- A
Export the model to TensorFlow Lite and use Vertex AI Edge Manager for deployment.
Correct. Edge Manager handles deployment to devices with TFLite or ONNX models.
- B
Use Cloud Run for on-device inference.
Why wrong: Cloud Run is cloud-based, not for edge devices.
- C
Deploy the model to a Vertex AI endpoint and rely on mobile connectivity.
Why wrong: Requires online connectivity which may not be available.
- D
Use AI Platform Prediction (not Vertex AI).
Why wrong: AI Platform is legacy and does not provide edge management.
Question 653 of 1,000
PMLE Serving and Scaling Models Practice Question
This PMLE practice question tests your understanding of serving and scaling 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.
You need to serve a model on an edge device with low latency and offline capability. Which approach should you use?
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
✓
Export the model to TensorFlow Lite and use Vertex AI Edge Manager for deployment.
TensorFlow Lite is specifically designed for on-device inference with low latency and offline capability, converting models into a lightweight format optimized for edge hardware. Vertex AI Edge Manager extends this by providing deployment, monitoring, and management of models on edge devices, ensuring they run efficiently without constant cloud connectivity.
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.
- ✓
Export the model to TensorFlow Lite and use Vertex AI Edge Manager for deployment.
Why this is correct
Correct. Edge Manager handles deployment to devices with TFLite or ONNX models.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Cloud Run for on-device inference.
Why it's wrong here
Cloud Run is cloud-based, not for edge devices.
- ✗
Deploy the model to a Vertex AI endpoint and rely on mobile connectivity.
Why it's wrong here
Requires online connectivity which may not be available.
- ✗
Use AI Platform Prediction (not Vertex AI).
Why it's wrong here
AI Platform is legacy and does not provide edge management.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between cloud-based inference services (like Vertex AI endpoints or Cloud Run) and edge-optimized solutions (like TensorFlow Lite with Edge Manager), trapping candidates who assume any Google Cloud ML service can be deployed offline.
Detailed technical explanation
How to think about this question
TensorFlow Lite uses quantization (e.g., post-training integer quantization) to reduce model size and accelerate inference on CPUs, GPUs, and specialized hardware like Edge TPUs, often achieving sub-10ms latency. Vertex AI Edge Manager supports over-the-air model updates and can bundle models with device-specific runtime optimizations, enabling consistent performance across heterogeneous edge devices. In a real-world scenario like a manufacturing plant with intermittent connectivity, this approach allows defect detection models to run locally on cameras, with results synced only when a connection is available.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Serving and Scaling Models — study guide chapter
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FAQ
Questions learners often ask
What does this PMLE question test?
Serving and Scaling Models — This question tests Serving and Scaling Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Export the model to TensorFlow Lite and use Vertex AI Edge Manager for deployment. — TensorFlow Lite is specifically designed for on-device inference with low latency and offline capability, converting models into a lightweight format optimized for edge hardware. Vertex AI Edge Manager extends this by providing deployment, monitoring, and management of models on edge devices, ensuring they run efficiently without constant cloud connectivity.
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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