- A
Use a larger instance type with more CPU/GPU
More compute power reduces per-request latency.
- B
Enable SageMaker Batch Transform to process predictions offline
Batch Transform is asynchronous and can handle large loads without affecting real-time latency.
- C
Enable data compression to reduce payload size
Smaller payloads reduce network transfer time.
- D
Use a multi-model endpoint to share instances across models
Why wrong: Multi-model endpoints optimize memory, not latency.
- E
Increase the number of instances in the endpoint
Why wrong: More instances improve throughput but not per-request latency.
Question 27 of 1,755
Reducing Inference Latency for SageMaker Endpoints
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 THREE actions can help reduce the inference latency of a SageMaker endpoint? (Choose three.)
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
✓
Use a larger instance type with more CPU/GPU
Option A is correct because using a larger instance type with more CPU or GPU resources directly increases the computational capacity available for inference. This reduces the time required to process each prediction request, thereby lowering inference latency. SageMaker endpoints scale horizontally and vertically, and vertical scaling (larger instances) is a straightforward way to improve per-request performance.
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 larger instance type with more CPU/GPU
Why this is correct
More compute power reduces per-request latency.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Enable SageMaker Batch Transform to process predictions offline
Why this is correct
Batch Transform is asynchronous and can handle large loads without affecting real-time latency.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Enable data compression to reduce payload size
Why this is correct
Smaller payloads reduce network transfer time.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a multi-model endpoint to share instances across models
Why it's wrong here
Multi-model endpoints optimize memory, not latency.
- ✗
Increase the number of instances in the endpoint
Why it's wrong here
More instances improve throughput but not per-request latency.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The MLS-C01 exam often tests the distinction between improving throughput (horizontal scaling) versus reducing latency (vertical scaling or optimization), and candidates mistakenly assume that adding more instances will speed up individual requests.
Detailed technical explanation
How to think about this question
Inference latency is primarily driven by model complexity, input size, and hardware capability. Larger instances with more powerful CPUs or GPUs reduce the time for matrix operations and tensor computations. Additionally, SageMaker endpoints use a load balancer to distribute requests, and while horizontal scaling (more instances) improves concurrency, it does not reduce the time for a single inference. Data compression (Option C) reduces network transfer time, which is a separate component of end-to-end latency, and Batch Transform (Option B) is an offline processing method that avoids real-time latency constraints entirely.
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.
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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: Use a larger instance type with more CPU/GPU — Option A is correct because using a larger instance type with more CPU or GPU resources directly increases the computational capacity available for inference. This reduces the time required to process each prediction request, thereby lowering inference latency. SageMaker endpoints scale horizontally and vertically, and vertical scaling (larger instances) is a straightforward way to improve per-request performance.
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.
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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 →
Same concept, more angles
2 more ways this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which TWO actions can help reduce inference latency for a SageMaker endpoint?
medium- A.Switch to batch transform
- ✓ B.Use SageMaker Neo to optimize the model
- C.Use a larger instance type
- D.Enable SageMaker Endpoint Cache
- E.Use a multi-model endpoint
Why B: SageMaker Neo optimizes trained models for a specific target hardware platform by compiling the model graph, fusing operations, and applying quantization and pruning techniques. This reduces the model's memory footprint and computational requirements, directly decreasing inference latency at the endpoint. Option D, 'Enable SageMaker Endpoint Cache,' is not a real SageMaker feature; there is no managed endpoint cache service. Batch transform (A) is used for offline inference, not for reducing endpoint latency. Larger instance types (C) primarily increase throughput and may not improve per-request latency. Multi-model endpoints (E) reduce model loading overhead but do not directly affect inference latency.
Variation 2. Which TWO actions can reduce inference latency for a SageMaker real-time endpoint? (Choose 2.)
medium- ✓ A.Choose a larger instance type with more compute capacity.
- B.Add more instances behind the endpoint.
- C.Use batch transform instead.
- ✓ D.Compile the model using SageMaker Neo.
- E.Switch to asynchronous inference.
Why A: Choosing a larger instance type with more compute capacity (Option A) reduces inference latency because it provides more CPU/GPU and memory resources, enabling the model to process each request faster. This directly decreases the time per inference, which is the primary driver of latency for real-time endpoints.
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Last reviewed: Jul 4, 2026
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