Question 1,241 of 1,672
Minimizing Cost for SageMaker Real-Time Inference
A data scientist is using Amazon SageMaker to deploy a model for real-time inference. The endpoint receives a large number of requests with variable traffic patterns. The team wants to minimize cost while ensuring low latency. Which THREE actions should the team take? (Choose THREE.)
Quick Answer
This 'choose three' question is built around variable traffic, and the three correct actions each reduce cost through a different mechanism suited to that variability rather than to steady, predictable load. A multi-model endpoint lets multiple models share the resources of the same underlying instances instead of each model requiring its own dedicated endpoint, which cuts cost whenever you're not running just one model in isolation. Enabling auto-scaling based on invocation count is the piece specifically aimed at variable traffic: instead of provisioning for peak load at all times, capacity expands automatically when invocations rise and contracts when they fall, so the team pays for compute roughly in proportion to actual demand rather than a fixed worst-case amount. Choosing a smaller instance type for the production variant lowers the baseline per-instance cost directly, which matters because auto-scaling still multiplies whatever the per-instance cost is, so starting from a smaller, right-sized instance keeps even the scaled-up cost lower than it would be with an oversized instance. The rejected options fail to specifically target cost under variability: setting a fixed variant weight and instance count is a traffic-distribution mechanism, not a cost-reduction one, and a single large, statically-provisioned instance is a poor fit for variable traffic because it's sized for the peak yet paid for continuously. Whenever a scenario pairs variable traffic with a goal of minimizing cost while keeping latency low, expect the answer to combine auto-scaling, appropriately-sized instances, and resource-sharing options like multi-model endpoints rather than static, fixed-capacity choices.
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 multi-model endpoint to host multiple models on the same instance.
Options A, B, and E are correct. Option A: Using a multi-model endpoint allows multiple models to be hosted on the same instance, reducing costs by sharing resources. Option B: Enabling auto-scaling based on invocation count dynamically adjusts capacity to match variable traffic patterns, minimizing cost while maintaining low latency. Option E: Creating a production variant with a smaller instance type reduces per-instance cost. Option C is incorrect because setting the initial variant weight to 1 and increasing the number of instances does not directly minimize cost; it is a traffic distribution strategy. Option D is incorrect because a single large instance may be over-provisioned for variable traffic, leading to higher costs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use a multi-model endpoint to host multiple models on the same instance.
Why this is correct
Multi-model endpoints reduce cost by sharing resources.
- ✓
Enable auto-scaling for the endpoint based on the invocation count.
Why this is correct
Auto-scaling adjusts to demand.
- ✗
Set the initial variant weight to 1 and increase the number of instances.
Why it's wrong here
More instances increase cost.
- ✗
Use a single large instance to handle all traffic.
Why it's wrong here
Large instance may be underutilized.
- ✓
Create a production variant with a smaller instance type.
Why this is correct
Smaller instances cost less.
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Same concept, more angles
1 more way this is tested on MLS-C01
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. A data scientist is deploying a model using Amazon SageMaker. The model endpoint needs to handle real-time inference requests with low latency. The model is a large ensemble of 10 deep learning models, each approximately 500 MB. What is the most cost-effective deployment strategy that meets the low-latency requirement?
easy- A.Deploy each model to a separate endpoint and use a load balancer.
- B.Use a single endpoint with multiple instances behind it.
- C.Use a SageMaker batch transform job to process inference requests in batches.
- ✓ D.Use a SageMaker multi-model endpoint to host all models on one or more instances.
Why D: A SageMaker multi-model endpoint (MME) allows hosting multiple models on a single or few instances, dynamically loading them from Amazon S3 into memory as needed. This is the most cost-effective option for a large ensemble of 500 MB models because it avoids the expense of separate endpoints or multiple instances per model, while still supporting low-latency real-time inference by keeping frequently used models cached.
Last reviewed: Jun 20, 2026
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