AI Associate Practice Exam 58 — 20 Questions

Question 1

A global company needs to ensure that customer data used for AI models complies with multiple regional regulations (GDPR, CCPA, LGPD). Which data governance practice is most effective?

Accepted Answer

Use a unified data catalog with tagging and classification.. The correct option is B: a unified data catalog with tagging and classification is the most effective practice because it provides a centralized inventory of data assets with metadata that identifies which records are subject to GDPR, CCPA, or LGPD, enabling consistent policy enforcement, access control, and auditability across regions. Tagging and classification allow the company to apply region-specific rules to the same data set without duplicating or isolating storage, which is essential for AI models that may consume data across jurisdictions. Option A is overly blunt because applying the strictest regulation globally can impose unnecessary restrictions and does not by itself map data to each regulation's requirements. Option C is insufficient because consolidating data in one warehouse does not provide the metadata, lineage, or classification needed for regulatory compliance. Option D is also inadequate because separate regional management creates fragmented governance, inconsistent policies, and poor visibility for a global AI data supply chain.

Answer

Apply the strictest regulation globally.

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Store all data in a single data warehouse.

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Allow each region to manage its own data separately.

Question 2

Which TWO practices contribute to ethical AI transparency?

Accepted Answer

Providing user-facing explanations.. Option B is correct because providing user-facing explanations directly supports transparency: it lets affected users understand what an AI system did and why, which is a core requirement of ethical AI disclosure (e.g., GDPR Articles 13-15 rights to meaningful information about automated decision-making). Option E is correct because documenting model decisions creates an auditable record of inputs, logic, and outcomes, enabling accountability, review, and reproducibility — a foundational transparency practice in AI governance frameworks. Option A is not inherently a transparency practice: open-source algorithms may aid scrutiny, but transparency concerns disclosure of system behavior and decisions, not merely code availability, and proprietary models can still be transparent. Option C is not a transparency practice and can increase privacy and bias risks, since collecting sensitive demographic data requires justification and safeguards rather than contributing to transparency. Option D concerns user control and consent (autonomy), not transparency; opting out does not by itself make an AI system's operations or decisions more understandable.

Answer

Using open-source algorithms.

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Collecting sensitive demographic data.

Answer

Allowing users to opt out of AI processing.

Question 3

A Salesforce admin is configuring an AI model to automatically approve customer refunds under $50. To ensure ethical use, what is the most important action?

Accepted Answer

Implement a human review process for all AI decisions. Option B is correct because implementing a human review process for all AI decisions ensures meaningful human oversight of automated refund approvals, which is the core ethical safeguard for consequential decisions affecting customers. This aligns with ethical AI principles of accountability and transparency, particularly since refund approvals have financial and customer-relationship impacts. Option A does not fit because training on all historical data may perpetuate bias and does not itself provide ethical oversight. Option C is wrong because raising the threshold to $100 expands automated authority without any ethical control. Option D is wrong because disabling logging reduces auditability and accountability, undermining rather than supporting ethical use.

Answer

Train the model on all historical refund data to maximize accuracy

Answer

Set the approval threshold to $100 to cover more cases

Answer

Disable logging to protect customer privacy

Question 4

A Salesforce administrator is preparing data for an AI model that predicts which leads are most likely to convert. They need to ensure the data is high quality. Which two data quality dimensions are most critical to address before training the model? (Choose two.)

Accepted Answer

Consistency. Completeness and consistency are fundamental data quality dimensions for AI. Missing values can skew predictions, while inconsistent data formats can lead to incorrect feature interpretation. Addressing these ensures the model learns from reliable, standardized data, improving its ability to predict lead conversion accurately.

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Timeliness

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Precision

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Accessibility

Question 5

A company deploys an AI system that recommends loan amounts. They want to ensure explainability. Which approach best aligns with ethical AI?

Accepted Answer

Provide loan recommendations with a detailed rationale.. Providing loan recommendations with a detailed rationale directly supports explainability, allowing stakeholders to understand how and why a specific loan amount was recommended, which is a core ethical AI principle for high-stakes decisions like lending. Explainability in this scenario means the system's outputs can be interpreted and justified, helping ensure fairness, accountability, and regulatory compliance. Option A does not fit because dynamically updating features without documentation reduces transparency and traceability. Option B does not fit because limiting use to internal employees does not make the system's decisions explainable. Option D does not fit because a black-box neural network prioritizes accuracy over interpretability, which conflicts with the requirement for explainability.

Answer

Allow the system to update its features dynamically without documentation.

Answer

Restrict the system's use to only internal employees.

Answer

Use a black-box neural network for highest accuracy.

Question 6

To ensure AI model fairness and avoid biased outcomes, which practice is most critical when preparing training data?

Accepted Answer

Use balanced training data. Use balanced training data, because fairness in AI models depends primarily on the training set representing all relevant groups proportionally, so the model does not learn skewed patterns that disadvantage underrepresented populations. Balanced data reduces the risk of bias by preventing overrepresentation of one class or demographic, which would otherwise lead the model to favor that group in its predictions. Using only recent data (A) does not address representation and may even exclude historically marginalized groups, while increasing model complexity (B) can amplify bias rather than fix it. Adding more features (D) is also not a fairness remedy and can introduce proxy variables that worsen discriminatory outcomes.

Answer

Use only recent data

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Increase model complexity

Answer

Use more features

Question 7

A financial services firm deploys an Einstein AI chatbot that provides investment advice. A customer asks why a particular recommendation was made. The chatbot is unable to provide any reasoning. Which ethical principle is most directly violated?

Accepted Answer

Transparency. Transparency, because the scenario centers on a customer's inability to understand why an AI system made a specific investment recommendation. Transparency in AI ethics requires that systems provide understandable explanations of their decisions and reasoning, especially in high-stakes domains like financial advice. Since the chatbot cannot explain its recommendation, it directly violates this principle. Privacy (A) concerns data protection and consent, which are not at issue here, while Reliability (B) concerns consistent and accurate performance, and Human oversight (D) concerns human review or control of AI decisions, neither of which is the primary failure described.

Answer

Privacy

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Reliability

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Human oversight

Question 8

A sales team uses an AI tool to prioritize leads. The tool is found to give lower scores to leads from certain regions. What ethical principle is most violated?

Accepted Answer

Fairness. Fairness, because the AI tool is systematically assigning lower scores to leads from certain regions, which constitutes discriminatory or biased treatment across groups and directly violates the ethical principle of fairness (often framed as justice, equity, or non-discrimination in AI ethics). Fairness requires that automated decisions not disadvantage individuals based on protected or irrelevant attributes such as geographic origin. Accountability (A) concerns who is responsible for the system's outcomes, privacy (B) concerns handling of personal data, and transparency (C) concerns explainability and disclosure of how the system works; none of these is the principle most directly breached by biased scoring itself.

Answer

Accountability

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Privacy

Answer

Transparency

Question 9

A sales manager sees that the Einstein Lead Score for a high-profile lead is low but expects it to be high. What should the manager do to investigate the discrepancy?

Accepted Answer

Use the Einstein Lead Score Insights to analyze the factors that contributed to the score.. The correct option is D: Use the Einstein Lead Score Insights to analyze the factors that contributed to the score. Einstein Lead Score Insights provides a breakdown of the specific fields and behaviors that positively or negatively influenced a lead's score, allowing the manager to understand exactly why the high-profile lead received a low score. This is the appropriate investigative step because it surfaces the model's reasoning without altering data or the model itself. Option A is wrong because retraining is a broad administrative action, not a per-lead investigation, and would not explain this specific score. Option B is wrong because manually overriding the score bypasses investigation and corrupts the score's meaning, and option C is wrong because disabling scoring for the record removes the feature rather than diagnosing the discrepancy.

Answer

Retrain the model to improve its accuracy for that lead.

Answer

Override the score manually to reflect their judgment.

Answer

Disable Einstein Lead Scoring for that lead record.

Question 10

An AI Associate reviews the bot configuration and test results. Which action best addresses the ethical issue?

Accepted Answer

Add Spanish language support with separate sentiment model and intents.. The correct option is D: Add Spanish language support with separate sentiment model and intents. This is right because sentiment models and intent classifiers are language-specific; Spanish text processed by an English model produces unreliable sentiment and intent predictions, so a dedicated Spanish model and Spanish intents are needed to serve those users fairly and accurately. Options A and C merely mask the problem by changing thresholds or disabling sentiment for non-English conversations, which degrades service rather than fixing the underlying language gap. Option B is wrong because collecting Spanish data to retrain the English model still conflates two languages in one model and does not provide proper Spanish language support.

Answer

Increase the fallback threshold for all languages.

Answer

Collect more data from Spanish-speaking customers and retrain the English model.

Answer

Disable sentiment analysis for non-English conversations.

Question 11

A company wants to use Einstein Bots to handle common customer service inquiries. Which feature should be enabled to allow the bot to escalate to a live agent when it cannot resolve the issue?

Accepted Answer

Omni-Channel Flow. The correct option is C, Omni-Channel Flow, because Einstein Bots use an Omni-Channel Flow to route a conversation to a live agent when the bot cannot resolve the customer's issue, transferring the session into the Omni-Channel routing queue. This flow-based escalation is the standard mechanism for handing off bot sessions to human agents in Service Cloud. In contrast, A, Einstein Case Classification, predicts case fields to help categorize cases but does not perform live-agent routing. B, Einstein Reply Recommendations, suggests replies to agents and does not escalate bot sessions. D, Einstein Article Recommendations, surfaces knowledge articles to agents and likewise does not provide bot-to-agent escalation.

Answer

Einstein Case Classification

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Einstein Reply Recommendations

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Einstein Article Recommendations

Question 12

A news outlet wants to build an AI model that predicts article popularity using real-time social media mentions. Which data source type should they use to ingest tweets?

Accepted Answer

Data Stream with Ingestion API connector. The correct option is D, Data Stream with Ingestion API connector, because ingesting real-time social media mentions such as tweets requires a streaming ingestion mechanism that can continuously receive and process high-velocity data as it arrives. An Ingestion API connector is designed for exactly this purpose, enabling near real-time data flow into the platform for use by an AI model predicting article popularity. The other options do not fit: A, Calculated Insight, refers to derived metrics rather than raw ingestion; B, Data Transform, is used to reshape or process data after ingestion; and C, Data Lake Object, is a storage abstraction, not a real-time ingestion source.

Answer

Calculated Insight

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Data Transform

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Data Lake Object

Question 13

A company deployed an AI chatbot to handle customer service. The chatbot sometimes generates responses that are biased against certain demographics. The company wants to mitigate this. What is the best first step?

Accepted Answer

Conduct an AI ethics audit.. The best first step is D, conduct an AI ethics audit, because the company must first systematically identify, assess, and document where and how bias is entering the chatbot's responses before choosing a remediation technique. An ethics audit evaluates training data, model behavior, deployment context, and governance controls, providing the evidence needed to select effective mitigations. Options A, B, and C do not fit as first steps: restricting the chatbot to predefined responses (A) is a blunt functional limitation rather than a bias diagnosis, increasing model complexity (B) can worsen or obscure bias, and simply removing all demographic data from training (C) is not sufficient because bias can persist through proxy variables and historical patterns in the remaining data.

Answer

Restrict chatbot to only predefined responses.

Answer

Increase model complexity.

Answer

Remove all demographic data from training.

Question 14

Which THREE actions align with Salesforce's responsible AI principles?

Accepted Answer

Involving diverse stakeholders in AI development.. Option A is correct because Salesforce's responsible AI principles emphasize inclusive, multi-stakeholder development so that diverse perspectives, values, and potential harms are surfaced early in the AI lifecycle. Option B is correct because human oversight for critical decisions is a core tenet of responsible AI, ensuring that consequential outcomes remain accountable to people rather than being fully delegated to automated systems. Option C is correct because continuous monitoring of model performance for bias is required to detect drift, disparate impact, and degradation after deployment, aligning with Salesforce's commitment to ongoing testing and evaluation. Option D is not correct because responsible AI does not mandate using only internal data sources; external or third-party data can be used provided it is lawfully sourced, representative, and governed appropriately. Option E is not correct because prioritizing accuracy over fairness inverts the responsible AI principle that fairness, safety, and trust must be balanced with, not subordinated to, raw performance metrics.

Answer

Using only internal data sources.

Answer

Prioritizing model accuracy over fairness.

Question 15

A data engineer is preparing a dataset for an Einstein Discovery model to predict customer churn. The dataset includes a field 'CustomerSince' that records the date a customer first joined. The engineer wants to create a new feature that represents the tenure of the customer in months. Which approach is most appropriate?

Accepted Answer

Calculate the difference between the current date and 'CustomerSince' in months and create a new field 'TenureMonths'.. Deriving a 'TenureMonths' feature from 'CustomerSince' is the most appropriate approach because it transforms a date into a meaningful numerical feature that directly relates to churn. Raw dates, year extraction, or binary flags lose information or are not directly usable, while tenure in months provides a continuous measure of customer loyalty.

Answer

Create a binary feature indicating whether the customer has been with the company for more than one year.

Answer

Convert 'CustomerSince' to a numeric value by extracting the year and use that as a feature.

Answer

Use the raw 'CustomerSince' date field directly in the model without transformation.

Question 16

A data scientist is building a model for credit scoring. They have access to a dataset with historical bias. What should they do?

Accepted Answer

Apply fairness constraints during training.. Apply fairness constraints during training. When a dataset contains historical bias, the model can learn and perpetuate that bias, so incorporating fairness constraints (e.g., demographic parity, equalized odds) into the training objective helps mitigate disparate impact while still learning from the data. Discarding all biased variables (B) is often ineffective because bias can be encoded in many correlated features and may remove useful signal. Using the data as is (C) would perpetuate discrimination, and a more complex model (D) typically increases the capacity to fit and amplify bias rather than reduce it.

Answer

Discard all biased variables.

Answer

Use the data as is because it reflects reality.

Answer

Use a more complex model to reduce bias.

Question 17

An autonomous vehicle AI is trained in simulation but performs poorly in rain and snow. The development team decides to deploy anyway, arguing that bad weather is rare. What ethical concern is most critical?

Accepted Answer

Insufficient robustness and safety. The correct option is B (Insufficient robustness and safety): the model was trained only in simulation and fails in rain and snow, so deploying it means the system lacks the robustness to handle real-world conditions, directly endangering lives—an unacceptable safety risk regardless of how rare bad weather is. Deploying a known-failing system violates the core ethical duty to avoid foreseeable harm. Option A is not the best fit because 'bias' typically refers to unfair treatment of people or groups, not environmental conditions. Option C does not apply since the team is clearly identifiable and accountable, and Option D is not the issue because the failure is a performance/robustness gap, not a lack of explainability.

Answer

Bias against certain weather conditions

Answer

Lack of human accountability

Answer

Lack of transparency

Question 18

A data scientist needs to feed customer interaction data into Einstein Discovery for predictive analysis. Which data format is required?

Accepted Answer

CSV. Einstein Discovery requires customer interaction data to be supplied as a flat, tabular dataset, and CSV is the supported format for importing that data into a Discovery dataset. CSV's row-and-column structure maps directly to Discovery's fields and records, which is why option A is correct. XML (B), Parquet (C), and JSON (D) are not the required format for feeding data into Einstein Discovery, even though they are common data-interchange or columnar-storage formats elsewhere.

Answer

XML

Answer

Parquet

Answer

JSON

Question 19

A company has deployed an AI-powered chatbot to handle customer service inquiries. The chatbot is designed to answer frequently asked questions and escalate complex issues to human agents. Which action best aligns with ethical AI principles regarding transparency?

Accepted Answer

Clearly disclose that the chatbot is an AI and provide an option for customers to switch to a human agent.. Option D is correct because ethical AI transparency requires that users know they are interacting with an AI system, and providing an easy escalation path to a human agent respects user autonomy and informed consent. Disclosing the chatbot's AI nature and offering a human alternative directly satisfies transparency and accountability principles for customer-facing AI. Option A is wrong because fully autonomous handling without disclosure or escalation removes human oversight and hides the AI's role. Option B is wrong because training on unaudited historical data can perpetuate bias and violates fairness and accountability, not transparency. Option C is wrong because impersonating a human agent is deceptive and directly contradicts transparency requirements.

Answer

Configure the chatbot to answer all queries without human intervention to maximize efficiency.

Answer

Use historical customer interaction data without auditing for bias to train the chatbot's responses.

Answer

Program the chatbot to identify itself as a human agent to build trust with customers.

Question 20

Which TWO of the following are required for GDPR compliance when using AI with personal data?

Accepted Answer

Obtaining explicit consent from users before processing their data. Option B is correct because GDPR requires a lawful basis for processing personal data, and explicit consent (Article 6(1)(a) and Article 9(2)(a) for special categories) is one such valid basis that must be obtained before processing, including for AI training or inference. Option D is correct because GDPR grants data subjects the right to erasure ("right to be forgotten") under Article 17, so organizations using personal data in AI systems must be able to honor deletion requests. Option A is incorrect because GDPR's storage limitation principle (Article 5(1)(e)) prohibits keeping personal data longer than necessary, so indefinite retention is non-compliant. Option C is incorrect because selling user data requires a valid lawful basis and transparency, and typically falls outside legitimate AI improvement without explicit consent, violating GDPR principles. Option E is incorrect because GDPR's transparency obligations (Articles 12–14) require informing users about how their data is processed, so silent processing is non-compliant.

Answer

Storing data indefinitely for future AI training

Answer

Selling user data to third parties for AI model improvement

Answer

Processing data without informing users