This PDE practice question tests your understanding of operationalizing machine learning 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 trains a custom model using TensorFlow and wants to deploy it to Vertex AI for low-latency predictions. The model is large (2 GB). Which deployment option should they choose?
Use Vertex AI Batch Prediction job
Why wrong: Batch is not for real-time.
Deploy as a Cloud Function
Why wrong: Cloud Functions have memory limits.
Deploy to Vertex AI Endpoint with a custom container
Custom containers allow large models.
Deploy to Cloud Run with minimum instances
Why wrong: Cloud Run also has memory limits.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Deploy to Vertex AI Endpoint with a custom container
Option C is correct because deploying a large (2 GB) model to Vertex AI Endpoint with a custom container allows you to package the model, its dependencies, and a serving framework (e.g., TensorFlow Serving) into a Docker image. This approach supports low-latency predictions by keeping the model loaded in memory across requests, and it can scale to handle real-time inference traffic, unlike batch or serverless options that have cold-start or size limitations.
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.
Use Vertex AI Batch Prediction job
Why it's wrong here
Batch is not for real-time.
Deploy as a Cloud Function
Why it's wrong here
Cloud Functions have memory limits.
Deploy to Vertex AI Endpoint with a custom container
Why this is correct
Custom containers allow large models.
Related concept
Read the scenario before looking for a memorised answer.
Deploy to Cloud Run with minimum instances
Why it's wrong here
Cloud Run also has memory limits.
Common exam traps
Google Cloud often tests the misconception that Cloud Run or Cloud Functions can handle large models for real-time inference, ignoring their size limits, cold-start latency, and lack of native Vertex AI integration for model management and scaling.
Detailed technical explanation
Vertex AI Endpoint with a custom container leverages TensorFlow Serving's gRPC or REST APIs to load the model into memory once and serve predictions with sub-100ms latency. Under the hood, the endpoint uses autoscaling based on CPU/memory utilization, and the custom container can be optimized with model quantization or XLA compilation to reduce inference time. In real-world scenarios, a 2 GB model (e.g., a large BERT variant) would require GPU acceleration, which Vertex AI Endpoint supports via custom containers with NVIDIA drivers and CUDA libraries.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
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FAQ
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
The correct answer is: Deploy to Vertex AI Endpoint with a custom container — Option C is correct because deploying a large (2 GB) model to Vertex AI Endpoint with a custom container allows you to package the model, its dependencies, and a serving framework (e.g., TensorFlow Serving) into a Docker image. This approach supports low-latency predictions by keeping the model loaded in memory across requests, and it can scale to handle real-time inference traffic, unlike batch or serverless options that have cold-start or size limitations.
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 PDE 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 PDE exam.
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