AI Associate Practice Test 4 — 10 Questions

Question 1

A Salesforce admin wants to use Einstein GPT to generate personalized email content for a marketing campaign. To ensure the AI does not produce responses that include sensitive customer data or violate company policies, which Salesforce feature should the admin configure?

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

Accepted Answer

Einstein Trust Layer. Einstein Trust Layer is the correct feature because it acts as a governance and security layer between Salesforce and the large language model (LLM). It automatically masks sensitive customer data (e.g., personally identifiable information) before the prompt is sent to the LLM and then unmasks the response, ensuring the AI never sees or exposes sensitive information. This directly addresses the admin's need to prevent responses containing sensitive data or violating company policies.

Answer

Prompt Builder

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Data Cloud

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Einstein Studio

Question 2

Which feature allows administrators to create and manage prompt templates for Einstein GPT features, such as Field Generation and Sales Email templates?

Accepted Answer

Prompt Builder. Prompt Builder is the dedicated Salesforce tool for creating and managing prompt templates that are used by Einstein GPT features like Field Generation and Sales Email templates. It allows administrators to define the structure, context, and variables for prompts that guide generative AI outputs within the Salesforce platform.

Answer

Einstein Copilot

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Agent Builder

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Einstein Studio

Question 3

A company wants to build an Einstein Bot that can handle order status inquiries and, if the customer is frustrated, hand off to a human agent. Which THREE steps are essential to implement this?

Accepted Answer

Configure a hand-off action to a human agent. Option B is correct because an Einstein Bot hands off to a live agent through a configured hand-off action (the 'Transfer to Agent' action), which routes the conversation to an Omni-Channel queue so a human agent can take over when frustration is detected. Option C is correct because the bot must define a dialog (a conversation flow built from dialogs and steps) that actually retrieves and presents the order status, which is the core task the bot is meant to handle. Option E is correct because the bot needs an intent such as 'Order Status' so its natural language model can recognize and classify the customer's request and trigger the appropriate dialog. Option A is not essential because Einstein Bots include built-in sentiment detection in the bot's dialog logic rather than requiring a separate Einstein Sentiment Analysis implementation. Option D is not essential because Einstein Bots use the platform's built-in NLP and intent model, so training a custom model with Einstein Platform Services is unnecessary for this scenario.

Answer

Use Einstein Sentiment Analysis to detect frustration

Answer

Train a custom NLP model using Einstein Platform Services

Question 4

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. 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.

Answer

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 5

A company wants to create a custom AI model that predicts whether a support case will be escalated based on historical case data. Which tool allows building this custom prediction without writing code?

Accepted Answer

Einstein Prediction Builder. Einstein Prediction Builder is the correct tool because it allows users to create custom predictive models—such as predicting case escalation—directly from Salesforce data using a point-and-click interface, without writing any code. It leverages historical case data to train a model that outputs a probability score for the target outcome, making it ideal for this no-code custom AI requirement.

Answer

Einstein Case Classification

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Einstein Next Best Action

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Einstein Discovery

Question 6

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

Accepted Answer

Apply normalization to numerical features. Option B is correct because applying normalization (e.g., min-max scaling or z-score standardization) to numerical features puts them on a comparable scale, which improves the convergence and performance of many machine learning algorithms used in Einstein Studio. Option D is correct because using domain knowledge to create derived features (such as ratios, aggregates, or time-based calculations) surfaces business-relevant signal that raw columns may not express, boosting model accuracy. Option E is correct because one-hot encoding converts categorical variables into binary indicator columns, which is required since most ML algorithms in Einstein Studio cannot consume raw string categories directly. Option A is not a best practice because dropping every record with missing values discards potentially valuable data and can bias the model; imputation or missing-value indicators are preferred. Option C is not a best practice because feeding raw, untransformed data typically violates algorithm assumptions (scaling, encoding, handling skew) and degrades model quality.

Answer

Remove all records with missing values

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Use raw data directly without any transformation

Question 7

Which THREE factors can affect the accuracy of an Einstein GPT response?

Accepted Answer

Model temperature setting. The model temperature setting directly controls the randomness of the output. A higher temperature (e.g., 0.9) produces more creative but potentially less accurate responses, while a lower temperature (e.g., 0.1) makes the model more deterministic and factual. This parameter is a core hyperparameter in large language models like those powering Einstein GPT, and it significantly influences response accuracy.

Answer

Brightness of the user interface

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Length of the response generated

Question 8

A multi-national company wants to use Einstein Activity Capture to automatically log emails from Outlook to Salesforce. They have a requirement that emails to certain external domains (e.g., competitors) must never be logged. How should they configure this?

Accepted Answer

Configure excluded addresses in Einstein Activity Capture settings. Einstein Activity Capture provides a native configuration to exclude specific email addresses or domains from being logged. By adding competitor domains to the 'Excluded Email Addresses' list in the Activity Capture settings, the system prevents those emails from ever being captured, ensuring compliance without post-processing workarounds.

Answer

Use a Process Builder to delete the logged emails

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Use Einstein Email Insights to filter out those domains after logging

Answer

Create a validation rule on the Email Message object

Question 9

A company’s AI model recommends products to customers. The team wants to measure how often the recommended products are actually purchased. Which metric is most appropriate?

Accepted Answer

Precision. Precision measures the proportion of recommended products that were actually purchased — i.e., of all the items the model flagged as 'recommend,' how many were true positives. That directly matches the business question of how often recommendations convert into purchases. Recall would instead measure how many of all actually purchased products were captured by the recommendations, which is a different question.

Answer

F1 score

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Recall

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Accuracy

Question 10

Which Einstein feature creates automated statistical analyses and stories, including waterfall charts and improvement suggestions?

Accepted Answer

Einstein Discovery. Einstein Discovery is the correct answer because it is the Einstein feature specifically designed to automatically generate statistical analyses, narratives, and visualizations such as waterfall charts, along with actionable improvement suggestions. Unlike other Einstein tools that focus on predictions or scoring, Einstein Discovery uses machine learning to uncover patterns in data and produce plain-language explanations of the insights.

Answer

Einstein Prediction Builder

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Einstein Forecasting

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Einstein Lead Scoring