AI Associate Practice Test 47 — 15 Questions

Question 1

A company uses Einstein Prediction Builder to forecast customer churn. The data science team discovers that the model is heavily influenced by a field containing the customer's income, which the company legally cannot use for automated decisions in certain jurisdictions. Which TWO steps should the team take to address this ethical and compliance issue?

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

Accepted Answer

Re-evaluate feature importance to ensure no other fields act as proxies for income. Option D is correct because removing the income field from the training data directly eliminates the legally prohibited attribute from the model's inputs, preventing Einstein Prediction Builder from using it to generate churn predictions. Option A is correct because even after removing income, other fields (such as zip code, job title, or credit score) can act as proxies that indirectly encode income, so re-evaluating feature importance is necessary to detect and mitigate these correlated features. Option B is not among the marked correct answers; while bias auditing is a valuable practice, the question specifically asks for steps to address the compliance issue of using income, and the two marked answers directly target removing the field and checking for proxies. Option C is wrong because internal use does not exempt a model from legal or ethical obligations in jurisdictions that prohibit automated decisions based on income. Option E is wrong because retaining the income field and merely adding a note does not prevent the model from learning and using that field, so it fails to resolve the compliance risk.

Answer

Audit the model for bias against different income groups

Answer

Ignore the issue because the model is used internally

Answer

Retain the income field but add a note that it may not be used in certain regions

Question 2

A financial services firm uses Einstein Discovery to predict loan default risk. To comply with data minimisation principles and avoid using sensitive PII unnecessarily, which TWO actions should the data science team take?

Accepted Answer

Remove demographic fields such as race and gender from the training dataset. Option A is correct because removing demographic fields such as race and gender from the training dataset directly enforces data minimisation and prevents the model from learning from sensitive PII or protected attributes, reducing legal and ethical risk under fair-lending and privacy regulations. Option B is correct because feature selection retains only the most predictive fields, which both improves model relevance and minimises the amount of personal data processed, aligning with the principle of collecting and using only what is necessary. Option C is wrong because including all available fields maximises data collection and contradicts data minimisation, and it can introduce sensitive or irrelevant attributes that increase privacy and bias risk. Option D is wrong because deriving synthetic attributes that correlate with protected attributes is a form of proxy discrimination and defeats the purpose of removing sensitive PII. Option E is wrong because Einstein Trust Layer's toxicity detection targets harmful or offensive text content, not the minimisation of sensitive PII in structured training data, so it does not address this scenario's compliance requirement.

Answer

Include all available fields to maximize model accuracy

Answer

Use feature engineering to derive synthetic attributes that correlate with protected attributes

Answer

Enable Einstein Trust Layer's toxicity detection on the model inputs

Question 3

A company wants to build an autonomous AI agent that can take actions in Salesforce, such as updating records and sending emails, based on user instructions. Which tool should they use?

Accepted Answer

Agentforce Agent Builder. Agentforce Agent Builder (formerly Einstein Copilot Studio) is Salesforce's tool for building autonomous AI agents that can reason, plan, and take actions across Salesforce — such as updating records and sending emails — based on natural-language user instructions. It supports topics, instructions, and actions that connect to Salesforce data and flows, making it the right choice for an autonomous agent.

Answer

Einstein Copilot

Answer

Einstein Bots

Answer

Flow Builder

Question 4

Which TWO considerations are critical when planning data labeling for a computer vision project in a regulated industry?

Accepted Answer

Mitigating labeler bias to ensure fairness. Option B is correct because in a regulated industry, labeler bias can produce systematically skewed annotations that lead to unfair or discriminatory model behavior, so bias mitigation and fairness auditing are critical planning considerations. Option C is correct because regulated industries must ensure data labeling workflows comply with data privacy regulations such as GDPR, including lawful processing, data minimization, consent, and cross-border transfer restrictions. Option A is not the key consideration here, since where label files are stored is an implementation detail that matters mainly as part of broader privacy and security compliance. Option D is not critical in this context, as timeline and budget are general project-management constraints rather than regulated-industry-specific labeling concerns. Option E is not critical here either, because choosing between bounding boxes and segmentation masks is an annotation-format decision driven by model requirements, not by regulatory compliance or fairness.

Answer

Data storage location for label files

Answer

Labeling timeline and budget constraints

Answer

Choosing between bounding boxes and segmentation masks

Question 5

A company wants to use Agentforce to create an autonomous AI agent that can handle customer service inquiries. Which TWO components must be configured in Agent Builder?

Accepted Answer

Topics. A is correct because Topics in Agent Builder define the high-level categories of customer inquiries (e.g., 'Billing', 'Returns') that the autonomous agent can handle. They are mandatory for structuring the agent's conversation flow and routing logic. Without Topics, the agent would have no defined scope of work.

Answer

Prompt Templates

Answer

Entities

Answer

Intents

Question 6

An admin wants to deploy an autonomous AI agent that can handle order cancellations end-to-end without human intervention. The agent needs to execute specific actions like querying order status and updating records. Which tool should they use?

Accepted Answer

Agentforce with Agent Builder. Agentforce with Agent Builder is the correct tool because it is specifically designed to build autonomous AI agents that can execute end-to-end workflows, including querying order status and updating records, without human intervention. It uses a combination of large language models, deterministic actions, and guardrails to handle complex, multi-step tasks like order cancellations autonomously.

Answer

Einstein Bots

Answer

Einstein Next Best Action

Answer

Einstein GPT with Prompt Builder

Question 7

An organization wants to build an autonomous AI agent that can handle customer inquiries order status and return requests without human intervention. Which Salesforce tool allows them to build such an agent with topics and actions?

Accepted Answer

Agentforce. Agentforce is the correct answer because it is the Salesforce tool specifically designed to build autonomous AI agents that can handle customer inquiries, order status, and return requests without human intervention. It allows you to define topics (e.g., 'Order Status') and actions (e.g., 'Lookup Order') that the agent can execute independently, using natural language processing and integration with Salesforce data.

Answer

Einstein Copilot

Answer

Einstein Bots

Answer

Einstein Next Best Action

Question 8

Which THREE of the following are best practices for training an Einstein Bot?

Accepted Answer

Test the bot with sample conversations before deployment. Testing an Einstein Bot with sample conversations before deployment allows you to validate the bot's intent recognition, dialog flow, and response accuracy in a controlled environment. This practice helps identify and fix issues with phrase matching, slot filling, and escalation paths, ensuring the bot performs reliably in production.

Answer

Train with only one intent to avoid confusion

Answer

Use exact match phrases only

Question 9

A marketing team wants to identify which leads are most likely to convert, based on historical lead data. They need a score from 1-99 visible on lead records. Which feature should they implement?

Accepted Answer

Einstein Lead Scoring. Einstein Lead Scoring is the correct feature because it is specifically designed to assign a score from 1 to 99 to leads based on historical conversion data and predictive models. This score is automatically calculated and displayed on lead records, enabling the marketing team to prioritize leads most likely to convert.

Answer

Einstein Prediction Builder

Answer

Einstein Opportunity Scoring

Answer

Einstein Discovery

Question 10

A marketing manager wants to use Einstein Send Time Optimization. To generate personalized send time recommendations, which data does the model primarily rely on?

Accepted Answer

The individual contact's past email open and click behavior.. Einstein Send Time Optimization (STO) uses a machine learning model that analyzes each individual contact's historical email engagement patterns—specifically their past open and click behavior—to predict the optimal send time unique to that contact. This personalized approach ensures that each recipient receives the email when they are most likely to engage, rather than relying on aggregate or rule-based heuristics.

Answer

The aggregated engagement data of all contacts in the same time zone.

Answer

The industry benchmarks for optimal send times.

Answer

The sender's historical campaign performance by hour.

Question 11

A company uses Einstein Discovery to predict customer churn. They want to ensure the predictions are explainable to non-technical stakeholders. What is the best way to provide explanation?

Accepted Answer

Use the score factors feature to show top predictors. Einstein Discovery's score factors feature explicitly lists the top predictors and their contributions to each prediction, making the model's reasoning transparent and accessible to non-technical stakeholders. This aligns with the need for explainable AI (XAI) without requiring deep data science knowledge.

Answer

Share the full training dataset for transparency

Answer

Allow stakeholders to retrain the model themselves

Answer

Provide the raw model weights and coefficients

Question 12

A Salesforce admin wants to create a prompt template that generates a custom field value for a record based on other field values. Which Prompt Builder template type should they use?

Accepted Answer

Field Generation. Field Generation is the correct Prompt Builder template type because it is specifically designed to automatically populate a custom field on a record by generating a value based on other field values within the same record. This template uses a Large Language Model (LLM) to analyze the provided field data and produce a deterministic output that is written directly to the field, fulfilling the admin's requirement.

Answer

Flex Prompt

Answer

Case Summary

Answer

Sales Email

Question 13

A company wants to use Einstein Relationship Intelligence to analyze email and calendar data for opportunity insights. Which two conditions must be met? (Select two answers.)

Accepted Answer

Users must have the Einstein Relationship Intelligence permission set.. The Einstein Relationship Intelligence permission set grants users access to the feature's insights and data processing capabilities. Without this permission set, users cannot view or interact with Einstein Relationship Intelligence data, even if the feature is enabled at the org level.

Answer

Exchange or Gmail must be used for email.

Answer

The feature is only available in Unlimited Edition.

Answer

All users must opt in to email logging.

Question 14

An admin is troubleshooting Einstein Sentiment. The model returns high confidence but wrong sentiment (e.g., positive reviews labeled negative). What is the most likely issue?

Accepted Answer

The training data has incorrect labels for sentiment.. If the training data contains incorrect labels for sentiment, the model learns from erroneous ground truth, leading to high confidence in wrong predictions. In Einstein Sentiment, the model's accuracy depends directly on the quality and correctness of the labeled training data; mislabeled examples cause the classifier to associate features with the wrong sentiment class, resulting in confident but incorrect outputs.

Answer

The model was not retrained after the last data load.

Answer

The training data contains predominantly neutral examples.

Answer

The field mapping for the sentiment field is incorrect.

Question 15

A company uses AI to automatically categorize customer support tickets into 'Billing', 'Technical', or 'General'. The model is trained on thousands of past tickets labeled by agents. What type of AI task is this?

Accepted Answer

Text classification. This is a text classification problem using supervised learning, where each ticket is assigned to a predefined category based on labeled training data.

Answer

Intent detection

Answer

Sentiment analysis

Answer

Named entity recognition