- A
Use gRPC streaming for lower latency
Why wrong: gRPC is about performance, not auditability; it does not inherently provide explainability or logging.
- B
Export the model to ONNX format and run on a dedicated inference server
Why wrong: ONNX focuses on interoperability, not on logging or explainability.
- C
Deploy the model on edge devices to avoid centralised logging
Why wrong: Edge deployment makes auditing harder, not easier; regulatory compliance typically requires centralised logging.
- D
Deploy the model as a containerised microservice with REST API and log all request/response pairs
REST APIs with logging provide a clear audit trail and can integrate with explainability tools.
Question 252 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 financial institution requires that all AI model predictions be explainable and auditable for regulatory compliance. Which model serving approach should be used to meet these requirements?
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
✓
Deploy the model as a containerised microservice with REST API and log all request/response pairs
Option D is correct because logging all request/response pairs provides a complete audit trail, which is essential for regulatory compliance in financial institutions. Containerized microservices with REST APIs are stateless and can be easily integrated with centralized logging systems (e.g., ELK stack) to capture every prediction for explainability and review. This approach ensures that model decisions are transparent and can be traced back to specific inputs, satisfying both explainability and auditability requirements.
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 gRPC streaming for lower latency
Why it's wrong here
gRPC is about performance, not auditability; it does not inherently provide explainability or logging.
- ✗
Export the model to ONNX format and run on a dedicated inference server
Why it's wrong here
ONNX focuses on interoperability, not on logging or explainability.
- ✗
Deploy the model on edge devices to avoid centralised logging
Why it's wrong here
Edge deployment makes auditing harder, not easier; regulatory compliance typically requires centralised logging.
- ✓
Deploy the model as a containerised microservice with REST API and log all request/response pairs
Why this is correct
REST APIs with logging provide a clear audit trail and can integrate with explainability tools.
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 performance optimizations (like gRPC or ONNX) inherently solve compliance requirements, when in fact auditability and explainability depend on explicit logging and traceability mechanisms, not just model format or transport protocol.
Detailed technical explanation
How to think about this question
Under the hood, a containerized microservice with REST API typically uses a web framework (e.g., Flask or FastAPI) to expose a /predict endpoint. Each request and response can be captured via middleware or decorators and streamed to a centralized log aggregator (e.g., using structured logging with JSON format). In a real-world scenario, a bank deploying a credit risk model would need to replay past predictions during an audit; the logged pairs allow regulators to verify that the model's output matches the input features at the time of inference, ensuring no post-hoc manipulation.
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.
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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: Deploy the model as a containerised microservice with REST API and log all request/response pairs — Option D is correct because logging all request/response pairs provides a complete audit trail, which is essential for regulatory compliance in financial institutions. Containerized microservices with REST APIs are stateless and can be easily integrated with centralized logging systems (e.g., ELK stack) to capture every prediction for explainability and review. This approach ensures that model decisions are transparent and can be traced back to specific inputs, satisfying both explainability and auditability requirements.
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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