AI Associate Hard Questions — 25 Difficult Questions

Question 1

A data architect is designing a data model for Einstein Discovery. The data includes categorical variables with high cardinality (e.g., postal codes). What is the best practice to handle such features?

Accepted Answer

Group them into higher-level categories (e.g., region).. Grouping high-cardinality categories into broader categories reduces overfitting and improves model stability.

Answer

Encode them as one-hot vectors.

Answer

Exclude them from the model.

Answer

Use the raw values without transformation.

Question 2

A credit scoring AI uses 50 features including zip code, age, and income. The model has high accuracy but denies credit disproportionately to a protected group. An audit reveals that zip code is a proxy for race. What is the best course of action?

Accepted Answer

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.

Answer

Remove zip code from the feature set and retrain.

Answer

Keep zip code but add a fairness penalty to the loss function.

Answer

Increase transparency by publishing the model's decision criteria.

Question 3

A company deploys an AI recommender system that personalizes content. The system is trained on user click data. After deployment, the company notices that the system increasingly recommends sensationalist content, leading to user polarization. Which principle is being violated?

Accepted Answer

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.

Answer

Accuracy

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Privacy

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Transparency

Question 4

A healthcare organization uses Einstein Discovery to predict patient readmission risk. The model uses protected attributes like race and age as features. Which action best aligns with Salesforce's ethical AI principles?

Accepted Answer

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.

Answer

Remove race and age features entirely to ensure fairness.

Answer

Replace age with an age group bucket to reduce granularity.

Answer

Use the model as is because predictions are accurate.

Question 5

A company uses Salesforce Data Cloud to unify customer data from multiple sources. After connecting a data stream, they notice that records are missing from the unified profile. What is the most likely cause?

Accepted Answer

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.

Answer

The data stream object is not a standard Salesforce object.

Answer

The data stream is not activated for identity resolution.

Answer

The data source is not from Salesforce, so it cannot be unified.

Question 6

A company has set up Einstein Next Best Action with a recommendation strategy. They want to ensure that recommendations are personalized based on the customer's recent behavior. What data should be used?

Accepted Answer

Streaming data from Data Cloud that includes recent website interactions.. Option B is correct because 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.

Answer

Event data from the website tracked via Google Analytics.

Answer

Static profile fields like customer age and location.

Answer

Historical data from a data warehouse updated daily.

Question 7

A data scientist is building a predictive model for customer churn using Salesforce data. The dataset has 20 features, and the target variable is highly imbalanced (5% churn, 95% non-churn). Which technique should be applied to handle the class imbalance before training?

Accepted Answer

Use Synthetic Minority Over-sampling Technique (SMOTE). SMOTE (Synthetic Minority Over-sampling Technique) is the correct choice because it generates synthetic samples for the minority class (churn) by interpolating between existing minority instances, effectively balancing the dataset without simply duplicating data. This prevents the model from being biased toward the majority class (non-churn) and improves recall for the churn class, which is critical in imbalanced classification problems.

Answer

Apply Principal Component Analysis (PCA) for dimensionality reduction

Answer

Create interaction features between existing variables

Answer

Use accuracy as the evaluation metric

Question 8

A Salesforce admin notices that Einstein Account Scoring is not generating scores for all accounts. Some accounts have no score even though they meet the data requirements. What is the most likely cause?

Accepted Answer

The org has fewer than 100 account records. Einstein Account Scoring requires a minimum of 100 account records to generate meaningful scores. If the org has fewer than 100 accounts, the model cannot establish a baseline for scoring, resulting in no scores for any account. This is a hard threshold enforced by the Einstein AI engine, not a configurable setting.

Answer

Einstein features are not enabled in the org

Answer

Users do not have the 'View Scores' permission

Answer

The accounts lack custom fields for scoring

Question 9

A company uses Einstein Prediction Builder to predict whether a lead will convert. The model's confidence score is low, and the admin wants to improve accuracy. What is the most effective action?

Accepted Answer

Increase the amount of historical data for training. Option B is correct because increasing the amount of high-quality historical data provides more examples for the model to learn from, improving accuracy. Option A is incorrect because more features can improve accuracy but only if they are relevant; just adding any field may cause noise. Option C is incorrect because Einstein models train automatically on your data, and you cannot directly increase training frequency. Option D is incorrect because training multiple models is not supported and doesn't improve the single model.

Answer

Create multiple prediction models and average their scores

Answer

Add more custom fields as predictor fields

Answer

Increase the frequency of model retraining

Question 10

A service team uses Einstein Discovery to analyze customer churn. The story shows 'Average Resolution Time' is a key driver. What is the best action?

Accepted Answer

Reduce Average Resolution Time through process changes. Einstein Discovery identifies drivers; reducing resolution time directly addresses root cause. Custom reports just show data, flows send alerts, processes update records but don't reduce time.

Answer

Update account records via process builder

Answer

Configure a flow to send churn alerts

Answer

Create a custom report on churn

Question 11

An organization uses Einstein Search to power a portal's search functionality. Users report that search results are not ranking relevant documents highly. Which configuration change is most likely to improve relevance?

Accepted Answer

Tune the search algorithm by promoting frequently accessed content.. Option B is correct because search relevance is improved by promoting popular or authoritative content. Option A is wrong because more fields may add noise. Option C is wrong because refresh frequency affects freshness but not ranking algorithm. Option D is wrong because stemming and synonyms help recall but not necessarily ranking.

Answer

Enable stemming and synonyms.

Answer

Increase the number of indexed fields.

Answer

Decrease the index refresh frequency.

Question 12

A financial services company is deploying Einstein Prediction Builder to predict customer churn. The data includes both numerical and categorical fields. Which step is essential to ensure the model is not biased against protected attributes like race or gender?

Accepted Answer

Exclude any protected attributes from the training data and ensure the model does not use correlated proxies.. Excluding protected attributes like race or gender from the training data and ensuring the model does not use correlated proxies is essential to prevent bias in Einstein Prediction Builder. This approach directly removes the risk of the model learning discriminatory patterns based on these attributes, as the platform relies on the data provided and does not automatically enforce fairness constraints. Including such attributes or relying on built-in fairness would not guarantee unbiased predictions because the model could still infer protected characteristics from correlated features.

Answer

Include race and gender as predictors to allow the model to adjust for them.

Answer

Rely on the model's built-in fairness constraints.

Answer

Use a deep learning algorithm to automatically handle bias correction.

Question 13

A developer is implementing retrieval augmented generation (RAG) for a customer service bot. Which component is essential for supplying real-time data to the prompt?

Accepted Answer

A vector database for embedding and retrieval. Correct: A vector database stores embeddings for efficient retrieval of relevant context. Option A: A fine-tuned LLM is static. Option B: Prompt Builder is for templates. Option D: A knowledge base alone doesn't provide real-time retrieval augmentation unless indexed.

Answer

A fine-tuned large language model

Answer

A prompt template in Prompt Builder

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A static knowledge base

Question 14

Refer to the exhibit. An admin runs a preprocess script before training an Einstein model. Why is normalization applied to the 'AnnualRevenue' and 'NumberOfEmployees' columns?

Accepted Answer

To ensure both features contribute equally to the model. Normalization scales features like 'AnnualRevenue' and 'NumberOfEmployees' to a comparable range (e.g., 0–1 or with zero mean and unit variance). Without normalization, a feature with larger numeric values (e.g., revenue in millions) would dominate distance-based calculations in models like k-nearest neighbors or gradient descent, causing the model to undervalue the smaller-scale feature. By normalizing, both features contribute equally to the model's learning process, which is essential for many machine learning algorithms used in Einstein.

Answer

To detect outliers in the data

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To remove rows with missing values

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To reduce the number of features from 30 to 2

Question 15

A sales operations admin wants to use Einstein Opportunity Scoring. Which two steps are required to activate Einstein Opportunity Scoring? (Select two answers.)

Accepted Answer

Enable Einstein in the Einstein Setup menu.. Options B and C are correct: you need historical data (100 won opps in 18 months) and enable Einstein. Option A is incorrect because the permission set is needed for user access but not for activation. Option D is optional, not required. Option E is incorrect because you don't need to deactivate other scoring rules.

Answer

Create a custom scoring model specific to the business.

Answer

Deactivate any existing scoring rules.

Answer

Assign the Einstein Opportunity Scoring permission set to users.

Question 16

An organization is implementing Einstein AI for sales forecasting. They have multiple custom objects and complex approval processes. Which design consideration is most critical for ensuring accurate AI predictions?

Accepted Answer

Implementing model explainability to verify that predictions align with business logic.. Option C is correct because model interpretability is essential to understand why predictions are made, especially in complex environments. Option A is wrong while relevant, user training is secondary. Option B is wrong as too much data can be noise, but it's not the most critical. Option D is wrong because a data warehouse may not be necessary.

Answer

Ensuring the data set includes at least 100,000 records per prediction field.

Answer

Creating a separate data warehouse to aggregate data from all objects.

Answer

Providing extensive user training on AI concepts.

Question 17

A global company needs to ensure that customer data used for AI models complies with multiple regional regulations (GDPR, CCPA, LGPD). Which data governance practice is most effective?

Accepted Answer

Use a unified data catalog with tagging and classification.. Option D is correct because a unified data catalog with tagging and classification allows the organization to manage data governance and compliance across regions consistently.

Answer

Apply the strictest regulation globally.

Answer

Store all data in a single data warehouse.

Answer

Allow each region to manage its own data separately.

Question 18

A data pipeline fails intermittently when processing large CSV files. The error log shows 'OutOfMemoryError'. Which configuration change is most likely to resolve this?

Accepted Answer

Increase the heap memory for the processing application.. The OutOfMemoryError indicates that the Java Virtual Machine (JVM) heap space is exhausted while processing large CSV files. Increasing the heap memory (e.g., using -Xmx flag) allocates more memory to the application, allowing it to handle larger datasets without crashing. This directly addresses the root cause of insufficient memory for the data pipeline's processing workload.

Answer

Use a smaller file size limit.

Answer

Increase the number of worker threads.

Answer

Switch to XML format.

Question 19

A data scientist is using Einstein Discovery to analyze sales data. The model results show a high correlation between two predictor variables. Which TWO actions should the data scientist take?

Accepted Answer

Combine them into a single feature.. Removing one correlated variable or combining them reduces multicollinearity.

Answer

Apply regularization.

Answer

Include both to capture more information.

Answer

Increase the sample size.

Question 20

A data scientist notices that an Einstein model for predicting customer churn has unusually high accuracy on training data but performs poorly on validation data. Which data issue is the most likely cause?

Accepted Answer

A field containing future information (e.g., 'churn_date') was included in features. Option D is correct because including a field like 'churn_date' in the feature set introduces target leakage, where the model has access to information that would not be available at prediction time. This causes the model to appear highly accurate on training data (since it can directly 'see' the outcome) but fails to generalize to validation data where such future information is absent. In Salesforce Einstein, features must be strictly historical or static to avoid this data leakage issue.

Answer

The dataset has an imbalanced class distribution

Answer

The dataset contains many missing values

Answer

The model was trained on stale data from a different season