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.

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.

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.

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.

The sentiment model was trained on a non-representative dataset.

The sentiment analysis model likely exhibits bias due to training data that does not adequately represent the linguistic patterns, idioms, or expressions of non-native English speakers. This causes the model to misinterpret neutral or positive statements from these users as negative or frustrated, leading to disproportionate escalations. A non-representative dataset is a common source of algorithmic bias in AI systems.

Analyze the distribution of scores across industry segments.

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.

Including gender to improve model accuracy.

Including gender as a feature in a predictive model for employee performance can introduce bias and lead to unfair or discriminatory outcomes. Even if the model's accuracy improves, using protected attributes like gender may violate ethical guidelines and regulations such as GDPR or anti-discrimination laws, as it could perpetuate historical biases or result in disparate impact.

Apply post-processing fairness adjustments to the recommendations.

Post-processing fairness adjustments directly modify the model's output to enforce demographic parity or equal opportunity, reducing biased recommendations without retraining the model. This approach is practical when the firm cannot easily change the underlying training data or model architecture, and it allows the AI Associate to intervene at the decision point to ensure fair lending practices.

The model is unfair to Spanish-speaking customers.

The primary ethical concern is fairness: the model was trained exclusively on English reviews, so it systematically misclassifies Spanish positive feedback as negative. This creates a disparate impact on Spanish-speaking customers, violating the principle of algorithmic fairness. The issue is not just low accuracy but an unjust bias that disadvantages a specific linguistic group.

Add Spanish language support with separate sentiment model and intents.

The correct option is D: Add Spanish language support with separate sentiment model and intents. This is right because sentiment models and intent classifiers are language-specific; Spanish text processed by an English model produces unreliable sentiment and intent predictions, so a dedicated Spanish model and Spanish intents are needed to serve those users fairly and accurately. Options A and C merely mask the problem by changing thresholds or disabling sentiment for non-English conversations, which degrades service rather than fixing the underlying language gap. Option B is wrong because collecting Spanish data to retrain the English model still conflates two languages in one model and does not provide proper Spanish language support.

To provide a secure gateway for AI data processing, including data masking and toxicity detection

The Einstein Trust Layer is designed to provide security, privacy, and governance controls for AI features within the Salesforce platform.

As a field on the lead record and in list views

Einstein Lead Scoring writes the score to a standard field on the Lead object, so it is visible directly on the lead record and can be surfaced in list views, reports, and filters. This makes prioritization actionable in the rep's normal workflow rather than requiring a separate analytics tool.

Define the prediction objective and the target date field.

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.

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.

Knowledge articles with a published status.

Knowledge articles with a published status are the correct data source because they contain curated, approved, and structured information that Einstein Bot can reliably use to generate accurate responses. The bot leverages natural language processing to match customer questions against these articles, ensuring answers are based on verified content rather than unstructured or transient data.

Investigate the categories within Case_Origin to understand their impact.

The correct option is D: investigate the categories within Case_Origin to understand their impact. When Einstein Discovery reports a field such as Case_Origin__c as highly important, the next step is to drill into that field's individual categories (e.g., Phone, Email, Web) to see which values drive longer case resolution times, enabling targeted process improvements. Option A is wrong because removing a high-importance field discards predictive signal rather than acting on the insight. Option B is premature, since interaction terms should be considered only after understanding the main effect of Case_Origin. Option C is irrelevant, as data quality thresholds do not explain why the field is important or how its categories affect resolution time.

Streaming data from Data Cloud that includes recent website interactions.

Einstein Next Best Action requires real-time or near-real-time data to personalize recommendations based on recent customer behavior. Streaming data from Data Cloud captures website interactions as they happen, enabling the recommendation engine to use the most current signals (e.g., page views, clicks) to adjust offers dynamically.