- A
Use only aggregated data that does not include any patient-level details.
Why wrong: Aggregation may lose granularity needed for readmission prediction; de-identification of patient-level data is preferred.
- B
De-identify all protected health information (PHI) by removing or masking identifiers.
De-identification ensures compliance with HIPAA and protects patient privacy, allowing safe use of data for AI.
- C
Obtain written patient consent for every record used in training.
Why wrong: While consent is important, HIPAA allows use of de-identified data without consent; obtaining consent for each record is impractical.
- D
Store the data in a separate, encrypted environment with access controls.
Why wrong: While security measures are important, they do not remove PHI; de-identification is the primary requirement.
AI Associate Data for AI Practice Question
This AI Associate practice question tests your understanding of data for 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.
A healthcare organization uses Salesforce to develop an AI model for patient readmission prediction. They must comply with HIPAA regulations. The dataset includes patient names, addresses, medical record numbers, and detailed clinical notes. The data scientist plans to train a supervised model using historical readmission outcomes. What is the most important data governance step before model training?
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
De-identify all protected health information (PHI) by removing or masking identifiers.
Option B is correct because HIPAA mandates that protected health information (PHI) must be de-identified before it can be used for model training without patient authorization. Removing or masking identifiers such as names, addresses, and medical record numbers ensures the dataset no longer contains individually identifiable information, allowing the organization to comply with the HIPAA Privacy Rule while still using clinical notes for predictive modeling.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use only aggregated data that does not include any patient-level details.
Why it's wrong here
Aggregation may lose granularity needed for readmission prediction; de-identification of patient-level data is preferred.
- ✓
De-identify all protected health information (PHI) by removing or masking identifiers.
Why this is correct
De-identification ensures compliance with HIPAA and protects patient privacy, allowing safe use of data for AI.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Obtain written patient consent for every record used in training.
Why it's wrong here
While consent is important, HIPAA allows use of de-identified data without consent; obtaining consent for each record is impractical.
- ✗
Store the data in a separate, encrypted environment with access controls.
Why it's wrong here
While security measures are important, they do not remove PHI; de-identification is the primary requirement.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the misconception that security controls like encryption or access controls alone satisfy HIPAA compliance, when in fact de-identification is the primary requirement for using PHI in AI model training without patient consent.
Detailed technical explanation
How to think about this question
Under the hood, HIPAA's Safe Harbor method requires the removal of 18 specific identifiers, including names, geographic subdivisions smaller than a state, dates (except year), phone numbers, and medical record numbers. De-identification can also be achieved via the Expert Determination method, where a qualified statistician certifies that the risk of re-identification is very small. In practice, clinical notes often contain free-text PHI, so natural language processing (NLP) tools are used to detect and mask identifiers before training begins.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A junior network technician can log in to a core router but cannot reach the enable prompt or configuration mode. The AAA server is authenticating the login — but the authorisation policy only grants privilege level 1, not 15. Authentication (who you are) is working; authorisation (what you can do) is not.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this AI Associate question test?
Data for AI — This question tests Data for AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: De-identify all protected health information (PHI) by removing or masking identifiers. — Option B is correct because HIPAA mandates that protected health information (PHI) must be de-identified before it can be used for model training without patient authorization. Removing or masking identifiers such as names, addresses, and medical record numbers ensures the dataset no longer contains individually identifiable information, allowing the organization to comply with the HIPAA Privacy Rule while still using clinical notes for predictive modeling.
What should I do if I get this AI Associate question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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