- A
Multi-model endpoint
Why wrong: Designed to host multiple models on one instance, not optimal for a single large model.
- B
Serverless inference
Why wrong: Serverless may introduce cold start latency and is better for intermittent traffic, not consistent low latency.
- C
Real-time endpoint with a single instance
Ensures low latency and is cost-effective for a single model with sustained traffic.
- D
Batch transform
Why wrong: Batch transform is for offline predictions, not real-time.
Question 788 of 1,000
mediumMultiple ChoiceObjective-mapped
Choosing the Right SageMaker Endpoint Type for Cost and Latency
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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 deploy a machine learning model that makes real-time predictions for a mobile app. The model is a deep neural network with a large model size (500 MB). Which SageMaker endpoint configuration is most cost-effective while meeting low-latency requirements?
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
✓
Real-time endpoint with a single instance
Option C is correct because a real-time endpoint with a single instance provides the lowest latency for a 500 MB deep neural network model, as it keeps the model loaded in memory and ready for inference without cold starts or multi-model overhead. This configuration is also cost-effective for consistent traffic patterns, as you pay for the instance uptime rather than per-invocation or for multiple model loads.
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.
- ✗
Multi-model endpoint
Why it's wrong here
Designed to host multiple models on one instance, not optimal for a single large model.
- ✗
Serverless inference
Why it's wrong here
Serverless may introduce cold start latency and is better for intermittent traffic, not consistent low latency.
- ✓
Real-time endpoint with a single instance
Why this is correct
Ensures low latency and is cost-effective for a single model with sustained traffic.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Batch transform
Why it's wrong here
Batch transform is for offline predictions, not real-time.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The MLA-C01 exam often tests the misconception that multi-model endpoints are always more cost-effective for large models, but the trap here is that multi-model endpoints introduce significant latency from disk I/O for models over 100 MB, making them unsuitable for real-time inference despite lower instance costs.
Detailed technical explanation
How to think about this question
Under the hood, a real-time SageMaker endpoint uses an Elastic Load Balancer (ELB) to distribute requests to one or more instances running the model container, with the model pre-loaded into GPU or CPU memory for sub-second inference. For a 500 MB DNN, using a single instance (e.g., ml.p3.2xlarge) avoids the overhead of model swapping seen in multi-model endpoints, where models are stored on Amazon EBS and loaded on demand, adding 100-500 ms latency per request. In a real-world scenario, a mobile app with steady traffic benefits from this setup because it minimizes jitter and ensures consistent response times under 100 ms.
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
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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.
Related practice questions
Related MLA-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
ML Model Development practice questions
Practise MLA-C01 questions linked to ML Model Development.
Data Preparation for Machine Learning practice questions
Practise MLA-C01 questions linked to Data Preparation for Machine Learning.
Deployment and Orchestration of ML Workflows practice questions
Practise MLA-C01 questions linked to Deployment and Orchestration of ML Workflows.
ML Solution Monitoring, Maintenance, and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance, and Security.
ML Solution Monitoring, Maintenance and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance and Security.
MLA-C01 fundamentals practice questions
Practise MLA-C01 questions linked to MLA-C01 fundamentals.
MLA-C01 scenario practice questions
Practise MLA-C01 questions linked to MLA-C01 scenario.
MLA-C01 troubleshooting practice questions
Practise MLA-C01 questions linked to MLA-C01 troubleshooting.
Practice this exam
Start a free MLA-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 MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Real-time endpoint with a single instance — Option C is correct because a real-time endpoint with a single instance provides the lowest latency for a 500 MB deep neural network model, as it keeps the model loaded in memory and ready for inference without cold starts or multi-model overhead. This configuration is also cost-effective for consistent traffic patterns, as you pay for the instance uptime rather than per-invocation or for multiple model loads.
What should I do if I get this MLA-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 MLA-C01 practice questions
- A team is using SageMaker Pipelines to train a model. The pipeline has multiple steps: data processing, training, evalua…
- A machine learning team deploys a custom container image for an Amazon SageMaker training job. The container needs to ac…
- A machine learning engineer sees the above error in Amazon CloudWatch Logs for a SageMaker endpoint. What is the most li…
- A data scientist has trained a model that achieves 95% accuracy on the training set but only 70% on the test set. Which…
- Refer to the exhibit. A data scientist reviews the output of a SageMaker training job. The model has 95% training accura…
- A team is using Amazon SageMaker to train a neural network. They want to minimize training time while effectively explor…
Last reviewed: Jul 4, 2026
This MLA-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 MLA-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.