- A
Enable output caching for frequently requested predictions
Why wrong: Caching does not help with data deletion compliance.
- B
Validate inputs to prevent prompt injection attacks
Why wrong: Prompt injection is a security concern, not related to data deletion compliance.
- C
Use a vector database to store user embeddings
Why wrong: Storing embeddings may actually make it harder to delete user data if embeddings are mixed.
- D
Maintain the ability to delete a user's data from training sets and derived features
Directly supports the right to erasure by allowing removal of user data and any features based on it.
- E
Implement data versioning and lineage tracking
Lineage tracking allows you to identify and delete all data associated with a user.
Question 383 of 1,000
AI0-001 AI Infrastructure and Technologies Practice Question
This AI0-001 practice question tests your understanding of ai infrastructure and technologies. 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 team is deploying a model that must comply with GDPR. Users can request deletion of their data. Which TWO practices should be implemented to support this compliance? (Select TWO.)
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
✓
Maintain the ability to delete a user's data from training sets and derived features
Option D is correct because GDPR's 'right to erasure' requires that upon user request, the organization must delete not only the user's raw data but also any derived features or embeddings that were generated from that data. Without this capability, the model could still indirectly retain user information through trained parameters or feature stores, violating compliance.
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.
- ✗
Enable output caching for frequently requested predictions
Why it's wrong here
Caching does not help with data deletion compliance.
- ✗
Validate inputs to prevent prompt injection attacks
Why it's wrong here
Prompt injection is a security concern, not related to data deletion compliance.
- ✗
Use a vector database to store user embeddings
Why it's wrong here
Storing embeddings may actually make it harder to delete user data if embeddings are mixed.
- ✓
Maintain the ability to delete a user's data from training sets and derived features
Why this is correct
Directly supports the right to erasure by allowing removal of user data and any features based on it.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Implement data versioning and lineage tracking
Why this is correct
Lineage tracking allows you to identify and delete all data associated with a user.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that simply using a vector database or caching mechanism satisfies GDPR deletion requirements, when in fact the critical practice is maintaining the ability to delete user data from all derived artifacts, including training sets and feature stores.
Detailed technical explanation
How to think about this question
Under GDPR, the right to erasure (Article 17) extends to all forms of personal data, including derived features, embeddings, and model weights that have been trained on user data. Implementing data versioning and lineage tracking (Option E) allows an organization to trace which data contributed to which model version, enabling targeted retraining or removal of a user's influence from the model. In practice, this often involves techniques like machine unlearning or maintaining separate data shards that can be retracted without full model retraining.
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 practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Infrastructure and Technologies — This question tests AI Infrastructure and Technologies — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Maintain the ability to delete a user's data from training sets and derived features — Option D is correct because GDPR's 'right to erasure' requires that upon user request, the organization must delete not only the user's raw data but also any derived features or embeddings that were generated from that data. Without this capability, the model could still indirectly retain user information through trained parameters or feature stores, violating compliance.
What should I do if I get this AI0-001 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.
About these practice questions
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Last reviewed: Jul 4, 2026
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.
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