- A
GPU requirement
Serverless inference does not support GPU instances.
- B
Inference traffic pattern (intermittent vs steady)
Serverless is cost-effective for intermittent traffic; real-time endpoints are for steady traffic.
- C
Integration with AWS Lambda
Why wrong: Both can be invoked via Lambda; not a deciding factor.
- D
Availability of built-in algorithms
Why wrong: Both support built-in and custom containers.
- E
Model size in GB
Why wrong: Both have size limits; serverless has a 6 GB memory limit, but model size is always a factor.
Question 1,164 of 1,755
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
Which TWO factors should be considered when choosing between Amazon SageMaker's real-time endpoints and serverless inference? (Select TWO.)
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
✓
GPU requirement
GPU requirement is a key factor because SageMaker real-time endpoints support GPU-based instances (e.g., ml.p3, ml.g4dn) for low-latency inference on deep learning models, while serverless inference only supports CPU instances. If your model requires GPU acceleration for acceptable latency, you must choose a real-time endpoint.
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.
- ✓
GPU requirement
Why this is correct
Serverless inference does not support GPU instances.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Inference traffic pattern (intermittent vs steady)
Why this is correct
Serverless is cost-effective for intermittent traffic; real-time endpoints are for steady traffic.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Integration with AWS Lambda
Why it's wrong here
Both can be invoked via Lambda; not a deciding factor.
- ✗
Availability of built-in algorithms
Why it's wrong here
Both support built-in and custom containers.
- ✗
Model size in GB
Why it's wrong here
Both have size limits; serverless has a 6 GB memory limit, but model size is always a factor.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often mistakenly think serverless inference cannot handle large models or lacks Lambda integration, but the real differentiators are GPU support and traffic pattern suitability.
Detailed technical explanation
How to think about this question
Serverless inference auto-scales to zero when not in use and is billed per invocation with a cold start latency of several seconds, making it ideal for intermittent or bursty traffic. Real-time endpoints maintain persistent instances and are billed per hour, providing sub-second latency for steady traffic. Under the hood, serverless inference uses AWS-managed infrastructure with a maximum concurrency of 200 per endpoint, while real-time endpoints allow fine-grained control over instance type, count, and auto-scaling policies.
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.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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.
- →
Machine Learning Implementation and Operations — study guide chapter
Learn the concepts, then practise the questions
- →
Machine Learning Implementation and Operations practice questions
Targeted practice on this topic area only
- →
All MLS-C01 questions
1,755 questions across all exam domains
- →
AWS Certified Machine Learning Specialty MLS-C01 study guide
Full concept coverage aligned to exam objectives
- →
MLS-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related MLS-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Data Engineering practice questions
Practise MLS-C01 questions linked to Data Engineering.
Machine Learning Implementation and Operations practice questions
Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.
Modeling practice questions
Practise MLS-C01 questions linked to Modeling.
Exploratory Data Analysis practice questions
Practise MLS-C01 questions linked to Exploratory Data Analysis.
MLS-C01 fundamentals practice questions
Practise MLS-C01 questions linked to MLS-C01 fundamentals.
MLS-C01 scenario practice questions
Practise MLS-C01 questions linked to MLS-C01 scenario.
MLS-C01 troubleshooting practice questions
Practise MLS-C01 questions linked to MLS-C01 troubleshooting.
Practice this exam
Start a free MLS-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this MLS-C01 question test?
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: GPU requirement — GPU requirement is a key factor because SageMaker real-time endpoints support GPU-based instances (e.g., ml.p3, ml.g4dn) for low-latency inference on deep learning models, while serverless inference only supports CPU instances. If your model requires GPU acceleration for acceptable latency, you must choose a real-time endpoint.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
More MLS-C01 practice questions
- A company needs to transfer 10 TB of data from an on-premises data center to Amazon S3. The network bandwidth is limited…
- A company is using Amazon Kinesis Data Streams to ingest real-time clickstream data. The data is consumed by a Lambda fu…
- A team is building a data pipeline to process terabytes of log data daily using Amazon EMR. The data arrives in 5-minute…
- A data science team is building a real-time fraud detection system. Transactions are streamed via Amazon Kinesis Data St…
- A company uses Amazon SageMaker to train and deploy machine learning models. The training data is stored in Amazon S3 (P…
- A data engineering team is designing a data lake on AWS for machine learning workloads. The data includes structured, se…
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.