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PMLE Practice Question: Serve a large XGBoost model that exceeds the 2GB…
A company wants to serve a large XGBoost model that exceeds the 2GB limit for Vertex AI Prediction. What should they do?
⚠ Common exam trap
Google Cloud often tests the misconception that compression (gzip) or feature reduction can circumvent hard platform limits, when in fact the correct solution is to use a custom container that bypasses the artifact size restriction entirely.
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
✓
Use a custom container to serve the model
Vertex AI Prediction has a 2GB limit for the model artifact when using pre-built containers. A custom container bypasses this limit because you package the model and serving code into a Docker image, which can be arbitrarily large. This allows you to serve XGBoost models exceeding 2GB without size constraints imposed by the managed serving infrastructure.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce model size by removing features
Why it's wrong here
Reducing features may degrade model performance.
- ✗
Compress the model using gzip and upload
Why it's wrong here
Compression does not reduce the size when loaded into memory.
- ✗
Deploy the model on Cloud Run Functions
Why it's wrong here
Cloud Run Functions have a 2GB limit as well.
- ✓
Use a custom container to serve the model
Why this is correct
Custom containers have no size limit.
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Same concept, more angles
1 more way this is tested on PMLE
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Your team is deploying a large language model (LLM) on Vertex AI for online prediction. The model exceeds the maximum request size for Vertex AI Prediction. Which approach should you take to serve this model?
hard- A.Use Vertex AI Endpoint with a larger machine type and gRPC
- B.Use Vertex AI Batch Prediction
- C.Split the model into smaller parts and deploy multiple endpoints
- ✓ D.Deploy the model on a Compute Engine VM with a custom container and a load balancer
Why D: Vertex AI Prediction has a request size limit (1.5 MB). Using a custom container with a ModelServer (e.g., TensorFlow Serving) behind an HTTP load balancer bypasses this limit and allows large payloads.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.