- A
Increase batch size
Why wrong: Batch size affects throughput but not the latency of a single inference request.
- B
Add more layers
Why wrong: Adding layers deepens the network and increases inference time.
- C
Use a larger model
Why wrong: A larger model would increase computational requirements and latency.
- D
Quantization
Quantization reduces model size and speeds up inference by using lower-precision arithmetic.
Question 558 of 1,000
AI0-001 AI Models and Data Engineering Practice Question
This AI0-001 practice question tests your understanding of ai models and data engineering. 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.
An AI model is deployed to a mobile app with limited computational resources. The model is a deep neural network with high latency. Which technique is best to reduce inference time?
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
✓
Quantization
Quantization reduces the precision of the model's weights and activations (e.g., from 32-bit floating point to 8-bit integer), which decreases memory footprint and speeds up computation on resource-constrained devices like mobile phones. This directly lowers inference latency without requiring additional hardware or architectural changes.
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.
- ✗
Increase batch size
Why it's wrong here
Batch size affects throughput but not the latency of a single inference request.
- ✗
Add more layers
Why it's wrong here
Adding layers deepens the network and increases inference time.
- ✗
Use a larger model
Why it's wrong here
A larger model would increase computational requirements and latency.
- ✓
Quantization
Why this is correct
Quantization reduces model size and speeds up inference by using lower-precision arithmetic.
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 increasing batch size or model size improves performance on edge devices, when in fact these techniques increase resource demands and latency in low-resource environments.
Detailed technical explanation
How to think about this question
Quantization maps floating-point values to a smaller integer range, often using techniques like uniform affine quantization where scale and zero-point parameters are calibrated from a representative dataset. In practice, post-training quantization can reduce model size by 4x and achieve 2-4x speedups on ARM CPUs and NPUs, though it may introduce minor accuracy loss that can be mitigated with quantization-aware training.
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 Models and Data Engineering — This question tests AI Models and Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Quantization — Quantization reduces the precision of the model's weights and activations (e.g., from 32-bit floating point to 8-bit integer), which decreases memory footprint and speeds up computation on resource-constrained devices like mobile phones. This directly lowers inference latency without requiring additional hardware or architectural changes.
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.
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Last reviewed: Jul 4, 2026
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