AI Associate Practice Test 42 — 15 Questions

Question 1

A sales rep wants to see which of their opportunities are most likely to close this quarter without reviewing each one manually. Which feature provides a win probability score for opportunities?

Accepted Answer

Einstein Opportunity Scoring. Einstein Opportunity Scoring provides a win probability score for opportunities, helping sales reps prioritize deals most likely to close. It uses AI to analyze historical data and predict which opportunities are most likely to convert, directly addressing the need to see which opportunities are most likely to close this quarter without manual review.

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Einstein Lead Scoring

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Einstein Discovery

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Einstein Forecasting

Question 2

A marketing team uses Einstein Send Time Optimization to determine the best time to send emails. To ensure the company follows Salesforce's Trusted AI principle of Honesty, which THREE practices should be adopted?

Accepted Answer

Tell recipients that the email timing was chosen by an AI algorithm. Option B is correct because Salesforce's Honesty principle requires transparency with stakeholders, so recipients should be told that Einstein Send Time Optimization (an AI algorithm) selected the send time rather than a human. Option C is correct because Honesty also demands explainability: providing a brief explanation of the factors that influenced the recommendation (e.g., past engagement patterns) helps users understand how the AI reached its decision. Option D is correct because being honest about a model's limitations — such as not fully accounting for time zones or sparse data — sets accurate expectations and avoids overstating the model's capabilities. Option A is not correct because fully automating sends without human oversight is not an Honesty practice and can conflict with responsible AI use. Option E is not correct because claiming 100% accuracy is a false, exaggerated claim that directly violates the Honesty principle.

Answer

Automate all email sends without human intervention to maintain consistency

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Claim the model is 100% accurate in predicting optimal send times

Question 3

A company is implementing Einstein Prediction Builder to forecast sales opportunities. They want to ensure transparency and trust with their sales team. Which TWO practices should they adopt?

Accepted Answer

Indicate clearly when a prediction is AI-generated. Option D is correct because Einstein Prediction Builder's transparency best practice is to label AI-generated scores and predictions so users know the value came from a model rather than a person, which builds trust and sets proper expectations for the sales team. Option E is correct because Prediction Builder provides prediction explanations that surface the top influencing factors behind each score, and sharing these factors lets sales reps understand why an opportunity is scored high or low, directly supporting transparency and adoption. Option A is not appropriate because training on only one month of historical data typically yields too few examples and misses seasonality, producing an unreliable model rather than improving trust. Option B is wrong because letting the model silently update opportunity records without human review removes accountability and can erode trust if predictions are inaccurate. Option C is wrong because concealing that predictions are AI-generated is the opposite of transparency and would undermine the trust the company is trying to build.

Answer

Use only historical data from the past month to train the model

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Allow the model to automatically update opportunity records without human review

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Hide the fact that predictions are AI-generated to avoid confusion

Question 4

Which THREE factors should be considered when evaluating the fairness of an AI model?

Accepted Answer

Disparate impact ratio across groups.. The fairness of an AI model is assessed with group-fairness metrics that compare outcomes across protected groups, so option A (disparate impact ratio across groups) is correct because it measures the ratio of favorable outcome rates between groups (typically using the 80% rule) to detect adverse impact. Option D (equal opportunity difference) is correct because it compares true positive rates across groups, ensuring qualified individuals receive favorable predictions regardless of group membership. Option E (demographic parity in predictions) is correct because it requires equal positive prediction rates across groups, a standard statistical parity fairness criterion. Option B (overall accuracy on the test set) is not a fairness metric; it aggregates performance across all groups and can mask disparate harm. Option C (model training time) is an operational efficiency measure and has no bearing on fairness.

Answer

Overall accuracy on the test set.

Answer

Model training time.

Question 5

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 determines model accuracy in Einstein Prediction Service, so missing values must be imputed or removed and outliers handled before training to avoid skewed predictions. Option C is correct because feature selection is a core best practice: choosing fields that genuinely correlate with the outcome improves model performance and reduces noise, overfitting, and training time. Options A, D, and E are not best practices: correlated features should be identified and managed rather than blindly ignored, including every available field adds noise and can degrade the model, and default field mapping should always be reviewed to ensure the correct fields are mapped to the prediction target and predictors.

Answer

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 6

Which TWO practices help ensure accountability in AI systems?

Accepted Answer

Assign a human owner for each AI system.. Option B is correct because assigning a named human owner for each AI system establishes a clear point of responsibility, so there is always an identifiable person accountable for the system's behavior, oversight, and remediation rather than diffuse or absent ownership. Option D is correct because implementing logging and auditing of model decisions creates an immutable, reviewable record of inputs, outputs, and model versions, which is essential for tracing what happened, detecting misuse or drift, and holding people accountable after the fact. By contrast, Option A is wrong because blaming the AI system shifts responsibility away from the humans who design, deploy, and operate it, and an AI cannot bear accountability. Option C is wrong because fully automating decisions removes human oversight and actually erodes accountability rather than strengthening it. Option E is wrong because open-sourcing code may improve transparency but does not by itself assign responsibility, and shared or diffuse responsibility can weaken accountability.

Answer

Blame the AI system for mistakes to protect employees.

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Automate all decisions to eliminate human error.

Answer

Open-source the model code to share responsibility.

Question 7

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 relies on captured article view event data to train and drive its recommendation model, so if view events are not being logged, no recommendations can be generated or surfaced. Option E is correct because a recommendation model that is not published and active will not serve recommendations to the site, making this a fundamental first check. Option A is not required, since the 100-article threshold is not a documented prerequisite for Einstein Article Recommendations to function. Option B is irrelevant, as author profile permissions do not control whether recommendations appear to site visitors. Option D is incorrect because changing the recommendation frequency from daily to hourly does not fix a missing-data or inactive-model problem and is not a valid troubleshooting step.

Answer

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 8

Which TWO actions best promote transparency in an AI system?

Accepted Answer

Publish an audit trail of model inputs and decisions.. Option B is correct because publishing an audit trail of model inputs and decisions creates a verifiable record that lets stakeholders trace how and why outputs were produced, which is a core mechanism of transparency and accountability. Option D is correct because providing clear explanations for individual predictions (e.g., via interpretability techniques such as SHAP, LIME, or counterfactual explanations) makes the model's reasoning understandable to users and auditors on a per-decision basis. Option A does not belong because restricting access to the model's logic reduces transparency by hiding how the system works, even if it protects intellectual property. Option C does not belong because using a complex deep learning model for higher accuracy addresses performance, not transparency, and can actually decrease interpretability. Option E does not belong because removing feature importance strips away information about which inputs drive predictions, directly undermining transparency rather than promoting it.

Answer

Limit access to the model's logic to protect intellectual property.

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Use a complex deep learning model for higher accuracy.

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Remove feature importance to simplify the model.

Question 9

Which TWO data types can be used as input for Einstein Vision?

Accepted Answer

URLs pointing to images.. Einstein Vision accepts image data for training and prediction, and one supported input form is a URL pointing to an image (option B), which lets the service fetch the image directly from a publicly accessible web address. It also accepts raw image files in formats such as JPEG and PNG (option C), which are the standard image encodings supported by the Einstein Vision APIs for classification, object detection, and OCR. Video files (option A) are not a supported input type because Einstein Vision processes still images, not MP4 video streams. Text documents such as PDFs (option D) are not image inputs for Einstein Vision, and audio files like WAV (option E) are unrelated to image recognition and would instead belong to speech or audio services.

Answer

Video files (MP4).

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Text documents (PDF).

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Audio files (WAV).

Question 10

A company wants to use AI to analyze call recordings and automatically capture key topics, talk time, and next steps. Which Einstein feature should they implement?

Accepted Answer

Einstein Conversation Insights. Einstein Conversation Insights is the correct feature because it is specifically designed to analyze call recordings and automatically extract key topics, talk time, and next steps using natural language processing (NLP) and speech-to-text technology. It provides post-call summaries, identifies action items, and surfaces conversation trends without requiring manual note-taking or configuration.

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Einstein Activity Capture

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Einstein Bots

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Einstein Email Insights

Question 11

An AI system used for medical diagnosis occasionally produces incorrect results. A doctor notices the errors but continues using the system without reporting them. Which ethical principle is primarily at risk?

Accepted Answer

Accountability. Accountability is at risk because the doctor observes incorrect AI outputs and fails to report them, breaking the chain of responsibility for the system's outcomes. In AI ethics, accountability requires that humans remain responsible for monitoring, reporting, and correcting AI errors. Ignoring known errors without escalation undermines the governance structure that ensures the system is safe and reliable.

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Fairness

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Transparency

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Privacy

Question 12

Which TWO of the following are common dimensions of data quality that must be addressed for AI training?

Accepted Answer

Accuracy of values. Accuracy of values (B) is a core data-quality dimension because AI models learn from the data they are trained on, so incorrect, outdated, or mislabeled values directly degrade model predictions and reliability. Completeness of records (E) is equally fundamental, since missing fields, null values, or absent records create gaps that bias training and prevent the model from learning the full feature space. Together, accuracy and completeness are standard dimensions in data-quality frameworks (alongside timeliness, uniqueness, validity, and consistency) that must be assessed before AI training. Storage efficiency (A) is an infrastructure/cost concern, not a data-quality dimension. Encryption strength (C) is a security control, not a measure of data quality. Consistency with external benchmarks (D) is not a standard data-quality dimension; consistency in data quality refers to agreement across internal sources or formats, not conformity to outside benchmarks.

Answer

Storage efficiency

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Encryption strength

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Consistency with external benchmarks

Question 13

Which type of AI is designed to perform only a specific task, such as playing chess or recommending products?

Accepted Answer

Narrow AI. Narrow AI (weak AI) is specialized for one task and cannot generalize beyond its training.

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Super AI

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General AI

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Strong AI

Question 14

A company uses Einstein Forecasting. Their sales reps' committed forecasts are consistently lower than the AI-predicted forecast. The manager wants to understand why. What is the BEST first step to investigate the discrepancy?

Accepted Answer

Review the AI forecast explanation to see which factors (e.g., deal stage, historical win rates) are driving the higher prediction.. Einstein Forecasting provides an AI-predicted forecast based on historical data and deals. Comparing the rep commit to the AI forecast, and analyzing the key drivers behind the AI prediction (such as deal stage, age, amount) helps identify why the AI expects more.

Answer

Disable Einstein Forecasting and revert to manager rollups only.

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Adjust the historical date range in forecast settings to exclude past low-performing quarters.

Answer

Run a report on closed won opportunities to see if the AI overestimates.

Question 15

What is the primary purpose of Einstein Studio in the Salesforce AI ecosystem?

Accepted Answer

Train, test, and deploy custom AI models using AutoML. Einstein Studio is specifically designed to allow users to train, test, and deploy custom AI models using AutoML, without requiring deep data science expertise. It provides a no-code interface for building models on Salesforce Data Cloud data, enabling predictive scoring and recommendations directly within the Salesforce ecosystem.

Answer

Create and manage prompt templates

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Unify customer data from multiple sources

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Deploy pre-built Einstein GPT models