AI Associate Practice Test 28 — 15 Questions

Question 1

A company is deploying an AI-powered chatbot to handle customer service inquiries. The bot uses historical chat data for training. Which ethical consideration is MOST important to address before deployment?

Accepted Answer

Obtaining consent from customers whose data is used for training. The most critical ethical consideration is obtaining consent from customers whose historical chat data is used to train the chatbot. Under regulations like GDPR and CCPA, personal data (including chat transcripts) requires explicit consent for processing, especially when used to train an AI model. Deploying without consent violates data privacy laws and erodes user trust, regardless of the chatbot's technical performance.

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Maximizing the chatbot's response accuracy

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Ensuring the chatbot can handle high traffic volumes

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Designing a human handoff protocol for complex issues

Question 2

A data analyst is troubleshooting Einstein Article Recommendations that are not showing up on the site. Which TWO checks should be performed first? (Choose 2)

Accepted Answer

Check that article view events are being captured in the data. Option C is correct because Einstein Article Recommendations rely on user interaction data, specifically article view events, to generate personalized recommendations. If these events are not being captured, the model has no input to learn from, and recommendations will not appear. Checking event capture is a fundamental first step in troubleshooting data pipeline issues.

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Ensure at least 100 articles are in the knowledge base

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Confirm that article authors have the correct profile permissions

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Increase the recommendation frequency from daily to hourly

Question 3

Which TWO techniques are commonly used to handle missing values in a dataset for AI training?

Accepted Answer

Deletion of rows with missing values. Option B is correct because deleting rows with missing values is a straightforward technique to handle missing data, especially when the missingness is random and the dataset is large enough that removing a few rows does not significantly impact model performance. This approach avoids introducing bias from imputation methods but can lead to loss of valuable information if too many rows are removed.

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L1 regularization

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One-hot encoding

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Min-max normalization

Question 4

A company is developing a chatbot for customer service. They want to ensure the bot does not generate offensive responses. Which practice should they implement?

Accepted Answer

Deploy a content filter and human-in-the-loop moderation for sensitive interactions.. Option D is correct because deploying a content filter combined with human-in-the-loop moderation provides both automated detection of offensive language and human oversight for ambiguous or sensitive interactions. This layered approach ensures that the chatbot can block clearly toxic outputs in real time while allowing human reviewers to handle edge cases, reducing the risk of generating offensive responses.

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Use unsupervised learning to allow the bot to learn naturally from conversations.

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Remove all toxicity detection to avoid false positives.

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Train the model only on customer service transcripts without review.

Question 5

A company wants to use Einstein Article Recommendations to surface relevant knowledge articles to its support agents. What two data components are required to set up this feature?

Accepted Answer

Knowledge Article View event data and Case records. Einstein Article Recommendations uses historical Knowledge Article View event data to understand which articles agents have found useful in the past, and Case records to provide context about the current issue. By analyzing patterns between case attributes and article views, the AI can predict and surface the most relevant articles for a given case. Without both data components, the recommendation engine cannot learn the association between case details and article usefulness.

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Email-to-case logs and Knowledge Article feedback

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Knowledge Article categories and Case priority

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Community user activity and Knowledge Article ratings

Question 6

Refer to the exhibit. A developer configured a grounding policy for Einstein GPT. What is the effect of the fallbackBehavior set to 'USE_MODEL_KNOWLEDGE'?

Accepted Answer

The AI will rely on its pre-trained knowledge when no grounding data meets the relevance threshold. When fallbackBehavior is set to 'USE_MODEL_KNOWLEDGE', the Einstein GPT grounding policy instructs the AI to fall back to its pre-trained (model) knowledge if the retrieved grounding data does not meet the configured relevance threshold. This ensures the AI still generates a response based on its internal training rather than returning no answer or ignoring the policy entirely.

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The AI will return all retrieved grounding data in the response

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The AI will not generate a response if no grounding data is found

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The AI will ignore the grounding policy and only use model knowledge

Question 7

A nonprofit organization wants to use Einstein Bots to handle inquiries on their website. They are concerned that the bot may give incorrect or insensitive responses. Which feature should they prioritize to maintain trustworthy AI?

Accepted Answer

Implement a fallback to human agent for uncertain queries.. Option C is correct because implementing a fallback to a human agent for uncertain queries directly addresses the risk of incorrect or insensitive responses by ensuring that when the bot cannot confidently answer, the conversation is escalated to a human. This aligns with the principle of maintaining trustworthy AI, as it prevents the bot from generating potentially harmful or inaccurate responses and provides a safety net for complex or sensitive inquiries.

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Use a larger training dataset from generic internet sources.

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Allow the bot to generate responses only from pre-authored articles.

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Disable sentiment analysis to avoid misinterpretation.

Question 8

A company is deploying Einstein Prediction Builder to predict equipment failure. Which three considerations are essential for building an accurate prediction model? (Choose 3)

Accepted Answer

Missing values in features should be handled appropriately.. Option A is correct because missing values in features can introduce bias or cause errors in the predictive model. Einstein Prediction Builder automatically handles missing data through imputation, but understanding how missing values are treated is essential for model accuracy, as inappropriate handling can distort relationships between features and the target outcome.

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All input features must be numerical.

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The model should be retrained only once after initial deployment.

Question 9

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

Accepted Answer

Clean the data to handle missing values and outliers.. Option B is correct because 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 10

A user asks Einstein GPT to generate a product description. The AI returns a response with a confidence score of 0.65. What does this score indicate?

Accepted Answer

The model is 65% confident that the response is accurate. Option B is correct because the confidence score in AI models like Einstein GPT quantifies the model's internal certainty that its generated output is factually correct or contextually appropriate. A score of 0.65 means the model estimates a 65% probability that the response is accurate based on its training and inference algorithms, not that it matches training data or has a fixed length.

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There is a 65% probability that the response exactly matches the training data

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The response is 65% shorter than the optimal length

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The model has a 65% likelihood of generating the same response again

Question 11

During the data preparation phase for an AI model, a data engineer discovers that the 'AnnualRevenue' field contains some negative values. What is the best course of action?

Accepted Answer

Investigate the data source to correct the negative values. Option D is correct because negative revenue values typically indicate data entry errors, system bugs, or incorrect data transformations. The best practice in data preparation is to investigate the source system to understand why negative values were generated and correct them at the origin, ensuring data integrity before any imputation or deletion. Simply deleting or imputing without root-cause analysis can introduce bias or mask underlying data quality issues.

Answer

Delete all records with negative revenue

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Replace negative values with the mean of positive values

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Keep negative values as they might represent returns or refunds

Question 12

A data scientist is preparing data for Einstein Discovery. The dataset has 10,000 records with 5 predictors and one outcome. The outcome is binary (1/0). What is the minimum number of positive outcomes typically required for a reliable model?

Accepted Answer

250. Option A is correct because for binary classification with 10,000 records and 5 predictors, a common rule of thumb in predictive modeling (including Einstein Discovery) is to have at least 10 events per predictor variable (EPV). With 5 predictors, you need at least 50 positive outcomes, but to ensure model stability and reliable training, a minimum of 250 positive outcomes (5% of 10,000) is typically required. This aligns with best practices for avoiding overfitting and achieving adequate statistical power.

Answer

500

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100

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50

Question 13

Which TWO of the following are key principles of trustworthy AI according to Salesforce's AI ethics guidelines?

Accepted Answer

Transparency. Transparency is a core principle of trustworthy AI because it requires that AI systems operate in a way that is open and understandable, allowing stakeholders to see how decisions are made. Salesforce's AI ethics guidelines emphasize transparency to ensure that users can trust the system's outputs and that any biases or limitations are visible.

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Profitability

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Complexity

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Accountability

Question 14

Which THREE factors should be considered when selecting features for a predictive model in Salesforce?

Accepted Answer

Correlation between features to avoid multicollinearity. Option B is correct because multicollinearity occurs when two or more features are highly correlated, which can destabilize model coefficients and reduce interpretability. In Salesforce's predictive models, such as those built with Einstein Discovery, correlated features can inflate variance and lead to unreliable predictions. Avoiding multicollinearity ensures that the model's feature importance estimates are trustworthy and that the model generalizes well to new data.

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Volume of data available for each feature

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Compliance with data privacy regulations

Question 15

A company is ingesting data from multiple sources into Data Cloud for Einstein. Which THREE data preparation steps should be performed?

Accepted Answer

Normalization. Normalization is correct because Data Cloud requires data from multiple sources to be transformed into a consistent format, such as standardizing date formats, units, or naming conventions, to ensure the data can be unified and analyzed effectively. This step is critical for Einstein AI models to process data without inconsistencies that could skew predictions or insights.

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Encryption

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Data labeling