Deploy a scikit-learn model on Vertex AI with minimum cost and high availability?
You need to deploy a scikit-leam classification model to production. The model must be able to serve requests 24/7, and you expect millions of requests per second to the production application from 8 am to 7 pm. You need to minimize the cost of deployment. What should you do?
Community Votes
100% of anonymous learners picked answer B. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
This question tests autoscaling configuration and GPU necessity for model serving; the common trap is assuming GPUs help with scikit-learn or that a single replica can handle millions of requests per second.
The correct answer is to deploy an online Vertex AI prediction endpoint with max replica count set to 100 and no GPU. Community consensus confirms that scikit-learn does not support GPU, so adding GPUs only increases cost without performance benefits.
Choosing options with GPUs (C or D) is the most common mistake, because many candidates think GPUs accelerate all ML serving. However, scikit-learn does not support GPU inference, so GPUs add cost without improving throughput.
Community Discussion (6 comments)
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Expert Analysis
Why the Answer Is Correct
Option B is correct because it provides an online Vertex AI prediction endpoint with a maximum of 100 replicas, allowing the service to autoscale to handle millions of requests per second during peak hours (8 am to 7 pm) while scaling down during off-peak times to minimize cost. Scikit-learn is a CPU-only framework; as noted in comments [2] and [3], GPU support is not available, so adding GPUs would be wasteful. Thus, a high max replica count with CPU-only instances is the best balance of scalability and cost efficiency.
Why the Other Options Are Wrong
Option A is insufficient because a max replica count of 1 means the endpoint can never scale beyond a single instance, which cannot handle millions of requests per second. Options C and D both include GPUs, which are not supported or beneficial for scikit-learn models, as confirmed by community comments [2] and [3]. Option D also adds unnecessary GPU costs with 100 replicas, while Option C has both the GPU problem and insufficient scaling capacity.
Community Comment Notes
Community comments overwhelmingly favor B, with [1] and [5] highlighting that 100 replicas are needed for massive scale, while [2] and [3] emphasize that scikit-learn has no GPU support. Comment [4] suggests that max replica count of 1 might minimize cost but ignores the throughput requirement, a point that reinforces why B is the correct choice. Taken together, the comments show that the key is to scale horizontally with CPU instances, not to depend on GPU acceleration.
Official Reference
Exam Strategy
When answering deployment questions, always check the framework's hardware support and the workload's expected traffic volume. For scikit-learn, never choose GPU-enabled endpoints, and ensure the max replica count is high enough to handle peak loads through horizontal autoscaling.
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