Salesforce Certified AI Associate practice questions

1. Question 1 of 25

   Which type of bias imposes a system's values on others?

   • AAssociation
   • BAutomation
   • CSocietal

   Show answer

   Correct answer: B

   How others answered

   • B70%
   • C30%

   Only 70% agreed on this answer — treat it as contested rather than settled.

   Explanation

   The correct answer is C. Societal bias. Explanation: Societal bias occurs when a system or algorithm reflects and imposes the broader societal values, stereotypes, or inequalities onto its users. This kind of bias emerges when systems are trained on data that is influenced by societal norms, prejudices, or disparities. As a result, the system may unintentionally reinforce these biases in its outcomes. Association bias refers to the bias that comes from algorithms associating certain characteristics (e.g., gender, race) with specific outcomes due to patterns in the data. Automation bias occurs when people place too much trust in automated systems, potentially leading to overreliance on these systems' decisions, even when they are flawed or incorrect. In the context of imposing values, societal bias is the type that spreads the existing societal norms and values, often without consideration for fairness or diversity.

   Adapted from community discussion by BlatantRegard · 2024-10-18 · 3 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

   Supporting references

   • trailhead.salesforce.com documentation
   • trailhead.salesforce.com documentation

   View community discussion4

   Candidate context

   Question 1 discussion

   These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by AI-ASSOCIATE or CertExamHQ.

   1. B

      BlatantRegard

      2024-10-18

      3 upvotes

      The correct answer is C. Societal bias. Explanation: Societal bias occurs when a system or algorithm reflects and imposes the broader societal values, stereotypes, or inequalities onto its users. This kind of bias emerges when systems are trained on data that is influenced by societal norms, prejudices, or disparities. As a result, the system may unintentionally reinforce these biases in its outcomes. Association bias refers to the bias that comes from algorithms associating certain characteristics (e.g., gender, race) with specific outcomes due to patterns in the data. Automation bias occurs when people place too much trust in automated systems, potentially leading to overreliance on these systems' decisions, even when they are flawed or incorrect. In the context of imposing values, societal bias is the type that spreads the existing societal norms and values, often without consideration for fairness or diversity.

   2. P

      ppremy

      2025-03-29

      Selected B1 upvote

      FROM TRAILHEAD: https://trailhead.salesforce.com/content/learn/modules/responsible-creation-of-artificial-intelligence/recognize-bias-in-ai Automation Bias: Automation bias imposes a system’s values on others. Take, for instance, a beauty contest judged by AI in 2016. The goal was to declare the most beautiful women with some notion of objectivity. But the AI in question was trained primarily on images of white women and its learned definition of "beauty" didn't include features more common in people of color. As a result, the AI chose mostly white winners, translating a bias in training data into real world outcomes.

   3. X

      Xx_Panda_xX

      2023-11-09

      Selected B3 upvotes

      B https://trailhead.salesforce.com/content/learn/modules/responsible-creation-of-artificial-intelligence/recognize-bias-in-ai

   4. S

      santo_aj

      2023-11-14

      Selected B3 upvotes

      https://trailhead.salesforce.com/content/learn/modules/responsible-creation-of-artificial-intelligence/recognize-bias-in-ai

2. Question 2 of 25

   A data quality expert at Cloud Kicks wants to ensure that each new contact contains at least an email address or phone number. Which feature should they use to accomplish this?

   • AValidation rule
   • BAutofill
   • CDuplicate matching rule

   Show answer

   Correct answer: A

   How others answered

   • A100%

   Explanation

   A. Want to make sure that phone numbers follow a particular format? Set up validation rules for any field. https://trailhead.salesforce.com/content/learn/modules/data_quality/data_quality_improve_quality?trailmix_creator_id=strailhead&trailmix_slug=prepare-for-your-salesforce-ai-associate-credential

   Adapted from community discussion by Geeblets · 2024-01-30 · 3 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

   Supporting references

   • trailhead.salesforce.com documentation

   View community discussion1

   Candidate context

   Question 2 discussion

   These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by AI-ASSOCIATE or CertExamHQ.

   1. G

      Geeblets

      2024-01-30

      Selected A3 upvotes

      A. Want to make sure that phone numbers follow a particular format? Set up validation rules for any field. https://trailhead.salesforce.com/content/learn/modules/data_quality/data_quality_improve_quality?trailmix_creator_id=strailhead&trailmix_slug=prepare-for-your-salesforce-ai-associate-credential

3. Question 3 of 25

   In the context of Salesforce's Trusted AI Principles, what does the principle of Empowerment primarily aim to achieve?

   • AEmpower users of all skill levels to build AI applications with clicks, not code.
   • BEmpower users to solve challenging technical problems using neural networks.
   • CEmpower users to contribute to the growing body of knowledge of leading AI research.

   Show answer

   Correct answer: A

   How others answered

   • A100%

   Explanation

   A. "We strive to abstract away the complexity of AI to make it possible for people of all technical skill levels -- not only advanced data scientists -- to build AI applications with just clicks, not code." https://blog.salesforceairesearch.com/meet-salesforces-trusted-ai-principles/#empowering

   Adapted from community discussion by Geeblets · 2024-07-29 · 3 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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   Question 3 discussion

   These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by AI-ASSOCIATE or CertExamHQ.

   1. G

      Geeblets

      2024-07-29

      Selected A3 upvotes

      A. "We strive to abstract away the complexity of AI to make it possible for people of all technical skill levels -- not only advanced data scientists -- to build AI applications with just clicks, not code." https://blog.salesforceairesearch.com/meet-salesforces-trusted-ai-principles/#empowering

   2. M

      MonBouj

      2024-05-30

      Selected A2 upvotes

      A. Empower users of all skill levels to build AI applications with clicks, not code.

4. Question 4 of 25

   Cloud Kicks wants to use an AI model to predict the demand for shoes using historical data on sales and regional characteristics. What is an essential data quality dimension to achieve this goal?

   • AAge
   • BReliability
   • CVolume

   Show answer

   Correct answer: A

   How others answered

   • A69%
   • B31%

   Only 69% agreed on this answer — treat it as contested rather than settled.

   Explanation

   I asked this question to chatGPT and here is the reply The correct answer is B. Reliability. For Cloud Kicks to use an AI model to accurately predict shoe demand, reliable data is essential. Reliability refers to the consistency and accuracy of the data, ensuring that the historical sales data and regional characteristics are trustworthy and can lead to accurate predictions. Here’s why the other options are less relevant: A. Age: While the age or recency of data can be important, it is not the most essential data quality dimension in this case. Older data can still be useful if it's reliable and relevant. C. Volume: Having a large amount of data (volume) is helpful for AI models, but without reliability, even large datasets can lead to poor predictions. Thus, reliable data is crucial to ensure the AI model makes accurate and consistent predictions.

   Adapted from community discussion by Dinesh_Khandelwal · 2024-09-19 · 2 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

   Supporting references

   • trailhead.salesforce.com documentation

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   Question 4 discussion

   These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by AI-ASSOCIATE or CertExamHQ.

   1. G

      Geeblets

      2024-01-30

      Selected A11 upvotes

      Data quality dimensions: Age, Completeness, Accuracy, Consistency, Duplication, Usage. https://trailhead.salesforce.com/content/learn/modules/data_quality/data_quality_assess_your_data?trailmix_creator_id=strailhead&trailmix_slug=prepare-for-your-salesforce-ai-associate-credential

   2. D

      docxdmd

      2024-04-17

      4 upvotes

      A is the correct and only choice that is a "Data quality dimension". I get how B seems like it would be correct but this question isn't asking you to make up your own "data quality dimensions" its asking out of the 6 (Age, Completeness, Accuracy, Consistency, Duplication, Usage) which is appropriate here. Please stop confidently writing the wrong answer (B) with no explanation and no evidence.

   3. D

      Dinesh_Khandelwal

      2024-09-19

      2 upvotes

      I asked this question to chatGPT and here is the reply The correct answer is B. Reliability. For Cloud Kicks to use an AI model to accurately predict shoe demand, reliable data is essential. Reliability refers to the consistency and accuracy of the data, ensuring that the historical sales data and regional characteristics are trustworthy and can lead to accurate predictions. Here’s why the other options are less relevant: A. Age: While the age or recency of data can be important, it is not the most essential data quality dimension in this case. Older data can still be useful if it's reliable and relevant. C. Volume: Having a large amount of data (volume) is helpful for AI models, but without reliability, even large datasets can lead to poor predictions. Thus, reliable data is crucial to ensure the AI model makes accurate and consistent predictions.

   4. S

      SHAAZ10

      2024-12-30

      2 upvotes

      When aiming to predict shoe demand using historical sales data and regional characteristics, ensuring reliable data is essential. Reliability in data quality refers to the degree to which data is accurate, consistent, and trustworthy, directly impacting the effectiveness of AI models. Inaccurate or inconsistent data can lead to flawed predictions, adversely affecting decision-making processes. While volume (the amount of data) and age (the recency of data) are important considerations, they are secondary to reliability. A large dataset is beneficial only if the data is reliable; similarly, recent data is valuable only when its accuracy and consistency are ensured. Therefore, among the options provided, A. Reliability is the most critical data quality dimension to focus on for accurately predicting shoe demand.

5. Question 5 of 25

   A financial institution plans a campaign for preapproved credit cards. How should they implement Salesforce's Trusted AI Principle of Transparency?

   • ACommunicate how risk factors such as credit score can impact customer eligibility.
   • BFlag sensitive variables and their proxies to prevent discriminatory lending practices.
   • CIncorporate customer feedback into the model’s continuous training.

   Show answer

   Correct answer: A

   How others answered

   • A68%
   • B32%

   Only 68% agreed on this answer — treat it as contested rather than settled.

   Explanation

   A. Transparency includes not only how we build our models but also why they made the prediction or recommendation they did. https://blog.salesforceairesearch.com/meet-salesforces-trusted-ai-principles/#transparent-1

   Adapted from community discussion by Geeblets · 2024-01-30 · 10 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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   Question 5 discussion

   These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by AI-ASSOCIATE or CertExamHQ.

   1. G

      Geeblets

      2024-01-30

      Selected A10 upvotes

      A. Transparency includes not only how we build our models but also why they made the prediction or recommendation they did. https://blog.salesforceairesearch.com/meet-salesforces-trusted-ai-principles/#transparent-1

   2. S

      shiowbah

      2024-01-03

      7 upvotes

      A. Communicate how risk factors such as credit score can impact customer eligibility.

   3. T

      test123_emailtupmailcom

      2024-09-12

      Selected A1 upvote

      It's common sense that Transparency relates to clear communication of how things are done. So letter A is the only one that applies in this context.

6. Question 6 of 25

   Which best describes the difference between predictive AI and generative AI?

   • APredictive AI and generative AI have the same capabilities but differ in the type of input they receive; predictive AI receives raw data whereas generative AI receives natural language.
   • BPredictive AI uses machine learning to classify or predict outputs from its input data whereas generative AI does not use machine learning to generate its output.
   • CPredictive AI uses machine learning to classify or predict outputs from its input data whereas generative AI uses machine leaning to generate new and original output for a given input.

   Show answer

   Correct answer: C

   How others answered

   • C100%

   Explanation

   C. At their foundation, both generative AI and predictive AI use machine learning. However, generative AI turns machine learning inputs into content, whereas predictive AI uses machine learning to determine the future and boost positive outcomes by using data to better understand market trends. https://www.eweek.com/artificial-intelligence/generative-ai-vs-predictive-ai/

   Adapted from community discussion by Geeblets · 2024-07-30 · 3 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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   Question 6 discussion

   These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by AI-ASSOCIATE or CertExamHQ.

   1. G

      Geeblets

      2024-07-30

      Selected C3 upvotes

      C. At their foundation, both generative AI and predictive AI use machine learning. However, generative AI turns machine learning inputs into content, whereas predictive AI uses machine learning to determine the future and boost positive outcomes by using data to better understand market trends. https://www.eweek.com/artificial-intelligence/generative-ai-vs-predictive-ai/

   2. C

      Codeneo

      2024-12-30

      Selected C1 upvote

      C. Predictive AI uses machine learning to classify or predict outputs from its input data whereas generative AI uses machine leaning to generate new and original output for a given input.

   3. T

      the_var

      2024-10-23

      1 upvote

      Point to ponder: Does Generative AI generate original output (everytime?) for a given input?

7. Question 7 of 25

   What is a potential source of bias in training data for AI models?

   • AThe data is collected in real time from source systems.
   • BThe data is collected from a diverse range of sources and demographics.
   • CThe data is skewed toward a particular demographic or source.

   Show answer

   Correct answer: C

   How others answered

   • C100%

   Supporting references

   • trailhead.salesforce.com documentation

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   Candidate context

   Question 7 discussion

   These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by AI-ASSOCIATE or CertExamHQ.

   1. G

      Geeblets

      2024-07-30

      Selected C1 upvote

      Types of AI model biases Association, Confirmation, Automation, Societal, Survival, Interaction https://trailhead.salesforce.com/content/learn/modules/responsible-creation-of-artificial-intelligence/recognize-bias-in-ai

   2. C

      Cody2305

      2025-02-05

      Selected C1 upvote

      C. The data is skewed toward a particular demographic or source.

8. Question 8 of 25

   What can bias in AI algorithms in CRM lead to?

   • AEthical challenges in CRM systems
   • BAdvertising cost increases
   • CPersonalization and targeted marketing changes

   Show answer

   Correct answer: A

   How others answered

   • A75%
   • C25%

   Supporting references

   • trailhead.salesforce.com documentation

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   Candidate context

   Question 8 discussion

   These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by AI-ASSOCIATE or CertExamHQ.

   1. G

      Geeblets

      2024-07-30

      Selected A2 upvotes

      A - ethical challenges https://trailhead.salesforce.com/content/learn/modules/responsible-creation-of-artificial-intelligence/recognize-bias-in-ai

9. Question 9 of 25

   Which Einstein capability uses emails to create content for Knowledge articles?

   • APredict
   • BDiscover
   • CGenerate

   Show answer

   Correct answer: C

   How others answered

   • C100%

   Explanation

   Discover: insights that bring new clarity about your company’s customers. Predict: outcomes so your users can make decisions with confidence. Recommend: the best actions to make the most out of every engagement. Automate: routine tasks so your users can focus on customer success. Generate: tailored content - from emails, to knowledge articles, to code.

   Adapted from community discussion by Geeblets · 2024-07-30 · 4 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

   Supporting references

   • trailhead.salesforce.com documentation

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   Candidate context

   Question 9 discussion

   These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by AI-ASSOCIATE or CertExamHQ.

   1. G

      Geeblets

      2024-07-30

      Selected C4 upvotes

      Discover: insights that bring new clarity about your company’s customers. Predict: outcomes so your users can make decisions with confidence. Recommend: the best actions to make the most out of every engagement. Automate: routine tasks so your users can focus on customer success. Generate: tailored content - from emails, to knowledge articles, to code.

   2. X

      Xinowi

      2024-07-29

      Selected C1 upvote

      C "Generate tailored content - from emails, to knowledge articles, to code" https://trailhead.salesforce.com/content/learn/modules/get_smart_einstein_feat/get_smart_einstein_feat_basics

10. Question 10 of 25

    A system admin recognizes the need to put a data management strategy in place. What is a key component of a data management strategy?

    • ANaming Convention
    • BColor Coding
    • CData Backup

    Show answer

    Correct answer: C

    How others answered

    • C83%
    • A17%

    Supporting references

    • Salesforce Help

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    Candidate context

    Question 10 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by AI-ASSOCIATE or CertExamHQ.

    1. L

       Luca1991

       2023-12-13

       8 upvotes

       C. Data Backup -> https://help.salesforce.com/s/articleView?id=000393223&type=1

    2. S

       shelend

       2025-01-12

       Selected C1 upvote

       C.Data Backup - A data management strategy is essential to ensure that data is stored, accessed, and protected effectively. While naming conventions and color coding can help organize data, data backup is a critical component because it ensures that data is recoverable in case of accidental deletion, hardware failure, or other data loss scenarios. Backup strategies typically involve regular, automated backups and secure storage of backup data, either on-site, off-site, or in the cloud.

    3. A

       ALPM

       2024-11-06

       1 upvote

       A. Naming Convention Naming conventions are crucial in a data management strategy as they provide a consistent and organized way to identify and categorize data. This helps in ensuring data is easily accessible, understandable, and maintainable across the organization.

11. Question 11 of 25

    What is an example of ethical debt?

    • AViolating a data privacy law and failing to pay fines
    • BDelaying an AI product launch to retrain an AI data model
    • CLaunching an AI feature after discovering a harmful bias

    Show answer

    Correct answer: C

    How others answered

    • C100%

    Explanation

    https://www.salesforceairesearch.com/static/ethics/EthicalAIMaturityModel.pdf “Ethical debt is accrued when you launch features that violate your ethical AI principles because, for example, you didn’t do a bias assessment, or you didn’t mitigate the bias that was found”

    Adapted from community discussion by Xinowi · 2024-07-29 · 6 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    Question 11 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by AI-ASSOCIATE or CertExamHQ.

    1. X

       Xinowi

       2024-07-29

       Selected C6 upvotes

       https://www.salesforceairesearch.com/static/ethics/EthicalAIMaturityModel.pdf “Ethical debt is accrued when you launch features that violate your ethical AI principles because, for example, you didn’t do a bias assessment, or you didn’t mitigate the bias that was found”

    2. S

       SANLE

       2025-02-14

       Selected C1 upvote

       An AI feature launched even after discovering a harmful bias.

12. Question 12 of 25

    A consultant conducts a series of Consequence Scanning Workshops to support testing diverse datasets. Which Salesforce Trusted AI Principle is being practiced?

    • AAccountability
    • BInclusivity
    • CTransparency

    Show answer

    Correct answer: B

    How others answered

    • B100%

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    Question 12 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by AI-ASSOCIATE or CertExamHQ.

    1. G

       Geeblets

       2024-07-30

       Selected B2 upvotes

       B. Keywords: Consequence, diverse https://www.salesforce.com/eu/blog/meet-salesforces-trusted-ai-principles/

    2. M

       Mark518

       2024-08-12

       2 upvotes

       Scratch that it's B per the SF Ethical AI Practice Maturity Model :)

    3. M

       Mark518

       2024-08-10

       1 upvote

       The answer is Accountable: We believe in holding ourselves accountable to our customers, partners, and society. We will seek independent feedback for continuous improvement of our practice and policies and work to mitigate harm to customers and consumers.

13. Question 13 of 25

    A marketing manager wants to use AI to better engage their customers. Which functionality provides the best solution?

    • ABring Your Own Model
    • BJourney Optimization
    • CEinstein Engagement

    Show answer

    Correct answer: C

    How others answered

    • C100%

    Supporting references

    • Salesforce Help

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    Question 13 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by AI-ASSOCIATE or CertExamHQ.

    1. N

       NaireZalez

       2024-09-01

       Selected C2 upvotes

       https://help.salesforce.com/s/articleView?id=sf.mc_anb_einstein_engagement_scoring.htm&type=5

14. Question 14 of 25

    Which action should be taken to develop and implement trusted generative AI with Salesforce's safety guideline in mind?

    • ABe transparent when AI has created and autonomously delivered content.
    • BDevelop right-sized models to reduce our carbon footprint.
    • CCreate guardrails that mitigate toxicity and protect PII.

    Show answer

    Correct answer: C

    Explanation

    C Safety: As with all of our AI models, we should make every effort to mitigate bias, toxicity, and harmful output by conducting bias, explainability, and robustness assessments, and red teaming. We must also protect the privacy of any personally identifying information (PII) present in the data used for training and create guardrails to prevent additional harm (e.g., force publishing code to a sandbox rather than automatically pushing to production). https://www.salesforce.com/news/stories/generative-ai-guidelines/

    Adapted from community discussion by Xinowi · 2024-07-29 · 2 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    Question 14 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by AI-ASSOCIATE or CertExamHQ.

    1. X

       Xinowi

       2024-07-29

       2 upvotes

       C Safety: As with all of our AI models, we should make every effort to mitigate bias, toxicity, and harmful output by conducting bias, explainability, and robustness assessments, and red teaming. We must also protect the privacy of any personally identifying information (PII) present in the data used for training and create guardrails to prevent additional harm (e.g., force publishing code to a sandbox rather than automatically pushing to production). https://www.salesforce.com/news/stories/generative-ai-guidelines/

15. Question 15 of 25

    Cloud Kicks learns of complaints from customers who are receiving too many sales calls and emails. Which data quality dimension should be assessed to reduce these communication inefficiencies?

    • ADuplication
    • BConsent
    • CUsage

    Show answer

    Correct answer: A

    How others answered

    • A75%
    • B25%

    Explanation

    A. Duplication "duplication can lead to communication inefficiencies if customers receive multiple calls or emails from different sources for the same purpose."

    Adapted from community discussion by oski990 · 2024-07-13 · 2 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    Question 15 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by AI-ASSOCIATE or CertExamHQ.

    1. O

       oski990

       2024-07-13

       Selected A2 upvotes

       A. Duplication "duplication can lead to communication inefficiencies if customers receive multiple calls or emails from different sources for the same purpose."

    2. N

       NaireZalez

       2024-09-01

       Selected A1 upvote

       Duplication, also since Consent is not a data quality dimension.

16. Question 16 of 25

    What is the most likely impact that high-quality data will have on customer relationships?

    • AImproved customer trust and satisfaction
    • BIncreased brand loyalty
    • CHigher customer acquisition costs

    Show answer

    Correct answer: A

    How others answered

    • A100%

    Explanation

    High-quality data ensures that AI models are accurate, unbiased, and robust in their predictions. It allows these models to better predict real-world scenarios and fosters trustworthiness in their applications. From an economic perspective, using high-quality data can maximize the return on investment in AI initiatives. https://trailhead.salesforce.com/content/learn/modules/data_quality/data_quality_getting_started?trailmix_creator_id=strailhead&trailmix_slug=prepare-for-your-salesforce-ai-associate-credential

    Adapted from community discussion by Geeblets · 2024-01-29 · 2 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

    Supporting references

    • trailhead.salesforce.com documentation

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    Question 16 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by AI-ASSOCIATE or CertExamHQ.

    1. G

       Geeblets

       2024-01-29

       2 upvotes

       High-quality data ensures that AI models are accurate, unbiased, and robust in their predictions. It allows these models to better predict real-world scenarios and fosters trustworthiness in their applications. From an economic perspective, using high-quality data can maximize the return on investment in AI initiatives. https://trailhead.salesforce.com/content/learn/modules/data_quality/data_quality_getting_started?trailmix_creator_id=strailhead&trailmix_slug=prepare-for-your-salesforce-ai-associate-credential

17. Question 17 of 25

    What is a potential outcome of using poor-quality data in AI applications?

    • AAI models may produce biased or erroneous results.
    • BAI models become more interpretable.
    • CAI model training becomes slower and less efficient.

    Show answer

    Correct answer: A

    How others answered

    • A100%

    Supporting references

    • trailhead.salesforce.com documentation

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    Question 17 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by AI-ASSOCIATE or CertExamHQ.

    1. G

       Geeblets

       2024-01-30

       Selected A1 upvote

       A. "AI can magnify bias" https://trailhead.salesforce.com/content/learn/modules/responsible-creation-of-artificial-intelligence/recognize-bias-in-ai

18. Question 18 of 25

    Which data does Salesforce automatically exclude from Marketing Cloud Einstein engagement model training to mitigate bias and ethical risks?

    • ACryptographic
    • BGeographic
    • CDemographic

    Show answer

    Correct answer: C

    How others answered

    • C100%

    Supporting references

    • Salesforce Help

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    Candidate context

    Question 18 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by AI-ASSOCIATE or CertExamHQ.

    1. S

       sabrina70612

       2024-05-28

       Selected C3 upvotes

       To avoid bias and other ethical risks, the Einstein Email Engagement Scoring model doesn't include demographic data.

    2. S

       sabrina70612

       2024-05-28

       2 upvotes

       the answer should be C see https://help.salesforce.com/s/articleView?id=sf.mc_anb_EES_email_model_card.htm&language=en_US&type=5#:~:text=Review%20the%20ethical%20factors%20associated,doesn't%20include%20demographic%20data.

19. Question 19 of 25

    Which statement exemplifies Salesforce's honesty guideline when training AI models?

    • AControl bias, toxicity, and harmful content with embedded guardrails and guidance.
    • BEnsure appropriate consent and transparency when using AI- generated responses.
    • CMinimize the AI model's carbon footprint and environmental impact during training.

    Show answer

    Correct answer: B

    How others answered

    • B80%
    • A20%

    Explanation

    Correct answer is B: Honesty: When collecting data to train and evaluate our models, we need to respect data provenance and ensure that we have consent to use data (e.g., open-source, user-provided). https://www.salesforce.com/news/stories/generative-ai-guidelines/

    Adapted from community discussion by pao2310 · 2024-04-29 · 3 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    Question 19 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by AI-ASSOCIATE or CertExamHQ.

    1. P

       pao2310

       2024-04-29

       3 upvotes

       Correct answer is B: Honesty: When collecting data to train and evaluate our models, we need to respect data provenance and ensure that we have consent to use data (e.g., open-source, user-provided). https://www.salesforce.com/news/stories/generative-ai-guidelines/

    2. A

       Adarsha_Shah

       2024-10-28

       Selected B2 upvotes

       https://www.salesforce.com/news/stories/generative-ai-guidelines/

20. Question 20 of 25

    Cloud Kicks wants to create a custom service analytics application to analyze cases in Salesforce. The application should rely on accurate data to ensure efficient case resolution. Which data quality dimension is essential for this custom application?

    • AAge
    • BDuplication
    • CConsistency

    Show answer

    Correct answer: A

    How others answered

    • A60%
    • C40%

    Only 60% agreed on this answer — treat it as contested rather than settled.

    Explanation

    Age is a data quality dimension that is essential for the custom service analytics application to analyze cases in Salesforce. Age refers to how recent the data is and how well it reflects the current situation. For the custom service analytics application, the data should be as fresh and up-to-date as possible, as older data may not capture the latest status, progress, or resolution of the cases. Using outdated data can lead to inaccurate or misleading analysis and insights, and affect the efficiency and effectiveness of the case resolution.

    Adapted from community discussion by ivo100 · 2024-08-08 · 2 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    Question 20 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by AI-ASSOCIATE or CertExamHQ.

    1. I

       ivo100

       2024-08-08

       Selected A2 upvotes

       Age is a data quality dimension that is essential for the custom service analytics application to analyze cases in Salesforce. Age refers to how recent the data is and how well it reflects the current situation. For the custom service analytics application, the data should be as fresh and up-to-date as possible, as older data may not capture the latest status, progress, or resolution of the cases. Using outdated data can lead to inaccurate or misleading analysis and insights, and affect the efficiency and effectiveness of the case resolution.

21. Question 21 of 25

    What are the key components of the data quality standard?

    • AAccuracy, Completeness, Consistency
    • BReviewing, Updating, Archiving
    • CNaming, Formatting, Monitoring

    Show answer

    Correct answer: A

    How others answered

    • A60%
    • C40%

    Only 60% agreed on this answer — treat it as contested rather than settled.

    Supporting references

    • trailhead.salesforce.com documentation
    • trailhead.salesforce.com documentation

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    Question 21 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by AI-ASSOCIATE or CertExamHQ.

    1. G

       Geeblets

       2024-01-29

       1 upvote

       Bad data (https://trailhead.salesforce.com/content/learn/modules/data_quality/data_quality_getting_started?trailmix_creator_id=strailhead&trailmix_slug=prepare-for-your-salesforce-ai-associate-credential) Missing Records, Duplicate Records, No Data Standards, Incomplete Records, Stale Data Data Quality Dimension (https://trailhead.salesforce.com/content/learn/modules/data_quality/data_quality_assess_your_data?trailmix_creator_id=strailhead&trailmix_slug=prepare-for-your-salesforce-ai-associate-credential) Age, Completeness, Accuracy, Consistency, Duplicate, Usage

    2. V

       vijay_sv01

       2025-03-09

       Selected A1 upvote

       Age, Completeness, Accuracy, Consistency, Duplication, Usage these are the parameters Age: How long ago data was updated Completeness: Data is complete Accuracy: It's Trusted Consistency: Say for ex: one field has DDMMYY in many places and in other places MMDDYY Duplication: Data Cleansing Usage: Harnessing in Reports, Dashboards

22. Question 22 of 25

    A customer using Einstein Prediction Builder is confused about why a certain prediction was made. Following Salesforce's Trusted AI Principle of Transparency, which customer information should be accessible on the Salesforce Platform?

    • AAn explanation of how Prediction Builder works and a link to Salesforce's Trusted AI Principles
    • BA marketing article of the product that clearly outlines the product’s capabilities and features
    • CAn explanation of the prediction's rationale and a model card that describes how the model was created

    Show answer

    Correct answer: C

    How others answered

    • C100%

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    Question 22 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by AI-ASSOCIATE or CertExamHQ.

    1. U

       uchiken

       2024-09-13

       Selected C1 upvote

       C. An explanation of the prediction's rationale and a model card that describes how the model was created

    2. R

       RoseJacome

       2025-01-30

       1 upvote

       c. An explanation of the predictions rationale and a model card that describes how the model was created

23. Question 23 of 25

    Which type of bias results from data being labeled according to stereotypes?

    • ASocietal
    • BAssociation
    • CInteraction

    Show answer

    Correct answer: B

    How others answered

    • B75%
    • A25%

    Supporting references

    • trailhead.salesforce.com documentation

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    Candidate context

    Question 23 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by AI-ASSOCIATE or CertExamHQ.

    1. T

       TenteCambero

       2024-03-14

       Selected B3 upvotes

       https://trailhead.salesforce.com/es/content/learn/modules/responsible-creation-of-artificial-intelligence/recognize-bias-in-ai

    2. T

       TenteCambero

       2024-03-14

       3 upvotes

       B: Labeling data based on stereotypes is an example of association bias https://trailhead.salesforce.com/es/content/learn/modules/responsible-creation-of-artificial-intelligence/recognize-bias-in-ai

24. Question 24 of 25

    Cloud Kicks implements a new product recommendation feature for its shoppers that recommends shoes of a given color to display to customers based on the color of the products from their purchase history. Which type of bias is most likely to be encountered in this scenario?

    • ASurvivorship
    • BSocietal
    • CConfirmation

    Show answer

    Correct answer: C

    How others answered

    • C67%
    • A33%

    Only 67% agreed on this answer — treat it as contested rather than settled.

    Explanation

    Confirmation bias labels data based on preconceived ideas. The recommendations you see when you shop online reflect your purchasing habits, but the data influencing those purchases already reflect what people see and choose to buy in the first place. You can see how recommendation systems reinforce stereotypes. If superheroes don’t appear on a website’s ‘toys for girls” section, a shopper is unlikely to know they’re elsewhere on the site, much less purchase them.

    Adapted from community discussion by csew · 2024-12-30 · 4 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    Question 24 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by AI-ASSOCIATE or CertExamHQ.

    1. C

       csew

       2024-12-30

       Selected C4 upvotes

       Confirmation bias labels data based on preconceived ideas. The recommendations you see when you shop online reflect your purchasing habits, but the data influencing those purchases already reflect what people see and choose to buy in the first place. You can see how recommendation systems reinforce stereotypes. If superheroes don’t appear on a website’s ‘toys for girls” section, a shopper is unlikely to know they’re elsewhere on the site, much less purchase them.

25. Question 25 of 25

    A business analyst (BA) wants to improve business by enhancing their sales processes and customer support. Which AI applications should the BA use to meet their needs?

    • ASales data cleansing and customer support data governance
    • BMachine learning models and chatbot predictions
    • CLead scoring, opportunity forecasting, and case classification

    Show answer

    Correct answer: C

    How others answered

    • C100%

    Supporting references

    • trailhead.salesforce.com documentation
    • trailhead.salesforce.com documentation

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    Question 25 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by AI-ASSOCIATE or CertExamHQ.

    1. G

       Geeblets

       2024-07-29

       Selected C3 upvotes

       C. https://trailhead.salesforce.com/content/learn/modules/artificial-intelligence-for-business?trailmix_creator_id=strailhead&trailmix_slug=prepare-for-your-salesforce-ai-associate-credential and https://trailhead.salesforce.com/content/learn/modules/artificial-intelligence-for-customer-service?trailmix_creator_id=strailhead&trailmix_slug=prepare-for-your-salesforce-ai-associate-credential

    2. M

       MonBouj

       2024-05-30

       Selected C2 upvotes

       C. Lead scoring, opportunity forecasting, and case classification

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AI-ASSOCIATE practice, question 1 of 25