This MLS-C01 practice question tests your understanding of modeling. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 is deploying a real-time inference endpoint using SageMaker. The model is a large deep learning model (5 GB) with strict latency requirements (< 100 ms per request). The team expects bursty traffic with up to 1000 requests per second. Which configuration best meets the latency and throughput requirements?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Deploy an ml.p3.2xlarge instance with automatic scaling based on a custom metric like 'InvocationsPerInstance'
Option A is correct because deploying on an ml.p3.2xlarge instance with automatic scaling based on 'InvocationsPerInstance' allows the endpoint to handle bursty traffic up to 1000 requests per second while maintaining sub-100 ms latency. The GPU-accelerated p3 instance provides the necessary compute for a 5 GB deep learning model, and custom scaling on invocations per instance ensures that additional instances are provisioned quickly during traffic spikes without over-provisioning.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✓
Deploy an ml.p3.2xlarge instance with automatic scaling based on a custom metric like 'InvocationsPerInstance'
Why this is correct
GPU instances handle large models; automatic scaling with custom metrics provides elasticity.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a multi-model endpoint with ml.c5.4xlarge instances
Why it's wrong here
Multi-model endpoints are for many small models, not a single large model.
✗
Use SageMaker Serverless Inference with a memory size of 6 GB
Why it's wrong here
Serverless inference has cold start latency and memory limit may not be sufficient for a 5 GB model.
✗
Deploy a single ml.p3.16xlarge instance with a production variant
Why it's wrong here
A single instance is a single point of failure and may not handle bursty traffic well.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume a single large instance (like ml.p3.16xlarge) can handle high throughput, but they overlook the need for horizontal scaling to manage bursty traffic without latency degradation.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker automatic scaling with 'InvocationsPerInstance' uses a target tracking policy that adjusts the instance count based on the average number of invocations per instance, ensuring that each instance operates within its capacity. For a 5 GB model, GPU instances like p3 are critical because they leverage CUDA cores for parallel tensor operations, reducing inference time compared to CPU-only instances. In real-world scenarios, bursty traffic patterns (e.g., from a viral app) require rapid scaling; the p3.2xlarge offers a balance of cost and performance, and the scaling metric prevents throttling by preemptively adding instances when the invocation rate per instance exceeds a threshold.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Deploy an ml.p3.2xlarge instance with automatic scaling based on a custom metric like 'InvocationsPerInstance' — Option A is correct because deploying on an ml.p3.2xlarge instance with automatic scaling based on 'InvocationsPerInstance' allows the endpoint to handle bursty traffic up to 1000 requests per second while maintaining sub-100 ms latency. The GPU-accelerated p3 instance provides the necessary compute for a 5 GB deep learning model, and custom scaling on invocations per instance ensures that additional instances are provisioned quickly during traffic spikes without over-provisioning.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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