- A
Post-training quantization to INT8
This is the recommended first step for edge deployment.
- B
Knowledge distillation
Why wrong: Knowledge distillation is more complex and typically applied after initial compression.
- C
Quantization-aware training
Why wrong: This is more advanced and requires retraining; post-training quantization is simpler and often sufficient.
- D
Weight pruning
Why wrong: Pruning can help but may cause accuracy loss without retraining; quantization is usually preferred first.
Question 487 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.
A machine learning engineer is deploying a TensorFlow model on an edge device with limited memory and compute. The model needs to perform inference with low latency. The engineer has a trained float32 model. Which model compression technique should be applied first to reduce the model size and improve inference speed without significant accuracy loss?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"first"Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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 is the correct first step because it directly reduces the model size by approximately 4x (from 32-bit floats to 8-bit integers) and speeds up inference on edge devices by leveraging integer-optimized hardware (e.g., ARM NEON or Qualcomm Hexagon). This technique requires no retraining and typically yields minimal accuracy loss for most TensorFlow models, making it the fastest path to deploy on resource-constrained devices.
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.
- ✓
Post-training quantization to INT8
Why this is correct
This is the recommended first step for edge deployment.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Knowledge distillation
Why it's wrong here
Knowledge distillation is more complex and typically applied after initial compression.
- ✗
Quantization-aware training
Why it's wrong here
This is more advanced and requires retraining; post-training quantization is simpler and often sufficient.
- ✗
Weight pruning
Why it's wrong here
Pruning can help but may cause accuracy loss without retraining; quantization is usually preferred first.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that quantization-aware training is always required for INT8 deployment, but the trap here is that post-training quantization is the simplest and most effective first step for reducing model size and latency on edge devices, with quantization-aware training reserved only for cases where accuracy drops below acceptable thresholds.
Detailed technical explanation
How to think about this question
Post-training quantization works by calibrating the model on a representative dataset to determine optimal scaling factors for activations and weights, then converting float32 values to INT8 using symmetric or asymmetric quantization. A subtle behavior is that models with batch normalization layers may require folding those layers into preceding convolution weights before quantization to avoid accuracy degradation. In real-world edge deployments (e.g., TensorFlow Lite on a Raspberry Pi), INT8 quantization can reduce latency by 2-4x compared to float32, but models with very narrow dynamic ranges (e.g., some object detection models) may need quantization-aware training to maintain acceptable accuracy.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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 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 is the correct first step because it directly reduces the model size by approximately 4x (from 32-bit floats to 8-bit integers) and speeds up inference on edge devices by leveraging integer-optimized hardware (e.g., ARM NEON or Qualcomm Hexagon). This technique requires no retraining and typically yields minimal accuracy loss for most TensorFlow models, making it the fastest path to deploy on resource-constrained devices.
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.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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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