- A
Use a larger pre-trained model as a starting point.
Why wrong: Larger model increases size and latency.
- B
Post-training quantization to INT8.
Reduces size and latency with minimal accuracy loss.
- C
Use half-precision (FP16) instead of INT8.
Why wrong: FP16 still uses 16 bits; INT8 is more aggressive in compression.
- D
Apply weight pruning to remove small weights.
Pruning reduces model size and can improve speed on specialized hardware.
- E
Increase the number of layers in the model.
Why wrong: Increases size and latency.
Question 409 of 1,000
PMLE Scaling Prototypes into ML Models Practice Question
This PMLE practice question tests your understanding of scaling prototypes into ml models. 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 ML team is optimizing an inference model for deployment on edge devices. They need to reduce the model size and improve latency while maintaining accuracy as much as possible. Which two techniques should they use? (Choose 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
✓
Post-training quantization to INT8.
Post-training quantization to INT8 reduces model size by converting 32-bit floating-point weights and activations to 8-bit integers, which also speeds up inference on edge devices with integer-optimized hardware. This technique typically maintains accuracy within 1-2% of the original model while significantly lowering memory footprint and latency.
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 a larger pre-trained model as a starting point.
Why it's wrong here
Larger model increases size and latency.
- ✓
Post-training quantization to INT8.
Why this is correct
Reduces size and latency with minimal accuracy loss.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use half-precision (FP16) instead of INT8.
Why it's wrong here
FP16 still uses 16 bits; INT8 is more aggressive in compression.
- ✓
Apply weight pruning to remove small weights.
Why this is correct
Pruning reduces model size and can improve speed on specialized hardware.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the number of layers in the model.
Why it's wrong here
Increases size and latency.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often think that FP16 is always better than INT8 for edge devices, but INT8 offers greater size reduction and is more widely supported on edge hardware, including Google's Edge TPU.
Detailed technical explanation
How to think about this question
INT8 quantization leverages integer arithmetic, which is faster and more power-efficient on CPUs and specialized NPUs than floating-point operations. Weight pruning removes connections with near-zero magnitude, often achieving 50-90% sparsity without accuracy loss when combined with retraining, but post-training pruning alone can degrade performance if not carefully thresholded. Real-world edge deployments like mobile phones or IoT cameras commonly combine INT8 quantization with structured pruning to meet strict latency budgets under 10ms.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this PMLE question test?
Scaling Prototypes into ML Models — This question tests Scaling Prototypes into ML Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Post-training quantization to INT8. — Post-training quantization to INT8 reduces model size by converting 32-bit floating-point weights and activations to 8-bit integers, which also speeds up inference on edge devices with integer-optimized hardware. This technique typically maintains accuracy within 1-2% of the original model while significantly lowering memory footprint and latency.
What should I do if I get this PMLE 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 PMLE practice question is part of Courseiva's free Google Cloud 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 PMLE exam.
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