- A
Using open-source algorithms.
Why wrong: Open-source does not ensure transparency of decision processes.
- B
Providing user-facing explanations.
Explanations help users understand how decisions are made.
- C
Collecting sensitive demographic data.
Why wrong: Collecting such data may violate privacy.
- D
Allowing users to opt out of AI processing.
Why wrong: Opt-out is about user autonomy, not transparency.
- E
Documenting model decisions.
Documentation supports auditability and transparency.
Quick Answer
The answer is documenting model decisions and providing user-facing explanations. These two practices create a clear audit trail and allow users to understand how an AI system reached a specific outcome, which is the core of AI transparency. Documenting decisions ensures accountability by making the model’s logic traceable, while user-facing explanations bridge the gap between complex algorithms and human comprehension. On the Salesforce AI Associate exam, this question tests your grasp of transparency as distinct from open-source licensing or privacy controls—a common trap is confusing open-source code with transparency about a specific model’s decisions, which are unrelated. Remember that transparency is about the “why” behind a result, not the code itself. A useful memory tip: think “audit and explain” as the two pillars of transparency, and avoid options that sound ethical but address privacy or user control instead.
AI Associate Ethical Considerations of AI Practice Question
This AI Associate practice question tests your understanding of ethical considerations of ai. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which TWO practices contribute to ethical AI transparency?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Providing user-facing explanations.
Options A and C are correct. Documenting model decisions provides an audit trail, and providing user-facing explanations helps users understand AI behavior. Option B is wrong because open-source algorithms do not guarantee transparency about specific model decisions. Option D is wrong because collecting sensitive data may violate privacy principles. Option E is wrong because opt-out is about user control, not transparency.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Using open-source algorithms.
Why it's wrong here
Open-source does not ensure transparency of decision processes.
- ✓
Providing user-facing explanations.
Why this is correct
Explanations help users understand how decisions are made.
Related concept
Static NAT maps one inside address to one outside address.
- ✗
Collecting sensitive demographic data.
Why it's wrong here
Collecting such data may violate privacy.
- ✗
Allowing users to opt out of AI processing.
Why it's wrong here
Opt-out is about user autonomy, not transparency.
- ✓
Documenting model decisions.
Why this is correct
Documentation supports auditability and transparency.
Related concept
Static NAT maps one inside address to one outside address.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
How this comes up in practice
A small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
What to study next
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI Associate NAT questions on configuration and troubleshooting.
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Ethical Considerations of AI — study guide chapter
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Ethical Considerations of AI practice questions
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FAQ
Questions learners often ask
What does this AI Associate question test?
Ethical Considerations of AI — This question tests Ethical Considerations of AI — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Providing user-facing explanations. — Options A and C are correct. Documenting model decisions provides an audit trail, and providing user-facing explanations helps users understand AI behavior. Option B is wrong because open-source algorithms do not guarantee transparency about specific model decisions. Option D is wrong because collecting sensitive data may violate privacy principles. Option E is wrong because opt-out is about user control, not transparency.
What should I do if I get this AI Associate question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI Associate NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
2 more ways this is tested on AI Associate
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company uses AI to monitor employee productivity. Employees feel surveilled. What ethical principle is being violated?
medium- A.Inclusion
- B.Accountability
- C.Safety
- ✓ D.Transparency
Why D: Option A is correct because transparency requires informing employees about AI monitoring. Option B is wrong accountability relates to responsibility for actions. Option C is wrong safety relates to harm prevention. Option D is wrong inclusion relates to diversity and belonging.
Variation 2. To comply with Salesforce's AI ethics principles when using Einstein Bots, which two practices should be implemented?
medium- ✓ A.Allow users to escalate to a human agent.
- B.Use the bot to make all customer decisions autonomously.
- C.Store all conversation transcripts indefinitely.
- ✓ D.Disclose that the user is interacting with a bot.
- E.Minimize data collection to only what is necessary.
Why A: Disclosing bot identity (transparency) and allowing human escalation (accountability) are key ethical practices.
Last reviewed: Jun 23, 2026
This AI Associate practice question is part of Courseiva's free Salesforce certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI Associate exam.
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