AI Associate Practice Test 35 — 15 Questions

Question 1

A company uses AI to monitor employee productivity. Employees feel surveilled. What ethical principle is being violated?

Accepted Answer

Transparency. The scenario describes employees feeling surveilled by AI monitoring. The primary ethical principle implicated is privacy, because continuous AI surveillance can intrude on workers’ personal sphere and create a chilling effect on their behavior. The stem does not state that the monitoring was undisclosed or that employees were not informed, so Transparency (D) is only the best available answer if the principle is understood as the need for openness about AI monitoring; it is not justified by an unsupported claim that the surveillance was concealed. Inclusion (A) concerns equitable access and representation, accountability (B) concerns responsibility for AI outcomes, and safety (C) concerns preventing harm or misuse.

Answer

Inclusion

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Accountability

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Safety

Question 2

An AI system for hiring is deployed. After six months, the HR team notices that the model's recommendations closely mimic past human hires, which were biased. The team wants to correct this. What should be their first step?

Accepted Answer

Implement continuous monitoring and a feedback loop to detect and mitigate bias. The model is replicating historical human bias, so the first step is to establish continuous monitoring and a feedback loop that detects and mitigates bias in the model's recommendations. This creates the mechanism to measure disparate impact, catch drift, and apply corrections iteratively. It is the responsible, actionable first step before retraining or redesigning.

Answer

Shut down the AI system entirely

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Retrain the model with the same historical data but with more features

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Make the model's decision process fully transparent to all candidates

Question 3

Which Salesforce Trusted AI principle ensures that users are informed when they are interacting with an AI-generated output or recommendation?

Accepted Answer

Transparency. The Salesforce Trusted AI principle of Transparency ensures that users are informed when they are interacting with AI-generated outputs or recommendations. This principle requires clear disclosure about AI involvement, enabling users to understand the context and limitations of the AI system. It aligns with the ethical goal of building trust by making AI operations visible and understandable.

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Safety

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Accuracy

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Honesty

Question 4

A CRM administrator is planning to implement predictive AI for lead scoring. Which TWO actions should be taken to ensure data quality?

Accepted Answer

Ensure lead source values are standardized (e.g., 'Web' not 'website' or 'web form'). Option A is correct because predictive lead scoring models depend on consistent categorical inputs; standardizing lead source values such as 'Web' instead of 'website' or 'web form' prevents the model from treating the same source as multiple distinct categories, which would fragment training data and degrade scoring accuracy. Option B is correct because duplicate leads inflate certain patterns and skew the model's learned relationships, while stale leads older than 5 years with no activity add noise and outdated behavioral signals that no longer reflect current buyer behavior, so removing both improves data quality. Option C is incorrect because adding as many fields as possible increases model complexity and risks overfitting rather than improving data quality. Option D is incorrect because manually relabeling all historical leads is impractical, subjective, and not a standard data-quality step for lead scoring. Option E is incorrect because using a smaller dataset reduces training signal and can worsen model performance rather than ensure data quality.

Answer

Use as many fields as possible to increase model complexity

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Manually label all historical leads again to remove bias

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Use a smaller dataset to train faster

Question 5

Which TWO of the following are valid methods to improve data quality in Data Cloud before training an AI model?

Accepted Answer

Use a Data Transform to filter out invalid records. Option B is correct because a Data Transform in Data Cloud lets you apply SQL-based transformations to cleanse and filter records—such as removing rows with null or malformed key fields—so invalid data never reaches the model training dataset. Option E is correct because enabling data profiling on data streams surfaces metrics like completeness, uniqueness, and value distributions, which is exactly how you identify quality problems (duplicates, missing values, outliers) before training. Option A is not a quality method; increasing ingestion frequency only changes how often data lands, not its accuracy or consistency. Option C, data retention policies, governs how long data is stored for compliance and storage management, not its correctness. Option D, Calculated Insights, computes aggregate metrics across DMO relationships for analytics and activation; while it can surface anomalies, it is not a primary data-quality remediation method and is not the best answer for improving data quality before training.

Answer

Increase data stream ingestion frequency

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Implement data retention policies

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Use Calculated Insights to detect anomalies

Question 6

A Salesforce admin is reviewing data sources for Einstein Recommendation Builder. Which two data types are required for training? (Choose two.)

Accepted Answer

User-item interactions. Option B (User-item interactions) is correct because Einstein Recommendation Builder is a collaborative-filtering engine that learns latent relationships between users and items from historical interaction events such as views, clicks, purchases, and ratings; without these interaction records there is no signal to train the model. Option E (Item metadata) is correct because the item catalog with attributes (for example product name, category, description, and other item-level fields) is required to define the items being recommended and to enrich and filter recommendations. Option A (User profile data) is not required — profile attributes are optional enrichment, not a training prerequisite. Option C (Sales reports) is not required — reports are a presentation artifact, not a required training data source. Option D (External product prices) is not required — pricing is optional business data and is not part of the required training inputs.

Answer

User profile data

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Sales reports

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External product prices

Question 7

A nonprofit uses Einstein Recommendations to suggest donations. They notice that the recommendations are not relevant. Which best practice should they follow to improve relevance?

Accepted Answer

Verify that the Recommendation object has enough historical interaction data and that events are correctly tracked.. Option A is correct because Einstein Recommendations relies on sufficient historical interaction data and correctly tracked events (such as views, clicks, and purchases) to generate relevant suggestions; if the Recommendation object lacks enough data or events are misconfigured, relevance degrades. Best practice is to verify data volume and event tracking before tuning other settings. Option B is wrong because deleting records older than 30 days reduces the historical signal Einstein needs, harming relevance rather than improving it. Option C is wrong because increasing the number of recommended items to 10 does not fix poor relevance and may dilute quality. Option D is wrong because displaying recommendations on every page, including the donation receipt page, does not improve relevance and can produce inappropriate or low-quality placements.

Answer

Set a data retention policy to delete records older than 30 days to keep data fresh.

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Increase the number of recommended items to 10 to give more choices.

Answer

Display recommendations on every page, including the donation receipt page.

Question 8

A company uses Einstein Discovery to identify factors that increase case resolution time. After training, the model shows that 'Case_Origin__c' has high importance. What action should the company take?

Accepted Answer

Investigate the categories within Case_Origin to understand their impact.. The correct option is D: investigate the categories within Case_Origin to understand their impact. When Einstein Discovery reports a field such as Case_Origin__c as highly important, the next step is to drill into that field's individual categories (e.g., Phone, Email, Web) to see which values drive longer case resolution times, enabling targeted process improvements. Option A is wrong because removing a high-importance field discards predictive signal rather than acting on the insight. Option B is premature, since interaction terms should be considered only after understanding the main effect of Case_Origin. Option C is irrelevant, as data quality thresholds do not explain why the field is important or how its categories affect resolution time.

Answer

Remove the field from the model to reduce complexity.

Answer

Create interaction terms between Case_Origin and other fields.

Answer

Increase the data quality threshold for Case_Origin records.

Question 9

A company deploys an AI system that makes decisions about loan approvals. For transparency, what should they provide to applicants?

Accepted Answer

Explanation of factors considered. Transparency in AI-driven loan approvals requires providing applicants with an explanation of the factors considered in the decision, such as credit score, income, or debt-to-income ratio. This aligns with ethical AI principles like explainability and fairness, enabling applicants to understand and potentially contest the decision. Providing the full algorithm or training data would expose proprietary information and potentially violate data privacy regulations like GDPR.

Answer

The training data

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The full algorithm

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Confidence scores

Question 10

A company has set up Einstein Next Best Action with a recommendation strategy. They want to ensure that recommendations are personalized based on the customer's recent behavior. What data should be used?

Accepted Answer

Streaming data from Data Cloud that includes recent website interactions.. Einstein Next Best Action requires real-time or near-real-time data to personalize recommendations based on recent customer behavior. Streaming data from Data Cloud captures website interactions as they happen, enabling the recommendation engine to use the most current signals (e.g., page views, clicks) to adjust offers dynamically.

Answer

Event data from the website tracked via Google Analytics.

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Static profile fields like customer age and location.

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Historical data from a data warehouse updated daily.

Question 11

Refer to the exhibit. What is the most likely cause of the pipeline failure?

Accepted Answer

Picklist value does not exist in the target picklist field. The question cannot be answered as written because the referenced exhibit is missing. The stem says 'Refer to the exhibit' but no exhibit (error message, log, or screenshot) is included, so there is no evidence that the failure is caused by a picklist value missing from the target picklist field. Without the exhibit, none of the options can be confirmed as the most likely cause. The exhibit must be supplied (e.g., a validation error such as 'bad value for restricted picklist field' or 'picklist value does not exist') before option D can be justified.

Answer

Data type mismatch between source and target

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Insufficient permissions to access the field

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Connection timeout during data transfer

Question 12

An organization uses Einstein Search to power a portal's search functionality. Users report that search results are not ranking relevant documents highly. Which configuration change is most likely to improve relevance?

Accepted Answer

Tune the search algorithm by promoting frequently accessed content.. Tuning the search algorithm to promote frequently accessed content can improve relevance ranking by prioritizing popular or authoritative documents. Option A is incorrect because stemming and synonyms improve recall but do not directly enhance ranking. Option C is incorrect because increasing indexed fields can add noise and dilute relevance. Option D is incorrect because decreasing index refresh frequency affects freshness, not the ranking algorithm.

Answer

Enable stemming and synonyms.

Answer

Increase the number of indexed fields.

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Decrease the index refresh frequency.

Question 13

A company uses Einstein GPT to answer customer inquiries. To improve response relevance, the admin wants to restrict the AI's knowledge to only the company's product catalog and knowledge articles. Which approach should the admin use?

Accepted Answer

Use grounding data in the prompt template to provide relevant context. Correct: Grounding the prompt with specific data sources (Option A) restricts the AI's knowledge to the provided context (product catalog and knowledge articles). Option C (fine-tuning) adjusts model weights but does not limit knowledge in real-time. Option D (prompt engineering) without grounding does not enforce the restriction. Option B (zero-shot learning) lacks context and does not restrict knowledge.

Answer

Apply zero-shot learning without any examples

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Fine-tune the model on the company's data

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Use prompt engineering to instruct the model to only use company data

Question 14

A Salesforce admin wants to use Einstein Recommendations to suggest products. What is a key requirement for the data used to train the recommendation model?

Accepted Answer

A minimum of 1,000 user-product interactions must exist.. Einstein Recommendations requires a minimum of 1,000 user-product interactions (such as views, clicks, or purchases) to train a statistically significant collaborative filtering model. This threshold ensures the algorithm can identify meaningful patterns in user behavior and generate accurate product suggestions.

Answer

Product prices must be stored in a custom currency field.

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User profiles must include demographic data.

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Product descriptions must be at least 100 characters long.

Question 15

Which of the following is an ethical concern when using AI to make decisions about customers?

Accepted Answer

Bias in training data may lead to unfair treatment of certain customer groups. Bias in training data is a well-known ethical concern in AI, as models learn patterns from historical data that may contain societal biases. When AI is used to make decisions about customers (e.g., loan approvals, hiring, pricing), biased training data can lead to unfair treatment of certain groups, perpetuating discrimination. This is a core ethical issue because it can cause harm and violate fairness principles. Options A, C, and D are incorrect because they are either false statements or not ethical concerns.

Answer

AI models always improve over time without intervention

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AI models require no human oversight

Answer

Deep learning is too complex for business use