AI Associate Practice Test 24 — 15 Questions

Question 1

Which THREE components are essential for an ethical AI governance framework within a large enterprise?

Accepted Answer

Establish a cross-functional AI ethics board.. Option A is correct because a cross-functional AI ethics board is essential for an ethical AI governance framework in a large enterprise, as it brings together legal, compliance, data science, business, and risk stakeholders to set policy, review high-risk use cases, and resolve ethical conflicts across the organization. Option B is correct because regular ethical impact assessments are a core governance control that systematically evaluates AI systems for bias, fairness, privacy, transparency, and societal harm before and after deployment, enabling risks to be identified and mitigated. Option C is correct because defining clear accountability for AI outcomes ensures that specific roles and owners are responsible for decisions, monitoring, and remediation, which is fundamental to enforceable governance and regulatory compliance. Option D is not correct because minimizing human oversight to cut costs directly undermines ethical governance, which requires appropriate human-in-the-loop review and control for high-risk AI decisions. Option E is not correct because optimizing for accuracy as the primary goal is a narrow technical objective that can conflict with ethical requirements such as fairness, explainability, and safety, so it is not an essential component of an ethical governance framework.

Answer

Minimize human oversight to reduce operational costs.

Answer

Optimize for accuracy as the primary goal.

Question 2

Which THREE are key considerations for data privacy when using AI models that process customer data? (Choose three.)

Accepted Answer

Limit data access to authorized personnel only. Option B is correct because limiting data access to authorized personnel only enforces least-privilege access controls, reducing the risk of unauthorized exposure of customer data processed by AI models. Option C is correct because obtaining user consent for data usage satisfies core privacy principles such as transparency and lawful processing, ensuring customers agree to how their data is used by the AI system. Option E is correct because anonymizing personally identifiable information (PII) removes or irreversibly alters identifiers, so even if data is exposed or reused, individuals cannot be re-identified. Option A is not appropriate because storing data indefinitely for future use violates data minimization and retention limits, increasing privacy and compliance risk. Option D, while a valuable security control, is not marked correct here and addresses protection of data rather than the privacy considerations of consent, access limitation, and anonymization emphasized in this scenario.

Answer

Store data indefinitely for future use

Answer

Encrypt data in transit and at rest

Question 3

A company uses Einstein Sentiment to analyze customer feedback. The tool incorrectly flags negative sentiment for customers with heavy accents. Which ethical issue is present?

Accepted Answer

Bias and discrimination. Bias and discrimination, because the model systematically produces skewed sentiment results for a specific group—customers with heavy accents—which is a classic algorithmic bias harming a protected or vulnerable population. This is not a data-protection breach, so Privacy violation (A) does not fit. It is also not primarily about unclear decision ownership or missing audit trails, so Accountability gap (C) is incorrect. And while opacity can contribute to bias, the core issue here is unfair, discriminatory outcomes rather than Lack of transparency (D).

Answer

Privacy violation

Answer

Accountability gap

Answer

Lack of transparency

Question 4

A company is using Einstein Article Recommendations in Service Cloud. They notice that articles about a specific product are never recommended, even when relevant. After reviewing, they find that the training data did not include any cases where that product was mentioned. Which Salesforce Trusted AI principle is most directly violated?

Accepted Answer

Accuracy. The Accuracy principle of Salesforce Trusted AI requires that AI systems be designed to produce accurate, reliable, and unbiased results. If the training data lacks cases mentioning a specific product, the model cannot learn to recommend articles about it, leading to inaccurate recommendations. This directly violates the Accuracy principle.

Answer

Empathy

Answer

Honesty

Answer

Safety

Question 5

A developer wants to use AI to classify images uploaded to Salesforce. Which platform/service should they use?

Accepted Answer

Einstein Vision and Language Platform. Einstein Vision and Language Platform is the correct service because it provides pre-built and custom AI models specifically designed for image classification, object detection, and optical character recognition (OCR) within Salesforce. It allows developers to upload images and train models using labeled data directly in the Salesforce ecosystem, making it the only option that directly addresses the requirement to classify images.

Answer

Einstein Bots

Answer

Einstein GPT

Answer

Einstein Discovery

Question 6

A sales operations admin wants to use Einstein Opportunity Scoring. Which two steps are required to activate Einstein Opportunity Scoring? (Select two answers.)

Accepted Answer

Enable Einstein in the Einstein Setup menu.. Option A is correct because Einstein features, including Einstein Opportunity Scoring, must first be turned on by enabling Einstein in the Einstein Setup menu (Setup → Einstein Setup), which activates the underlying Einstein services for the org. Option B is correct because Einstein Opportunity Scoring requires a sufficient volume of historical data to train its model — specifically, at least 100 won opportunities and 100 lost opportunities within the last 24 months — before scoring can be activated. Option C is not required because Einstein Opportunity Scoring uses a built-in, pre-trained model rather than a custom scoring model the admin must create. Option D is not required because there is no prerequisite to deactivate existing scoring rules to activate Einstein Opportunity Scoring. Option E is not required for activation because assigning the permission set controls user access to the feature, not the activation of the scoring model itself.

Answer

Create a custom scoring model specific to the business.

Answer

Deactivate any existing scoring rules.

Answer

Assign the Einstein Opportunity Scoring permission set to users.

Question 7

A large financial institution uses Einstein Discovery to automate loan pre-approval decisions. The model was trained on ten years of historical data. After deployment, the compliance team finds that the approval rate for minority groups is 15% lower than the majority group, even after controlling for credit score and income. The data is balanced across groups. The model uses features like zip code, employment history, and debt-to-income ratio. The institution has a strict policy of fairness and non-discrimination. The AI team proposes three options: (1) remove zip code and employment history from the model, (2) add a fairness constraint to the model training, (3) lower the decision threshold for minority groups to balance approval rates. The compliance officer must choose the most ethical and effective course of action that aligns with Salesforce AI ethical guidelines. Which option should they choose?

Accepted Answer

Add a fairness constraint to the model training. Adding a fairness constraint during model training directly addresses the disparate impact by optimizing the model to reduce bias across protected groups while still using relevant predictive features, aligning with Salesforce's AI ethics principles of equality and accountability. Lowering the decision threshold for minority groups (B) is a form of affirmative action that can be legally risky and may not address root causes of bias, and it can also reduce overall model accuracy. Removing zip code and employment history (C) may reduce some proxy discrimination but can degrade model performance and does not guarantee fairness, as other features may still encode bias. Continuing as is (D) ignores the compliance finding and violates the institution's non-discrimination policy, so it is not acceptable.

Answer

Lower the decision threshold for minority groups

Answer

Remove zip code and employment history from the model

Answer

Continue using the model as is, since data is balanced

Question 8

A company uses an AI model to automate customer service responses. A customer receives an incorrect response that results in a financial loss. Who is primarily accountable for this error?

Accepted Answer

The organization that deployed the AI. The organization that deployed the AI, because accountability for an AI system's outcomes rests with the entity that chose to put it into production and use it in customer-facing operations. Under governance frameworks such as the NIST AI RMF and OECD AI Principles, the deploying organization owns the risk, must ensure human oversight, and bears responsibility for harms like financial loss caused by automated responses. The AI model (A) is a tool and cannot hold legal or ethical accountability, and the customer (D) is the harmed party, not the responsible one. The developer (B) may share some technical responsibility for design or defects, but primary accountability for how the model is used and its business impact lies with the deployer.

Answer

The AI model

Answer

The developer who built the model

Answer

The customer who received the response

Question 9

A Salesforce admin wants to create a custom predictive model that predicts whether a support case will be escalated based on historical case data. The admin has identified the prediction field as 'Escalated__c' (checkbox). Which Einstein AI feature should they use to build this model without writing code?

Accepted Answer

Einstein Prediction Builder. Einstein Prediction Builder is the correct choice because it allows admins to create custom predictive models using point-and-click tools, without writing code. The admin needs to predict a binary outcome (whether a case will be escalated) based on historical data, and Prediction Builder is specifically designed for this use case: it lets you select a standard or custom object (like Case), choose a checkbox field (Escalated__c) as the prediction field, and automatically trains a model using the platform's AI engine.

Answer

Einstein Discovery

Answer

Einstein Case Classification

Answer

Einstein Next Best Action

Question 10

An admin is setting up Einstein Bot for a customer service chat. The bot needs to collect the customer's account number before transferring to a human agent. What should the admin configure?

Accepted Answer

Create a Dialog to ask for the account number. In Einstein Bots, a Dialog is the conversational building block used to prompt the customer for input, such as asking for an account number, and to branch the conversation based on the response. Creating a Dialog that asks for the account number and stores it in a variable is the correct configuration before transferring to a human agent. This is the standard pattern for collecting structured information during a bot interaction.

Answer

Add a Variable with the account number field

Answer

Define a Rule to validate the account number

Answer

Use a Set Value action to assign the account number

Question 11

A company is building an Einstein Bot that needs to handle customer inquiries and escalate to a human agent when necessary. The bot must also analyze conversations to improve its performance. Which THREE capabilities should they configure? (Choose 3)

Accepted Answer

Intents and entities to understand customer requests. Option C is correct because Einstein Bots rely on intents (what the customer wants) and entities (the specific data values extracted from the utterance) to interpret natural-language requests and drive the correct dialog flow. Option D is correct because Bot Analytics provides the reporting on bot sessions, intent matches, and escalation/containment outcomes needed to analyze conversations and improve performance. Option E is correct because a handoff (escalation) to a human agent is the built-in mechanism that transfers the conversation when the bot cannot resolve the request, which is exactly the escalation requirement. Option A is not appropriate because Einstein Case Classification is a predictive feature that categorizes Cases, not bot conversations. Option B is not appropriate because Einstein Discovery is an analytics/ML prediction tool for datasets, not the mechanism for analyzing bot logs or conversation transcripts.

Answer

Einstein Case Classification to categorize conversations

Answer

Einstein Discovery to analyze bot logs

Question 12

A company uses Einstein's predictive lead scoring. The model inadvertently overweights leads from certain geographic regions. Which action aligns with Salesforce's Responsible AI principles?

Accepted Answer

Implement a feedback loop to continuously monitor and adjust.. Option A is correct because Salesforce's Responsible AI principles emphasize ongoing human oversight, transparency, and accountability, so implementing a feedback loop to continuously monitor and adjust the model directly addresses detected bias while keeping humans in control. This approach aligns with the Einstein Trust Layer's guidance to regularly review model outputs, measure fairness, and remediate drift or skewed weighting rather than ignoring it. Option B does not fit because simply removing the geographic feature can degrade model performance and does not by itself establish the monitoring and governance Responsible AI requires. Option C is wrong because accepting a biased model just for overall accuracy violates fairness and accountability principles, and Option D is wrong because restricting use to favorable regions hides rather than mitigates the bias.

Answer

Remove the geographic feature from the model.

Answer

Use the model as-is because it improves overall accuracy.

Answer

Only use the model for regions where it performs well.

Question 13

A news aggregator app uses an AI algorithm to personalize the news feed for each user. The algorithm selects articles based on past clicks and reading time. Recently, a study reveals that the algorithm disproportionately shows sensational and polarizing news to users from certain political orientations, while showing more neutral content to others. The company's user engagement metrics have increased, but journalists express concern about reinforcing echo chambers and misinformation. The company wants to uphold ethical standards while keeping users engaged. What should they do?

Accepted Answer

Modify the algorithm to include diversity and reliability scores for news sources, promoting a balanced feed.. Adding diversity and reliability scores directly addresses the algorithmic bias identified in the study while preserving personalization and engagement; it mitigates echo chambers and misinformation by weighting sources for balance and trustworthiness rather than optimizing solely for clicks and reading time. Options B, C, and D do not fit: B shifts the burden to users and does not fix the underlying biased ranking, C removes personalization entirely and would likely reduce engagement, and D ignores the ethical problem by prioritizing engagement metrics over fairness and accuracy.

Answer

Allow users to manually select the types of news they want to see.

Answer

Show the same generic news feed to all users.

Answer

Continue with the current algorithm since it increases engagement.

Question 14

A service manager wants to automatically log emails and events from Gmail into Salesforce without manual user intervention. Which Einstein feature should they enable?

Accepted Answer

Einstein Activity Capture. Einstein Activity Capture (D) is the correct feature because it automatically logs emails and events from Gmail into Salesforce without requiring manual user intervention. It uses server-side synchronization to capture and associate email and calendar data with relevant Salesforce records, eliminating the need for plugins or manual logging.

Answer

Einstein Email Insights

Answer

Einstein Conversation Insights

Answer

Einstein Bots

Question 15

Which THREE types of data sources are commonly integrated into Salesforce Data Cloud for AI use cases?

Accepted Answer

Third-party demographic data. Option A (Third-party demographic data) is correct because Data Cloud commonly ingests external datasets such as demographic or firmographic attributes from providers to enrich unified customer profiles for AI segmentation and scoring. Option B (Web and mobile app engagement data) is correct because behavioral event streams from websites and mobile apps are ingested via sources like Web/Mobile SDKs or Interaction/Ingestion APIs to power real-time personalization and predictive models. Option C (CRM transaction records) is correct because CRM data such as Sales/Service Cloud objects and transactional history are core Data Cloud sources, harmonized into the Customer 360 data model via Data Streams and Data Model Objects. Option D (Model training logs) does not belong because those are internal artifacts of model development/training pipelines, not a standard customer data source integrated into Data Cloud. Option E (Data transformation scripts) does not belong because scripts are processing logic (e.g., SQL transformations in Data Cloud), not a data source type ingested for AI use cases.

Answer

Model training logs

Answer

Data transformation scripts