AI Associate Multiple Select — 20 Questions

Question 1

A sales operations manager wants to use Einstein GPT for Sales to improve rep productivity. Which THREE tasks can Sales GPT perform?

Accepted Answer

Summarize sales calls. Einstein GPT for Sales includes a call summarization feature that uses generative AI to automatically create concise summaries of sales calls from transcripts. This directly improves rep productivity by saving time on manual note-taking and capturing key action items.

Answer

Create sales dashboards

Answer

Predict lead scores

Question 2

A sales team is implementing Einstein Lead Scoring. Which two actions should they take to ensure the model is effective? (Choose 2)

Accepted Answer

Ensure the training data includes both converted and unconverted leads.. Option D is correct because Einstein Lead Scoring is a supervised machine-learning model that learns patterns distinguishing successful outcomes from failures, so the training dataset must contain a balanced mix of converted (won) and unconverted (lost) leads to build a predictive model. Option E is correct because the model's accuracy depends on continuous feedback: as sales reps update lead statuses and conversion outcomes in Salesforce, the model retrains on that fresh labeled data, so regularly reviewing and providing accurate conversion feedback keeps scores relevant and improves predictive quality. Option A is incorrect because restricting scoring to demographic data alone ignores behavioral and engagement signals (such as email opens, website visits, and activity history) that are among the strongest predictors in Einstein Lead Scoring. Option B is incorrect because a single fixed score threshold for all users ignores differing sales processes and territories; thresholds should be configurable per user or business unit. Option C is incorrect because disabling the model for low-volume leads would prevent scoring exactly where prioritization help is needed, and Einstein Lead Scoring requires sufficient historical data rather than being turned off for low-volume segments.

Answer

Include only demographic data for scoring.

Answer

Set a fixed score threshold for all users.

Answer

Disable the model for low-volume leads.

Question 3

Which THREE capabilities are provided by Einstein GPT in Sales and Service?

Accepted Answer

Summarizing case conversations from service interactions.. Einstein GPT leverages generative AI to automatically summarize case conversations from service interactions, enabling agents to quickly grasp key details without reading entire transcripts. This capability is built on Salesforce's proprietary AI models that process natural language from service records to produce concise, actionable summaries.

Answer

Generating report charts and dashboards.

Answer

Creating workflow rules based on user behavior.

Question 4

An admin wants to create a custom AI model to predict lead conversion using Einstein Prediction Builder. Which TWO items must they select when creating the model? (Choose two)

Accepted Answer

Data set (records to train on). The data set defines the records (e.g., leads, opportunities) that the model will use for training. Without specifying which records to train on, the model has no source of historical data to learn patterns from. Einstein Prediction Builder requires you to select a data set (such as a report or object) to provide the training examples.

Answer

Model algorithm type

Answer

Prediction explanation settings

Answer

Features (input fields)

Question 5

A retail company wants to use AI to predict next month's sales for each product category. They have five years of monthly sales data. Which THREE factors are most critical for the accuracy of the predictive model? (Select THREE.)

Accepted Answer

Including seasonality and trend components from historical data. Option B is correct because retail sales data almost always exhibits recurring seasonal peaks (e.g., holidays, back-to-school) and long-term trends, so explicitly modeling seasonality and trend components (as in SARIMA, Prophet, or seasonal decomposition) materially improves forecast accuracy. Option D is correct because missing values, outliers, and inconsistent records in five years of monthly data bias parameter estimation and can cause the model to learn spurious patterns, so cleaning and imputing the training data is a prerequisite for reliable predictions. Option E is correct because sales are driven by exogenous events such as holiday calendars, promotions, and markdowns; incorporating these as external regressors (e.g., in Prophet or SARIMAX) captures demand shifts that pure historical autocorrelation cannot explain. Option A does not belong because algorithm popularity is irrelevant to accuracy; the right choice depends on data characteristics, and a trendy algorithm can underperform a simple, well-tuned one. Option C does not belong because deep learning is not inherently more accurate for a small tabular dataset of five years of monthly sales, and simpler models often generalize better with limited data.

Answer

Choosing the most popular AI algorithm on the market

Answer

Using a deep learning model instead of a simpler model

Question 6

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 7

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

Answer

Use raw data directly without any transformation

Question 8

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

Answer

Length of the response generated

Question 9

A company is deploying Einstein Vision for product quality inspection. To ensure ethical use, which TWO practices should they adopt? (Choose two.)

Accepted Answer

Test the model for bias across different product types and lighting conditions. Option A is correct because testing the Einstein Vision model for bias across different product types and lighting conditions ensures the model performs equitably and accurately across the varied real-world scenarios it will encounter, which is a core ethical practice in AI deployment. Option D is correct because providing clear documentation on the model's limitations and expected accuracy promotes transparency and informed use, allowing stakeholders to understand when and how the model should be trusted. Option B is incorrect because keeping the decision-making process proprietary undermines transparency and ethical accountability, which are essential for responsible AI. Option C is incorrect because deploying the model without human oversight risks unchecked errors and ethical harm, contradicting the need for human-in-the-loop practices. Option E is incorrect because using training images from a single supplier introduces bias and reduces the model's generalizability, which is unethical and ineffective for quality inspection.

Answer

Keep the model's decision-making process proprietary to protect intellectual property

Answer

Deploy the model without human oversight to maximize efficiency

Answer

Only use training images from a single supplier to maintain consistency

Question 10

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.. 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.

Answer

All input features must be numerical.

Answer

The model should be retrained only once after initial deployment.

Question 11

An admin is training an Einstein Prediction Builder model for binary classification (lead conversion). The model performance is poor. Which THREE actions should the admin take to improve it?

Accepted Answer

Increase the number of records in the training dataset. Option A is correct because Einstein Prediction Builder learns from historical data, so increasing the number of records in the training dataset gives the model more examples to detect meaningful patterns and improves accuracy, provided the data is relevant and clean. Option C is correct because removing features with little correlation to conversion reduces noise and irrelevant inputs, which helps the model focus on the variables that actually influence the predicted outcome. Option E is correct because the prediction field (e.g., converted) must be well-represented in the data; if one class is severely imbalanced or the field has many blanks, the model cannot learn the binary classification reliably. Option B is not correct because using fewer records generally reduces the model's ability to learn and can worsen performance rather than prevent overfitting. Option D is not correct because adding unrelated features introduces noise and can degrade model quality instead of improving it.

Answer

Use fewer records to avoid overfitting

Answer

Add more features, even if they are not related

Question 12

Which TWO actions align with ethical AI practices in Salesforce?

Accepted Answer

Monitoring models for bias. Option C is correct because ethical AI in Salesforce requires ongoing bias detection and mitigation—Salesforce's Einstein Trust Layer and Model Cards emphasize regularly monitoring models for disparate impact across protected groups so that biased outcomes are caught and corrected. Option E is correct because providing explanations for AI predictions supports transparency and accountability, aligning with Salesforce's responsible AI principles and emerging regulations (e.g., GDPR Article 22) that give users the right to understand automated decisions. Option A is not inherently ethical—restricting training to internal data can still violate privacy or bias concerns, and ethical AI does not mandate internal-only data. Option B is wrong because fully automating decisions without human review removes the human oversight that ethical AI frameworks require for high-impact choices. Option D is wrong because using customer data without explicit consent violates privacy and consent principles central to ethical AI and Salesforce's data-use policies.

Answer

Using only internal data for training

Answer

Automating all decisions without human review

Answer

Using customer data without explicit consent

Question 13

An admin is building a custom AI prediction with Einstein Prediction Builder for a binary classification problem. Which THREE steps are required in the configuration? (Choose 3)

Accepted Answer

Select a prediction field (the field to predict). In Einstein Prediction Builder, the core configuration flow for a binary classification prediction requires you to first choose the prediction field (the field whose value the model will predict), which is exactly what option B states, since the model needs a defined target variable. Option C is also required because you must select the data set — the object and the set of records (with filters) used to train the model — so the algorithm has historical examples to learn from. Option D is required as well: you must select the features (input fields from the object) that the model will use as predictors, since without input variables no model can be trained. Option A is not required because Prediction Builder builds models automatically from selected fields and does not need a custom Apex class for data transformation. Option E is not required because retraining is handled by Einstein's scheduled model refresh, not by an admin-configured Apex trigger running daily.

Answer

Define a custom Apex class for data transformation

Answer

Configure a trigger to retrain the model daily

Question 14

A sales team wants to use AI to get product recommendations for customers. Which TWO types of machine learning could be used?

Accepted Answer

Unsupervised learning. Supervised learning (E) is correct because product recommendation can be framed as a classification or regression task trained on labeled historical data, such as past customer purchases and ratings, to predict which products a customer is likely to buy. Unsupervised learning (D) is also correct because techniques like clustering and association rules can group customers with similar behavior or find products frequently bought together, enabling recommendations without explicit labels. Generative AI (A) is not the intended answer here since it creates new content rather than being the core ML paradigm for recommendation, and reinforcement learning (B) is typically used for sequential decision-making and reward optimization, not standard product recommendation. Computer vision (C) is unrelated because it processes images and video, not the tabular customer-product interaction data used for recommendations.

Answer

Generative AI

Answer

Reinforcement learning

Answer

Computer vision

Question 15

A sales operations manager wants to use Einstein Forecasting to improve forecast accuracy. Which TWO capabilities does Einstein Forecasting provide beyond traditional manager rollups? (Select two.)

Accepted Answer

Comparison of AI forecast to the rep's commit amount. Option E is correct because Einstein Forecasting's core capability is generating AI-based forecast predictions that analyze historical opportunity data, trends, and patterns to project future revenue, going beyond what a manager's manual rollup can produce. Option D is correct because Einstein Forecasting lets users compare the AI-generated forecast against the rep's own commit or forecast amount, highlighting gaps and improving forecast accuracy through that side-by-side comparison. Option A is incorrect because automated opportunity scoring is a feature of Einstein Opportunity Scoring, not Einstein Forecasting. Option B is incorrect because call script generation is not a forecasting capability; it relates to sales enablement tools. Option C is incorrect because Einstein Forecasting does not automatically adjust quota targets; quota setting remains a separate planning function.

Answer

Automated opportunity scoring for each deal

Answer

Generation of call scripts for sales reps

Answer

Automatic adjustment of quota targets based on AI predictions

Question 16

A Salesforce administrator deploys an Einstein Bot. Which TWO ethical considerations should be addressed? (Choose two.)

Accepted Answer

The bot should disclose it is an AI. Option A is correct because ethical AI deployment requires transparency: an Einstein Bot should clearly disclose that it is an AI/automated agent so users know they are not interacting with a human, which supports informed consent and trust. Option E is correct because ethical bot design includes a clear escalation path to a human agent when the bot cannot resolve an issue or when the user requests it, preventing frustration and ensuring accountability for sensitive or complex cases. Option B is incorrect because deliberately mimicking a human without disclosure is deceptive and conflicts with transparency principles. Option C is incorrect because fully autonomous decision-making without human oversight is risky for ethical, compliance, and customer-care reasons; bots should operate within defined guardrails. Option D is incorrect because it is overly absolute: bots may legitimately collect personal data when necessary, provided it is done lawfully, with notice/consent, and in line with privacy requirements.

Answer

The bot should mimic a human

Answer

The bot should make decisions autonomously

Answer

The bot should not collect personal data

Question 17

A data analyst uses Einstein Discovery to analyze a dataset and receives a story that includes a waterfall chart and improvement suggestions. The analyst wants to share the insights with business users who don't have access to Einstein Discovery. Which three methods can they use to share the results?

Accepted Answer

Export the story as a PDF. Einstein Discovery allows users to export a story as a PDF, which can then be shared with business users who lack direct access to Einstein Discovery. This method provides a static, portable snapshot of the insights, including the waterfall chart and improvement suggestions, without requiring the recipients to have any Salesforce or Einstein licenses.

Answer

Send an email with the raw data

Answer

Create a custom mobile app

Question 18

Which TWO are best practices when implementing Einstein Bots? (Choose two.)

Accepted Answer

Start with high-volume, low-complexity conversations. Option A is correct because starting with high-volume, low-complexity conversations lets you deliver quick wins, keep the bot's intent model manageable, and build user trust before expanding scope. Option D is correct because Einstein Bots provides conversation logs and analytics that should be reviewed regularly to refine utterances, add training phrases, and improve intent matching accuracy over time. Option B is incorrect because complex intents increase misclassification risk and make initial training and testing harder, so they should be deferred until the bot matures. Option C is incorrect because a bot should not be expected to handle all conversations; escalation to a human agent is a required fallback for unsupported or sensitive requests. Option E is incorrect because deploying to all channels immediately without testing skips validation of intents, channel-specific behavior, and fallback handling, risking poor customer experiences.

Answer

Use complex intents for initial setup

Answer

Configure the bot to handle all customer conversations

Answer

Deploy the bot to all channels immediately without testing

Question 19

A data scientist is building a custom AI model using Salesforce Data Cloud. They want to follow best practices for data minimisation and consent management. Which THREE steps should they take?

Accepted Answer

Obtain consent from customers before using their data in the model. Option A is correct because consent management is a core Data Cloud best practice: before using customer data to train or score an AI model, the data scientist must ensure the individual's consent (e.g., via Consent Management objects and Data Cloud consent policies) permits that specific use, otherwise the processing violates data minimisation and privacy obligations. Option C is correct because documenting the data sources, Data Streams, Data Model Objects (DMOs), and calculated insights/features used in the model provides the auditability and lineage required for governance, regulatory review, and reproducibility of the model. Option D is correct because data minimisation means selecting only the fields that are relevant to the prediction task rather than ingesting or exposing unnecessary attributes, reducing privacy risk and improving model quality. Option B is not correct because including all available fields contradicts data minimisation and can introduce noise, overfitting, and unnecessary personal data processing. Option E is not correct because using synthetic data is not a required best-practice step here and does not by itself satisfy consent or minimisation requirements for real customer data.

Answer

Include all available fields in the model to maximize accuracy

Answer

Use synthetic data instead of real customer data

Question 20

Which THREE factors should an AI Associate consider when evaluating a model for potential bias?

Accepted Answer

Whether features are correlated with protected attributes.. Option B is correct because bias often enters a model when input features are correlated with protected attributes such as race, gender, or age, which can cause the model to indirectly discriminate even if those attributes are excluded. Option C is correct because evaluating performance metrics like accuracy, precision, recall, or false positive rate across demographic groups reveals disparate impact and unequal error rates that signal bias. Option E is correct because if the training data underrepresents or misrepresents certain groups, the model will learn skewed patterns and generalize poorly to those populations. Option A is not a direct bias factor, since model complexity affects capacity and overfitting but not whether outcomes are systematically unfair to a group. Option D is irrelevant to bias evaluation, as deployment recency says nothing about the fairness or representativeness of the model's data and predictions.

Answer

The complexity of the model architecture.

Answer

The date the model was last deployed.