- A
Use a multi-model endpoint
Why wrong: Multi-model endpoints may increase latency due to model loading overhead.
- B
Add Elastic Inference
Why wrong: Elastic Inference adds GPU acceleration but may not reduce latency as effectively as upgrading the instance.
- C
Enable SageMaker Batch Transform
Why wrong: Batch Transform is for asynchronous batch processing, not real-time.
- D
Switch to a larger instance type
Correct: Larger instances provide more CPU/GPU for faster inferences.
Question 767 of 1,000
hardMultiple ChoiceObjective-mapped
Lower Inference Latency with a Larger SageMaker Instance
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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.
A team is deploying a model that requires low-latency inference for real-time predictions. They are using a SageMaker endpoint with a single instance. During testing, they observe high latency. Which change would most effectively reduce latency?
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
✓
Switch to a larger instance type
Switching to a larger instance type (Option D) directly increases the compute and memory resources available to the SageMaker endpoint, which reduces inference latency by allowing the model to process requests faster. Since the team is using a single instance, scaling up is the most straightforward way to handle the computational load and meet real-time latency requirements.
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.
- ✗
Use a multi-model endpoint
Why it's wrong here
Multi-model endpoints may increase latency due to model loading overhead.
- ✗
Add Elastic Inference
Why it's wrong here
Elastic Inference adds GPU acceleration but may not reduce latency as effectively as upgrading the instance.
- ✗
Enable SageMaker Batch Transform
Why it's wrong here
Batch Transform is for asynchronous batch processing, not real-time.
- ✓
Switch to a larger instance type
Why this is correct
Correct: Larger instances provide more CPU/GPU for faster inferences.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse scaling up (larger instance) with scaling out (multiple instances) or assume that Elastic Inference always reduces latency, but Elastic Inference adds network latency and is better for cost savings on large models, not for minimizing per-request latency.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker endpoints use a synchronous HTTP/HTTPS API where each request is processed by the model container; a larger instance type (e.g., moving from ml.m5.large to ml.m5.xlarge) doubles the vCPU and memory, reducing CPU queue depth and memory swapping. In real-world scenarios, if the model is compute-bound (e.g., a large ensemble or deep neural network), scaling up is often more effective than scaling out because it avoids the overhead of load balancing and inter-instance communication.
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.
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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: Switch to a larger instance type — Switching to a larger instance type (Option D) directly increases the compute and memory resources available to the SageMaker endpoint, which reduces inference latency by allowing the model to process requests faster. Since the team is using a single instance, scaling up is the most straightforward way to handle the computational load and meet real-time latency requirements.
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.
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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.
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