Question 225 of 1,672
MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company wants to use Amazon SageMaker to host a model that was trained using a custom algorithm. The model artifact is stored in Amazon S3. The company wants to ensure that the endpoint can automatically scale based on the number of incoming requests. Which configuration should the company use?
⚠ Common exam trap
Watch out — candidates often confuse SageMaker Serverless Inference with real-time endpoints, assuming serverless automatically handles all scaling needs, but they overlook the limitations of serverless (e.g., model size limits, cold starts, and concurrency caps) that make it unsuitable for many custom algorithms, especially those requiring high throughput or large artifacts.
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
✓
Create a SageMaker real-time endpoint and configure automatic scaling using a target tracking policy.
A SageMaker real-time endpoint with automatic scaling using a target tracking policy allows the endpoint to dynamically adjust the number of instances based on the incoming request load. This configuration is ideal for hosting a custom algorithm model artifact stored in S3, as it provides low-latency inference and can scale out or in based on a target metric like average CPU utilization or request count per instance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create a SageMaker multi-model endpoint with automatic scaling.
Why it's wrong here
Multi-model endpoints can auto-scale, but the question does not mention multiple models.
- ✓
Create a SageMaker real-time endpoint and configure automatic scaling using a target tracking policy.
Why this is correct
Real-time endpoints with auto-scaling adjust instance count based on load.
- ✗
Use SageMaker Serverless Inference which scales automatically.
Why it's wrong here
Serverless is a different offering, not an endpoint configuration.
- ✗
Use SageMaker Batch Transform with a scheduled job.
Why it's wrong here
Batch Transform is for batch, not real-time.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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Last reviewed: Jul 4, 2026
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services 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 MLS-C01 exam.
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