AI Associate Practice Test 36 — 15 Questions

Question 1

A retail company wants to use Einstein AI to personalize marketing offers. They plan to include customer purchase history and demographic data. What is the essential first step to ensure ethical use of customer data?

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

Accepted Answer

Obtain explicit consent from customers for using their data in AI personalization.. The correct answer is B: Obtain explicit consent from customers for using their data in AI personalization. Ethical AI use in marketing requires that customers knowingly agree to how their purchase history and demographic data will be used, since consent is the foundational principle for lawful and transparent data processing under privacy regulations like GDPR and CCPA. Option A is insufficient because not opting out is not the same as affirmative permission for AI-driven personalization, and opt-out frameworks still may not satisfy consent requirements. Option C is wrong because data collected for general business purposes cannot automatically be repurposed for AI personalization without a lawful basis. Option D is incorrect because anonymization, while a privacy safeguard, does not replace the need for consent and may also undermine personalization that depends on identifiable customer data.

Answer

Only use data from customers who have not opted out of marketing.

Answer

Proceed with available data since it was collected for business purposes.

Answer

Anonymize all customer data before feeding it into the model.

Question 2

Refer to the exhibit. Which ethical principle is violated?

Accepted Answer

Transparency. Transparency, because the scenario involves concealing information or failing to disclose how a system operates, which directly violates the ethical principle of transparency. Transparency requires that relevant stakeholders have visibility into data use, decision-making, and system behavior, and hiding such details undermines informed consent and trust. Safety (A) concerns preventing harm, fairness (B) concerns equitable treatment and bias, and accountability (C) concerns taking responsibility for actions, none of which are the primary violation when information is deliberately withheld.

Answer

Safety

Answer

Fairness

Answer

Accountability

Question 3

A financial institution deploys an AI model to approve loan applications. The model uses features like income, credit score, and postal code. An audit reveals that the model denies loans at a higher rate for applicants in certain postal codes, which correlate with minority neighborhoods. What should the institution do to align with ethical AI principles?

Accepted Answer

Remove postal code from the model and retrain using only non-biased features.. The best answer is C, but removing postal code alone does not guarantee fairness. Postal code can act as a proxy for race or minority neighborhoods, so dropping it is a reasonable first mitigation step. However, other features such as income or credit score may still correlate with protected characteristics and continue to produce disparate impact. A proper ethical-AI response therefore requires retraining without the problematic feature AND validating the retrained model for disparate impact, using fairness metrics and ongoing monitoring. Option A is insufficient because monitoring alone does not stop ongoing discrimination, and monthly review still allows unfair denials. Option B only adds post-hoc transparency and does not fix the biased model. Option D accepts known disparate impact as a business risk, which conflicts with ethical AI obligations to prevent discrimination.

Answer

Continue using the model but monitor the denial rates monthly.

Answer

Add an explanation to applicants in affected areas about why they were denied.

Answer

Publish a report on the model's disparate impact and accept it as a business risk.

Question 4

A Salesforce admin wants to use Einstein Prediction Builder to predict customer churn. What ethical consideration is most important?

Accepted Answer

Data privacy. Data privacy is the most important ethical consideration when using customer data for predictions like churn. Option A is wrong because cost of implementation is a business concern, not an ethical one. Option B is wrong because while model accuracy is important, it is a technical metric and not the primary ethical concern; privacy takes precedence. Option D is wrong because transparency of predictions is important for trust, but data privacy is more foundational when handling sensitive customer data.

Answer

Cost of implementation

Answer

Model accuracy

Answer

Transparency of predictions

Question 5

Refer to the exhibit. A company uses this policy for a customer-facing AI model. What is the most critical ethical risk?

Accepted Answer

Lack of transparency. Lack of transparency. The policy explicitly states 'explainability: none', which means the AI model's decisions cannot be explained or understood by users. This lack of transparency prevents users from knowing why a decision was made, challenging the decision, or trusting the system, making it the most critical ethical risk. Option A (Performance) is not indicated in the policy and is less critical than transparency. Option B (Data privacy) is important but the policy does not mention any privacy violations. Option C (Model accuracy) is not addressed in the policy and while important, transparency is more fundamental for ethical AI.

Answer

Performance

Answer

Data privacy

Answer

Model accuracy

Question 6

A company uses an AI model to screen job applications. They discover the model is less likely to recommend female candidates. What should the company prioritize first?

Accepted Answer

Implement a fairness metric to evaluate bias.. The company should first implement a fairness metric to evaluate bias (Option C), because before mitigating the problem it must quantify and measure the disparity in the model's recommendations across genders using metrics such as demographic parity or equal opportunity. Without a defined fairness metric, any remediation is unguided and cannot be validated. Removing gender-related features (Option A) is a possible later mitigation, but proxy variables can still encode gender, so it is not the first priority. Retraining with only male candidates (Option B) would worsen and entrench the bias, and simply increasing training data size (Option D) does not guarantee reduced bias and may even amplify existing imbalances.

Answer

Remove gender-related features from the model.

Answer

Re-train the model with only male candidates.

Answer

Increase the model's training data size.

Question 7

A non-profit organization uses Data Cloud to manage donor data from multiple sources (email campaigns, event attendance, donations). They want to use an AI model to predict future donations. The data scientist says the model needs a unified view of each donor with consistent fields. What is the first step the data architect should take in Data Cloud to enable this?

Accepted Answer

Define a Data Model that maps each source's fields to a common donor object. Define a Data Model that maps each source's fields to a common donor object. In Data Cloud, the Data Model (Data Cloud canonical model plus custom objects) is what provides the unified, consistent schema that harmonizes disparate source fields into a single donor profile, which is the prerequisite for any AI model needing consistent inputs. Data Streams (B) only ingest raw source data and do not by themselves reconcile differing field names or semantics across email, events, and donations. Data Actions (C) are used for triggering workflows or activations, not for establishing the unified schema, and training on raw data (D) skips the required harmonization step and would produce inconsistent features.

Answer

Create a Data Stream for each source (email, events, donations)

Answer

Set up Data Actions to clean data at each source

Answer

Immediately start training the AI model on raw data from the streams

Question 8

Refer to the exhibit. What action should be taken?

Accepted Answer

Retrain the model with balanced training data. The correct action is D: retrain the model with balanced training data, because the exhibit shows a classification model whose performance metrics (such as precision, recall, or confusion matrix) indicate bias toward one class due to imbalanced training data. Retraining with balanced data addresses the root cause by giving the model representative examples of each class, improving fairness and overall predictive performance. Increasing the accuracy threshold (A) only changes the decision cutoff and does not fix the underlying data imbalance, while removing the model (B) discards a potentially salvageable asset, and deploying as is (C) would ship a biased or unreliable model into production.

Answer

Increase the accuracy threshold

Answer

Remove the model

Answer

Deploy as is

Question 9

Which TWO components are essential for an AI ethics governance framework?

Accepted Answer

Conducting regular audits. Option D (Conducting regular audits) is correct because an AI ethics governance framework requires ongoing, independent review of models, data, and outcomes to detect bias, drift, and compliance gaps, ensuring accountability over time rather than a one-time check. Option E (Establishing a code of ethics) is correct because a formal code of ethics codifies the organization's principles, values, and behavioral expectations for AI development and use, giving teams a shared standard against which decisions and audits are measured. Together, the code sets the normative rules while audits verify adherence, which is why these two are essential governance components. Option A (Using the most recent algorithms) is not essential, since newer algorithms are not inherently more ethical and governance is about process and accountability, not recency. Option B (Maximizing data collection) is not essential and can even increase privacy and bias risks, so it is not a governance requirement. Option C (Assigning an ethics officer) is valuable in practice but is not marked correct here, as the question asks for the two essential framework components of a code of ethics and regular audits.

Answer

Using the most recent algorithms

Answer

Maximizing data collection

Answer

Assigning an ethics officer

Question 10

A healthcare organization uses AI to prioritize patient appointments. The AI gives lower priority to patients with a specific chronic condition. To ensure ethical AI, what should the organization do?

Accepted Answer

Conduct a fairness audit and involve medical experts to review the model's decisions. The correct answer is D: conduct a fairness audit and involve medical experts to review the model's decisions. In a healthcare scenario where an AI system appears to systematically deprioritize patients with a specific chronic condition, a fairness audit is the appropriate governance step to detect and quantify bias across protected or clinically sensitive groups, while medical expert review ensures clinical validity and ethical alignment before any remediation. This approach addresses the bias without abandoning the system's benefits. Option A does not fit because simply adding more data from the affected group does not guarantee removal of bias and may even reinforce it. Option B is overly drastic, as disabling the system is not required before investigating and mitigating the issue. Option C is incorrect because data-driven decisions can still encode historical or structural bias, so accepting them uncritically is unethical.

Answer

Train the model on more data from patients with that condition

Answer

Immediately disable the AI system and revert to manual scheduling

Answer

Accept the model's decisions since they are based on data

Question 11

A healthcare provider uses Einstein's Prediction Builder to predict patient readmission risk. The model outputs a risk score, but clinicians do not understand how the score is calculated. According to ethical AI principles, what should the provider implement?

Accepted Answer

Provide model interpretability by documenting and surfacing the key factors that influence each prediction, with clinician-facing explanations.. The correct answer is A: providing model interpretability by documenting and surfacing the key factors influencing predictions. Ethical AI principles emphasize transparency and interpretability, especially in healthcare, so clinicians must be able to understand which patient attributes (e.g., prior admissions, medication history) drove a given readmission risk score. Einstein Prediction Builder does not offer a native per-prediction explainability feature, so the provider should implement model interpretability through feature importance analysis and clinician-facing explanations. Option B is wrong because accuracy alone does not satisfy the ethical duty to explain clinical decision support outputs. Option C is insufficient because transparency must reach the clinicians using the score, not just the data science team. Option D is not required, since interpretability can be addressed without necessarily sacrificing accuracy.

Answer

Proceed with the model since it provides accurate predictions.

Answer

Train only the data science team on the model's inner workings.

Answer

Replace the model with a simpler, less accurate but fully transparent model.

Question 12

A company is developing an AI system to screen job applications. They want to ensure compliance with ethical AI standards and avoid discrimination. Which approach demonstrates the most robust ethical governance?

Accepted Answer

Implement an AI ethics board with cross-functional stakeholders, conduct bias testing before deployment, and establish ongoing monitoring. (Implement an AI ethics board with cross-functional stakeholders, conduct bias testing before deployment, and establish ongoing monitoring) is the most robust approach. Option A (only test the model for bias after receiving complaints) is reactive and insufficient. Option B (rely solely on Salesforce's built-in fairness metrics) lacks organizational governance and may miss subtle biases. Option C (remove sensitive attributes from training data) can still result in discrimination through proxy variables.

Answer

Only test the model for bias after receiving complaints from applicants

Answer

Rely solely on Salesforce's built-in fairness metrics to validate the model

Answer

Remove sensitive attributes from training data to ensure fairness

Question 13

A telecom company uses Einstein Discovery to predict customer churn. The training dataset contains 100,000 records, but only 5% represent churned customers. The model achieves 95% accuracy on a holdout test set, but the recall for churn is only 20%. The business wants to proactively retain at-risk customers, so they need to identify as many churners as possible. What action should the data scientist take to improve churn recall?

Accepted Answer

Oversample the minority class using SMOTE to create synthetic churn examples.. The dataset is highly imbalanced (5% churn), so the model achieves high accuracy by predicting the majority class but misses most churners (20% recall). Oversampling the minority class with SMOTE creates synthetic churn examples, giving the model more signal to learn churn patterns and improving recall for the minority class. This directly targets the class imbalance causing low recall.

Answer

Increase the regularization parameter to prevent overfitting.

Answer

Collect more data, especially of churned customers.

Answer

Undersample the majority class to match the minority class size.

Question 14

Refer to the exhibit. A developer receives this fairness check error. What is the most likely cause?

Accepted Answer

The model has higher false positive and false negative rates for Group B.. The fairness check error indicates a disparity in error rates between groups, and option A correctly identifies that the model has higher false positive and false negative rates for Group B, which directly violates fairness metrics such as equalized odds or equal opportunity. This scenario typically arises when a model's performance systematically differs across demographic groups, triggering fairness alerts. Option B is not the most likely cause because insufficient training data for Group A would more likely affect Group A's performance, not Group B's error rates. Option C is too generic, as overfitting alone does not specifically explain a group-based fairness violation. Option D is incorrect because the presence of a reweighting recommendation implies the model is not yet fair, and reweighting is a mitigation technique, not evidence of fairness.

Answer

The error is due to insufficient training data for Group A.

Answer

The model is overfitting.

Answer

The recommendation suggests reweighting, so the model is already fair.

Question 15

Which TWO actions are most effective in promoting transparency in AI systems? (Choose two.)

Accepted Answer

Provide a model card that describes the purpose, accuracy, and limitations of the AI model.. Option B is correct because a model card is a standardized transparency artifact that documents a model's intended purpose, performance metrics such as accuracy, and known limitations, giving stakeholders the context needed to understand and appropriately use the system. Option E is correct because an explanation feature (e.g., local interpretable explanations such as SHAP or LIME outputs) reveals the factors behind an individual prediction, which is a core requirement of transparency and the right to explanation in frameworks like the EU AI Act and GDPR Article 22. Option A does not belong because dumping raw source code is not effective transparency—it exposes intellectual property, may reveal security weaknesses, and does not communicate model behavior or limitations to non-expert stakeholders. Option C is incorrect because withholding training data sources actively reduces transparency and undermines accountability and bias auditing. Option D is incorrect because encrypting customer data is a security and privacy control, not a transparency mechanism—it protects data confidentiality but does not make the AI system's behavior or decisions more understandable.

Answer

Publish the entire source code of the model online.

Answer

Withhold the data sources used for training to protect the company's competitive advantage.

Answer

Encrypt all customer data used in the model.