high-priced items to affluent areas suggests the training data may be skewed or contain historical biases. Einstein Prediction Builder relies on historical data to learn patterns, and if the data over-represents affluent users or under-represents others, the model will perpetuate those biases. Auditing the data for fairness and representation is the foundational step before any remediation, as per responsible AI practices.

Define the prediction objective and the target date field.

Option B is correct because Einstein Prediction Builder requires you to define the prediction objective (e.g., 'Will this customer churn?') and specify the target date field that marks the event. This step is essential as it tells the model what to predict and over what time window, enabling the automated feature engineering and model training process.

Review training data for representativeness

Reviewing training data for representativeness helps identify and mitigate bias sources.

Group them into higher-level categories (e.g., region).

Grouping high-cardinality categories into broader categories reduces overfitting and improves model stability.

Einstein Lead Scoring

Einstein Lead Scoring uses historical data to predict lead conversion probability and assign scores, enabling prioritization.

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.

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.

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.

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.

Retrain the Einstein Lead Scoring model with the latest lead data.

Option C is correct because retraining the model with recent data will capture current conversion patterns. Option A is wrong because increasing field history retention does not retrain the model. Option B is wrong because field-level security does not affect scoring. Option D is wrong because adjusting scoring ranges manually defeats the purpose of machine learning.

Omni-Channel Flow

Option D is correct because Omni-Channel Flow routes work to agents. Option A is wrong because Einstein Case Classification categorizes cases, not escalates. Option B is wrong because Einstein Article Recommendations suggests knowledge articles. Option C is wrong because Einstein Reply Recommendations suggests responses, not escalation.

Analyze the distribution of scores across industry segments.

Option D is correct because analyzing the distribution of scores across industry segments directly validates whether the model exhibits systematic bias. By comparing score distributions, the associate can identify if certain industries are consistently under-scored, which would indicate a biased pattern rather than random variation. This approach aligns with ethical AI practices that require transparency and fairness assessment before any model adjustments.

Replace zip code with more relevant non-discriminatory features and retrain with fairness constraints.

Option B is correct because replacing biased proxy with more relevant features can maintain accuracy while reducing discrimination. Option A is wrong because simply removing zip code may not eliminate all proxies. Option C is wrong because retraining with same data yields same bias. Option D is wrong because transparency alone doesn't fix bias.

Beneficence

The recommender system's shift toward sensationalist content, which polarizes users, violates the principle of beneficence because it causes harm (user polarization) rather than promoting well-being. Beneficence requires AI systems to act in the best interests of users and society, not to optimize for engagement metrics at the expense of ethical outcomes.

Add Spanish language support with separate sentiment model and intents.

The bot underperforms for Spanish speakers, so adding a Spanish sentiment model and intents would improve fairness. Option A does not fix the disparity. Option C may not solve the root cause. Option D is about privacy, not the language issue.

Retain the features but monitor for disparate impact and ensure compliance with regulations.

Removing protected attributes is a common step, but if they are proxies for legitimate medical factors, they may be retained with monitoring. Option A is too aggressive. Option C ignores that age can be medically relevant. Option D violates transparency and accountability.

Re-sample the training data to include more female candidates and use fairness-aware algorithms.

Option C is correct because ensuring gender balance in training data addresses the root cause. Option A is wrong because removing gender may not eliminate proxy variables like 'years of experience gaps.' Option B is wrong because ignoring the issue perpetuates bias. Option D is wrong because post-processing adjustments may not be sufficient without data changes.

The training data is imbalanced, causing the model to perform better on the majority group.

Option B is correct because imbalanced training data often leads to disparate performance. Option A is wrong because the model is not inherently biased. Option C is wrong because overall accuracy can be high despite bias. Option D is wrong because there is no indication of overfitting.

The reconciliation rule is not configured for the data source.

Option D is correct because reconciliation rules in Salesforce Data Cloud define how records from different data sources are matched and merged into a unified profile. If a reconciliation rule is not configured for a data source, records from that source may not be properly linked to existing profiles, leading to missing records in the unified view. This is a common configuration step that must be completed after connecting a data stream.

Investigate the categories within Case_Origin to understand their impact.

Option C is correct because the model identifies 'Case_Origin__c' as important; analyzing its categories can reveal which origins cause delays. Option A is wrong because removing the field loses information. Option B is wrong because the model already accounts for interactions. Option D is wrong because the origin is not necessarily a data quality issue.