- A
Use tokenization for highly predictive fields like diagnosis codes instead of masking
Tokenization replaces PHI with consistent tokens that retain the relationship between values, enabling the model to learn without exposing sensitive data. This maintains predictive performance while complying with HIPAA.
- B
Implement differential privacy within Einstein Studio
Why wrong: Differential privacy adds noise to the data or model, which could further degrade performance and is not directly available as a built-in feature in Einstein Studio.
- C
Remove masking and rely on user permissions to restrict access
Why wrong: Removing masking and relying on user permissions would expose PHI during training, violating HIPAA requirements for data-level protection.
- D
Increase the volume of training data to compensate for masking
Why wrong: Increasing training data volume does not restore the predictive value lost when fields are masked; the masked fields still lack useful information regardless of dataset size.
AI Associate Data for AI Practice Question
This AI Associate practice question tests your understanding of data for ai. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 provider implements Data Cloud to predict patient readmission rates. They have HIPAA compliance requirements. The data includes sensitive patient health information (PHI). The AI model must be trained without exposing PHI to unauthorized users. The data architect uses Data Cloud's data masking on PHI fields. However, model performance drops significantly after masking because the masked values lose predictive value. What additional step should the architect consider to maintain model performance while protecting PHI?
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
Use tokenization for highly predictive fields like diagnosis codes instead of masking
Option A is correct because tokenization replaces PHI with unique tokens that preserve the relationships between values (e.g., diagnosis codes remain distinguishable), allowing the model to learn patterns without exposing actual PHI. Unlike masking, which scrambles values and destroys predictive utility, tokenization maintains data utility while satisfying HIPAA requirements. Option C is incorrect because relying solely on user permissions does not protect against unauthorized data exposure during model training; HIPAA requires data-level protection. Option B is incorrect because Einstein Studio does not natively support differential privacy, and implementing it would add complexity without directly addressing the loss of predictive value from masking. Option D is incorrect because increasing data volume does not recover the information lost through masking.
Key principle: Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets.
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 tokenization for highly predictive fields like diagnosis codes instead of masking
Why this is correct
Tokenization replaces PHI with consistent tokens that retain the relationship between values, enabling the model to learn without exposing sensitive data. This maintains predictive performance while complying with HIPAA.
Related concept
CIDR notation defines the prefix length.
- ✗
Implement differential privacy within Einstein Studio
Why it's wrong here
Differential privacy adds noise to the data or model, which could further degrade performance and is not directly available as a built-in feature in Einstein Studio.
- ✗
Remove masking and rely on user permissions to restrict access
Why it's wrong here
Removing masking and relying on user permissions would expose PHI during training, violating HIPAA requirements for data-level protection.
- ✗
Increase the volume of training data to compensate for masking
Why it's wrong here
Increasing training data volume does not restore the predictive value lost when fields are masked; the masked fields still lack useful information regardless of dataset size.
Common exam traps
Common exam trap: usable hosts are not the same as total addresses
Subnetting questions often tempt you into counting all addresses. In normal IPv4 subnets, the network and broadcast addresses are not usable host addresses.
Detailed technical explanation
How to think about this question
Subnetting questions test whether you can identify the network, broadcast address, usable range, mask and correct subnet. Slow down enough to calculate the block size correctly.
KKey Concepts to Remember
- CIDR notation defines the prefix length.
- Block size helps identify subnet boundaries.
- Network and broadcast addresses are not usable hosts in normal IPv4 subnets.
- The required host count determines the smallest suitable subnet.
TExam Day Tips
- Write the block size before choosing the subnet.
- Check whether the question asks for hosts, subnets or a specific address range.
- Do not confuse /24, /25, /26 and /27 host counts.
Key takeaway
Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets.
Real-world example
How this comes up in practice
A network engineer segments a warehouse floor into three subnets: 20 scanners, 5 printers, and 2 management hosts. Picking the wrong mask wastes addresses or leaves too few usable hosts. Exam questions test whether you can apply CIDR notation, calculate block size, and identify the correct usable-host range for a given prefix.
What to study next
Got this wrong? Here's your next step.
Review block sizes, usable host formulas (2^n − 2), and how to find network and broadcast addresses for /24 through /30. Then practise related AI Associate subnetting questions on CIDR, address ranges, and subnet selection.
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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 — CIDR notation defines the prefix length..
What is the correct answer to this question?
The correct answer is: Use tokenization for highly predictive fields like diagnosis codes instead of masking — Option A is correct because tokenization replaces PHI with unique tokens that preserve the relationships between values (e.g., diagnosis codes remain distinguishable), allowing the model to learn patterns without exposing actual PHI. Unlike masking, which scrambles values and destroys predictive utility, tokenization maintains data utility while satisfying HIPAA requirements. Option C is incorrect because relying solely on user permissions does not protect against unauthorized data exposure during model training; HIPAA requires data-level protection. Option B is incorrect because Einstein Studio does not natively support differential privacy, and implementing it would add complexity without directly addressing the loss of predictive value from masking. Option D is incorrect because increasing data volume does not recover the information lost through masking.
What should I do if I get this AI Associate question wrong?
Review block sizes, usable host formulas (2^n − 2), and how to find network and broadcast addresses for /24 through /30. Then practise related AI Associate subnetting questions on CIDR, address ranges, and subnet selection.
What is the key concept behind this question?
CIDR notation defines the prefix length.
About these practice questions
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Last reviewed: Jun 23, 2026
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