- A
Use model distillation to create a smaller student model
Why wrong: Distillation alone may not achieve the required memory reduction if the student model is still large; combining with quantization and pruning is more effective.
- B
Apply INT8 quantization and weight pruning only
Why wrong: Quantization and pruning help, but may not be sufficient alone to reduce memory from 8 GB to 2 GB without significant accuracy loss; distillation is often needed.
- C
Apply INT8 quantization, pruning, and model distillation
Combining all three techniques can achieve the necessary memory reduction while preserving accuracy.
- D
Use FP16 precision and increase batch size
Why wrong: FP16 reduces memory somewhat, but increasing batch size increases memory usage, which is counterproductive.
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 has trained a large transformer model that achieves 95% accuracy but requires 8 GB of GPU memory for inference. They need to deploy it on edge devices with only 2 GB of memory and minimal accuracy loss. Which combination of techniques should they apply?
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
Apply INT8 quantization, pruning, and model distillation
Option C is correct because the team needs to reduce the model's memory footprint from 8 GB to under 2 GB while preserving accuracy. INT8 quantization reduces memory by 4x (from 32-bit floats to 8-bit integers), weight pruning removes redundant connections, and model distillation trains a smaller student model to mimic the teacher, collectively achieving the required compression with minimal accuracy loss.
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 model distillation to create a smaller student model
Why it's wrong here
Distillation alone may not achieve the required memory reduction if the student model is still large; combining with quantization and pruning is more effective.
- ✗
Apply INT8 quantization and weight pruning only
Why it's wrong here
Quantization and pruning help, but may not be sufficient alone to reduce memory from 8 GB to 2 GB without significant accuracy loss; distillation is often needed.
- ✓
Apply INT8 quantization, pruning, and model distillation
Why this is correct
Combining all three techniques can achieve the necessary memory reduction while preserving accuracy.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use FP16 precision and increase batch size
Why it's wrong here
FP16 reduces memory somewhat, but increasing batch size increases memory usage, which is counterproductive.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that a single technique (like quantization alone) is sufficient for extreme memory reduction, when in reality the combination of distillation, quantization, and pruning is required to meet aggressive edge deployment constraints without unacceptable accuracy loss.
Detailed technical explanation
How to think about this question
Model distillation uses a temperature-scaled softmax to transfer knowledge from the teacher's logits to the student, often achieving 90%+ of the teacher's accuracy with 10x fewer parameters. INT8 quantization leverages calibration datasets to minimize quantization error, while weight pruning (e.g., magnitude-based) can remove up to 50% of weights with minimal impact when combined with retraining. In practice, deploying BERT-based models on edge devices like Raspberry Pi requires this triple approach to meet both memory and latency constraints.
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: Apply INT8 quantization, pruning, and model distillation — Option C is correct because the team needs to reduce the model's memory footprint from 8 GB to under 2 GB while preserving accuracy. INT8 quantization reduces memory by 4x (from 32-bit floats to 8-bit integers), weight pruning removes redundant connections, and model distillation trains a smaller student model to mimic the teacher, collectively achieving the required compression with minimal accuracy loss.
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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