- A
Quantization-aware training (QAT)
Why wrong: QAT is more accurate but requires retraining the model, which is more effort than post-training quantization.
- B
Weight pruning
Pruning removes near-zero weights, reducing model size. It can be done post-training with minimal accuracy loss.
- C
Knowledge distillation
Why wrong: Distillation trains a new smaller model, which is more complex and not a direct compression of the existing model.
- D
Post-training float16 quantization
Float16 quantization reduces model size by half and is well-supported in TensorFlow Lite.
- E
Increasing model depth
Why wrong: Increasing depth would enlarge the model, opposite of compression.
Question 660 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 company is deploying a computer vision model on edge devices using TensorFlow Lite. They want to reduce model size without significant accuracy loss. Which TWO model compression techniques are most suitable?
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
✓
Weight pruning
Weight pruning (B) is suitable because it removes redundant connections (weights) from the neural network, reducing the model size and computational cost while often preserving accuracy if done gradually. Post-training float16 quantization (D) converts model weights from float32 to float16, halving the storage size with minimal accuracy loss, and is directly supported by TensorFlow Lite for edge deployment.
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.
- ✗
Quantization-aware training (QAT)
Why it's wrong here
QAT is more accurate but requires retraining the model, which is more effort than post-training quantization.
- ✓
Weight pruning
Why this is correct
Pruning removes near-zero weights, reducing model size. It can be done post-training with minimal accuracy loss.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Knowledge distillation
Why it's wrong here
Distillation trains a new smaller model, which is more complex and not a direct compression of the existing model.
- ✓
Post-training float16 quantization
Why this is correct
Float16 quantization reduces model size by half and is well-supported in TensorFlow Lite.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increasing model depth
Why it's wrong here
Increasing depth would enlarge the model, opposite of compression.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between techniques that directly reduce model size (pruning, quantization) versus those that improve accuracy or create new models (QAT, knowledge distillation), leading candidates to select QAT as a compression method when it is actually a training-time optimization.
Detailed technical explanation
How to think about this question
Weight pruning typically uses magnitude-based pruning, where weights below a threshold (e.g., 0.01) are set to zero, and the model is retrained to recover accuracy; TensorFlow Lite supports structured pruning for hardware efficiency. Post-training float16 quantization works by converting weights to half-precision, which reduces memory bandwidth and storage by 50%, but on edge devices with float16 support, it can also speed up inference without retraining. A subtle behavior is that pruning can create sparse matrices that require specialized hardware or software for speedups, while float16 quantization may cause slight accuracy drops in models with very small weight ranges.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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: Weight pruning — Weight pruning (B) is suitable because it removes redundant connections (weights) from the neural network, reducing the model size and computational cost while often preserving accuracy if done gradually. Post-training float16 quantization (D) converts model weights from float32 to float16, halving the storage size with minimal accuracy loss, and is directly supported by TensorFlow Lite for edge deployment.
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.
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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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