The correct choice is to create a SageMaker real-time endpoint and configure automatic scaling using a target tracking policy. This works because SageMaker real-time endpoints integrate with Application Auto Scaling to dynamically adjust instance count based on a target metric, such as the number of incoming requests per instance, ensuring the endpoint handles traffic spikes without manual intervention. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of scaling strategies for hosted models—distinguishing real-time endpoints with auto-scaling from alternatives like Multi-Model endpoints (which serve multiple models but still require scaling configuration) or Serverless Inference (which auto-scales but may have cold-start and payload limits). A common trap is choosing Batch Transform for real-time needs, but that is for offline inference only. Memory tip: think “real-time + target tracking = automatic scaling for live traffic.”
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 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?
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.
SageMaker real-time endpoints support automatic scaling using Application Auto Scaling, which can adjust the number of instances based on metrics like request count. Multi-model endpoints (A) are for serving multiple models. Batch Transform (B) is for offline inference. Serverless Inference (D) scales automatically but has limitations; the question asks for endpoint scaling, and real-time endpoints with auto-scaling is the standard approach.
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.
✗
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.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Create a SageMaker real-time endpoint and configure automatic scaling using a target tracking policy. — SageMaker real-time endpoints support automatic scaling using Application Auto Scaling, which can adjust the number of instances based on metrics like request count. Multi-model endpoints (A) are for serving multiple models. Batch Transform (B) is for offline inference. Serverless Inference (D) scales automatically but has limitations; the question asks for endpoint scaling, and real-time endpoints with auto-scaling is the standard approach.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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.
Question Discussion
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