AI Associate Practice Exam 51 — 20 Questions

Question 1

A company uses Einstein Prediction Builder to score leads. The model systematically gives lower scores to leads from a particular geographic region, even though those leads often convert. Which action should the company take to address this ethical concern?

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

Accepted Answer

Retrain the model with a balanced dataset that includes more leads from the under-scored region.. Retraining with a balanced dataset helps mitigate bias by ensuring the model learns from a representative sample, aligning with fairness principles.

Answer

Switch to a different AI vendor.

Answer

Ignore the bias because the model is proprietary.

Answer

Remove the region field from the model entirely.

Question 2

A Salesforce admin is configuring Einstein Search for an organization with users in multiple countries. Which ethical consideration is most important?

Accepted Answer

Language bias in the search results.. Language bias can lead to unequal search quality for non-English speakers, making it a key ethical concern.

Answer

Stop word exclusion.

Answer

Query performance optimization.

Answer

Tokenization settings.

Question 3

Refer to the exhibit. An admin runs two queries on the Lead object. Both include the custom field Score__c used by Einstein Lead Scoring. The second query is significantly slower. What is the most likely cause?

Accepted Answer

The query is not using a selective filter on Score__c. Einstein Lead Scoring uses the Score__c field to store lead scores, and the field is not indexed by default. When a query filters on Score__c without an index, it forces a full table scan on the Lead object, which becomes significantly slower as the number of leads grows. The second query likely includes a non-selective filter on Score__c (e.g., a range or inequality), which cannot leverage any existing index and thus degrades performance.

Answer

The query uses a date function that is not selective

Answer

The Score__c field is indexed

Answer

The Einstein Lead Scoring job is running simultaneously

Question 4

A Salesforce admin is configuring Einstein Conversation Insights. They want to automatically capture next steps from sales call recordings. Which component of Conversation Insights provides this functionality?

Accepted Answer

Next step capture (powered by Einstein NLP). Einstein Conversation Insights uses Einstein Natural Language Processing (NLP) to automatically analyze sales call transcripts and extract actionable next steps, such as follow-up tasks or commitments. This feature is specifically designed to identify and capture these items without manual effort, leveraging AI to parse conversational context.

Answer

Call recording transcription

Answer

Keyword tracking

Answer

Talk-time metrics

Question 5

Which THREE components are essential for an ethical AI governance framework within a large enterprise?

Accepted Answer

Establish a cross-functional AI ethics board.. Options A, B, and C are correct. An ethics board provides oversight, impact assessments identify risks, and accountability ensures responsibility. Minimizing human oversight (D) contradicts governance. Only focusing on accuracy (E) neglects other ethical dimensions.

Answer

Minimize human oversight to reduce operational costs.

Answer

Optimize for accuracy as the primary goal.

Question 6

A company is deploying an AI-powered chatbot for customer service. The chatbot is trained on historical support tickets. Which ethical consideration is MOST important to address before deployment?

Accepted Answer

Checking for biased or discriminatory patterns in training data. Historical data may contain biased responses, leading to unfair treatment of customers. Option A is wrong because cost is a business consideration, not ethical. Option B is wrong while performance is important, it is secondary to fairness. Option D is wrong because maintenance is operational.

Answer

Minimizing the cost of AI training

Answer

Ensuring the chatbot responds quickly to all queries

Answer

Planning for regular model retraining

Question 7

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.. 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 8

Refer to the exhibit. What is the most likely cause of the fairness issue?

Accepted Answer

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

Answer

The model overfits to the male group.

Answer

The overall accuracy is too low.

Answer

The model is inherently biased against females.

Question 9

A company uses Einstein Forecasting for revenue prediction. The historical data shows seasonal spikes every quarter. The model consistently underestimates peak periods. What is the best data preparation step to improve accuracy?

Accepted Answer

Add a 'quarter' index field (1-4) to the dataset.. Einstein Forecasting can detect seasonality if the data contains enough history and a seasonality marker. Adding a 'quarter' feature explicitly helps the model capture recurring patterns.

Answer

Increase the forecast horizon to 12 months.

Answer

Remove the spike data points as outliers.

Answer

Use only the last 6 months of data to reduce noise.

Question 10

What is the most likely cause of the error?

Accepted Answer

Data quality threshold violation. The error mentions a high percentage of null values in a critical field, which violates a data quality threshold. Option A is wrong because schema mismatch would show field type inconsistencies. Option C is wrong because authentication failure would show a different error. Option D is wrong because network timeout would mention connection issues.

Answer

Authentication failure

Answer

Data schema mismatch

Answer

Network timeout

Question 11

A marketing team wants to use Einstein Recommendations to personalize product offers on their e-commerce site. They have a dataset of 50,000 customers with purchase history. However, 40% of customers have no purchase history (new registrations). The model performs well for returning customers but gives generic recommendations for new ones. The team wants to improve recommendations for new customers. What data preparation step should they take?

Accepted Answer

Impute missing purchase history with the average purchase frequency across all customers.. Imputing missing purchase data with a sensible default (e.g., average purchase frequency) gives the model signal for new customers, improving recommendations without discarding data.

Answer

Remove all customers with missing purchase history from the training set.

Answer

Assign a random purchase frequency to each new customer to add variety.

Answer

Use only customers with complete purchase history to train a more accurate model.

Question 12

Refer to the exhibit. A developer runs this SOQL query to prepare data for Einstein Lead Scoring. The query returns an error. What is the most likely issue?

Accepted Answer

The alias 'TotalAmount' is not allowed in the HAVING clause.. The HAVING clause references alias TotalAmount, but SOQL does not allow aliases in HAVING; the aggregated expression must be repeated.

Answer

The query misses a GROUP BY clause.

Answer

The SUM(Amount) cannot be used in the HAVING clause.

Answer

The WHERE clause condition is invalid.

Question 13

Which THREE of the following are best practices for feature engineering in Einstein Studio?

Accepted Answer

Apply normalization to numerical features. Options B, D, and E are correct. Normalization scales features, domain knowledge creates meaningful derived features, and one-hot encoding handles categorical variables. Option A is wrong because raw data often needs processing. Option C is wrong because removing missing values can lose information; imputation is often better.

Answer

Remove all records with missing values

Answer

Use raw data directly without any transformation

Question 14

A fraud detection model is being trained on transaction data where only 1% of transactions are fraudulent. The current model predicts 'non-fraud' for all transactions, achieving 99% accuracy. Which technique should be applied to improve model performance?

Accepted Answer

Oversample the minority class or undersample the majority class. Oversampling or undersampling addresses class imbalance, allowing the model to learn minority patterns. Using more features alone doesn't fix imbalance, setting a lower threshold may help but is less common than resampling, and removing minority class is counterproductive.

Answer

Remove the minority class to have balanced data

Answer

Set a lower classification threshold for fraud

Answer

Add more features like transaction location

Question 15

Refer to the exhibit. What data quality issue does the exhibit reveal?

Accepted Answer

The Fall Clearance campaign has no response data.. The Fall Clearance campaign has zero non-null responses, indicating all response data is missing for that campaign.

Answer

The Summer Sale campaign has duplicate records.

Answer

The query syntax is incorrect.

Answer

The data is not normalized.

Question 16

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 17

A company wants to use Einstein Vision for product categorization. To ensure ethical use, they should:

Accepted Answer

Test the model for bias across different demographic groups.. Testing for bias across demographic groups helps ensure the model treats all users fairly.

Answer

Avoid using any images that contain people.

Answer

Use only high-resolution images.

Answer

Only use images from a single demographic.

Question 18

An AI system for hiring is deployed. After six months, the HR team notices that the model's recommendations closely mimic past human hires, which were biased. The team wants to correct this. What should be their first step?

Accepted Answer

Implement continuous monitoring and a feedback loop to detect and mitigate bias. Continuous monitoring and feedback loops can detect and correct drift or bias. Option A is wrong because removing the model does not solve underlying bias. Option C is wrong because past data already contains bias. Option D is wrong because complete transparency does not automatically correct bias.

Answer

Shut down the AI system entirely

Answer

Retrain the model with the same historical data but with more features

Answer

Make the model's decision process fully transparent to all candidates

Question 19

An organization uses Einstein Discovery to analyze survey data. The model reveals a correlation between age and satisfaction. What is the responsible use of this insight?

Accepted Answer

Investigate causality before action. Correlation does not imply causation; responsible use involves investigating the underlying cause before acting.

Answer

Act on the correlation immediately

Answer

Discard the result

Answer

Publish the result as-is

Question 20

When implementing AI in Salesforce, which practice best supports the ethical principle of transparency?

Accepted Answer

Provide human-readable explanations for each AI prediction. Transparency requires that the logic and outcomes of AI systems are understandable. Option A is correct because providing explanations for predictions allows users to understand and trust the AI. Option B (keeping proprietary algorithms secret) hinders transparency. Option C (using complex models without explanation) obscures decision-making. Option D (only reporting accuracy metrics) does not explain specific decisions.

Answer

Use proprietary algorithms without disclosing their logic

Answer

Deploy a complex neural network model without interpretability features

Answer

Only report overall model accuracy metrics to end users