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.
A company wants to serve a large XGBoost model that exceeds the 2GB limit for Vertex AI Prediction. What should they do?
Reduce model size by removing features
Why wrong: Reducing features may degrade model performance.
Compress the model using gzip and upload
Why wrong: Compression does not reduce the size when loaded into memory.
Deploy the model on Cloud Run Functions
Why wrong: Cloud Run Functions have a 2GB limit as well.
Use a custom container to serve the model
Custom containers have no size limit.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
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.
Detailed technical explanation
Custom containers in Vertex AI allow you to define your own serving environment using any base image (e.g., nvidia/cuda for GPU inference) and any model format (e.g., XGBoost's native model file or ONNX). The container image is stored in Artifact Registry, which has no hard size limit, and the model is loaded directly into memory from the container's filesystem. In practice, this also enables you to use model parallelism or sharding across multiple containers if the model exceeds a single node's memory.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Serving and scaling models — This question tests Serving and scaling models — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 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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