- A
Enable SageMaker Neo to compile the model.
Neo optimizes models for target hardware, reducing latency.
- B
Increase the batch size for inference.
Why wrong: Larger batch sizes increase latency per request, though throughput may improve.
- C
Use GPU instances for inference.
GPUs accelerate deep learning inference.
- D
Reduce the input data size (e.g., lower resolution images).
Smaller inputs reduce computation time.
- E
Use a multi-model endpoint to share the instance.
Why wrong: Multi-model endpoints can add latency when loading models.
Question 78 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 THREE measures can help reduce inference latency for a deep learning model deployed on SageMaker real-time endpoints? (Select 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
✓
Enable SageMaker Neo to compile the model.
A is correct because SageMaker Neo compiles the trained model into an optimized binary for the target hardware (e.g., CPU, GPU, or Inferentia), using Apache TVM to fuse operations and prune unused computations. This reduces inference latency by up to 2x without requiring code changes, making it a direct latency-reduction measure for real-time endpoints.
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.
- ✓
Enable SageMaker Neo to compile the model.
Why this is correct
Neo optimizes models for target hardware, reducing latency.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the batch size for inference.
Why it's wrong here
Larger batch sizes increase latency per request, though throughput may improve.
- ✓
Use GPU instances for inference.
Why this is correct
GPUs accelerate deep learning inference.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Reduce the input data size (e.g., lower resolution images).
Why this is correct
Smaller inputs reduce computation time.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a multi-model endpoint to share the instance.
Why it's wrong here
Multi-model endpoints can add latency when loading models.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The MLS-C01 exam often tests the misconception that increasing batch size always reduces latency, but for real-time endpoints, larger batches increase per-request processing time, making it a throughput optimization, not a latency reduction technique.
Detailed technical explanation
How to think about this question
SageMaker Neo leverages Apache TVM to perform graph-level optimizations (e.g., operator fusion, constant folding) and target-specific code generation (e.g., Intel MKL-DNN for CPUs, NVIDIA TensorRT for GPUs). In real-world scenarios, a model compiled with Neo can see latency drop from 50ms to 20ms on a CPU instance, enabling cost-effective deployment without GPU hardware. The compilation is hardware-specific, so you must specify the target instance type (e.g., ml.c5.xlarge) during compilation.
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.
- →
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: Enable SageMaker Neo to compile the model. — A is correct because SageMaker Neo compiles the trained model into an optimized binary for the target hardware (e.g., CPU, GPU, or Inferentia), using Apache TVM to fuse operations and prune unused computations. This reduces inference latency by up to 2x without requiring code changes, making it a direct latency-reduction measure for real-time endpoints.
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.