AI-ASSOCIATE practice questions 21–30

1. Question 21 · 78 in bank

   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.

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   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/

   Source: Adapted from community discussion — Xinowi

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2. Question 22 · 78 in bank

   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

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   Answer: A

   Explanation

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

   Source: Adapted from community discussion — oski990

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3. Question 23 · 78 in bank

   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

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   Answer: A

   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

   Source: Adapted from community discussion — Geeblets

   References

   • trailhead.salesforce.com documentation

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4. Question 24 · 78 in bank

   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.

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   Answer: A

   References

   • trailhead.salesforce.com documentation

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5. Question 25 · 78 in bank

   What role does data quality play in the ethical use of AI applications?

   • AHigh-quality data is essential for ensuring unbiased and fair AI decisions, promoting ethical use, and preventing discrimination.
   • BLow-quality data reduces the risk of unintended bias as the data is not overfitted to demographic groups.
   • CHigh-quality data ensures the presence of demographic attributes required for personalized campaigns.

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   Answer: A

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6. Question 26 · 78 in bank

   Cloud Kicks wants to decrease the workload for its customer care agents by implementing a chatbot on its website that partially deflects incoming cases by answering frequently asked questions. Which field of AI is most suitable for this scenario?

   • ANatural language processing
   • BPredictive analytics
   • CComputer vision

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   Answer: A

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7. Question 27 · 78 in bank

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

   • ACryptographic
   • BGeographic
   • CDemographic

   Show answer

   Answer: C

   References

   • Salesforce Help

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8. Question 28 · 78 in bank

   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

   Answer: B

   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/

   Source: Adapted from community discussion — pao2310

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9. Question 29 · 78 in bank

   What are some of the ethical challenges associated with AI development?

   • AImplicit transparency of AI systems, which makes it easy for users to understand and trust their decisions
   • BPotential for human bias in machine learning algorithms and the lack of transparency in AI decision-making processes
   • CInherent neutrality of AI systems, which eliminates any potential for human bias in decision-making

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   Answer: B

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10. Question 30 · 78 in bank

    What is one way to achieve transparency in AI?

    • AAllow users to give feedback regarding the inferences the AI makes about them.
    • BEstablish an ethical and unbiased culture amongst those involved.
    • CCommunicate AI goals and objectives with those involved prior to all interactions.

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    Answer: A

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AI-ASSOCIATE practice, question 21. 78 questions available.