- A
Quantisation to INT8
Reduces model size by reducing precision of weights.
- B
Transfer learning from a larger model
Why wrong: Transfer learning does not reduce model size; it may retain the same architecture.
- C
Knowledge distillation
Trains a smaller student model to mimic larger teacher, reducing size significantly.
- D
Increasing model capacity with more layers
Why wrong: Opposite of compression.
- E
Pruning of redundant connections
Removes weights that contribute little, reducing storage.
Question 247 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.
Your team is deploying a large model on edge devices and needs to reduce its size by 80% while maintaining reasonable accuracy. Which THREE techniques should they consider? (Choose 3.)
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
✓
Quantisation to INT8
Quantisation to INT8 reduces the precision of model weights and activations from 32-bit floating point to 8-bit integers, cutting memory usage by approximately 75% (4x compression). This directly addresses the 80% size reduction target while often preserving accuracy within 1-2% through careful calibration and scaling, making it a primary technique 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.
- ✓
Quantisation to INT8
Why this is correct
Reduces model size by reducing precision of weights.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Transfer learning from a larger model
Why it's wrong here
Transfer learning does not reduce model size; it may retain the same architecture.
- ✓
Knowledge distillation
Why this is correct
Trains a smaller student model to mimic larger teacher, reducing size significantly.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increasing model capacity with more layers
Why it's wrong here
Opposite of compression.
- ✓
Pruning of redundant connections
Why this is correct
Removes weights that contribute little, reducing storage.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that transfer learning reduces model size, when in fact it only transfers learned features and does not compress the model; candidates may confuse it with knowledge distillation.
Detailed technical explanation
How to think about this question
Quantisation to INT8 uses techniques like symmetric or asymmetric scaling with zero-point offsets to map FP32 values into the INT8 range (-128 to 127 or 0-255). During inference, operations like matrix multiplication are performed using integer arithmetic, which is significantly faster on edge hardware (e.g., ARM NEON or Qualcomm Hexagon) and reduces power consumption. Pruning removes redundant connections by setting weights below a threshold to zero, often achieving 50-90% sparsity without accuracy loss when combined with retraining. Knowledge distillation trains a smaller student model to mimic the output probabilities (logits) of a larger teacher model, transferring dark knowledge via temperature scaling, enabling the student to achieve near-teacher accuracy at a fraction of the size.
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: Quantisation to INT8 — Quantisation to INT8 reduces the precision of model weights and activations from 32-bit floating point to 8-bit integers, cutting memory usage by approximately 75% (4x compression). This directly addresses the 80% size reduction target while often preserving accuracy within 1-2% through careful calibration and scaling, making it a primary technique 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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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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