- A
Enable SageMaker Data Compression for network transfer.
Compression reduces data transfer time.
- B
Use GPU instances (e.g., p3, inf1) for faster inference.
GPUs accelerate deep learning inference.
- C
Use SageMaker Multi-Model Endpoints to serve multiple models.
Multi-model endpoints can share resources efficiently.
- D
Use SageMaker Serverless Inference to avoid managing instances.
Why wrong: Serverless has payload limits (6 MB) and cold starts.
- E
Attach Elastic Inference accelerators.
Why wrong: Elastic Inference is deprecated.
Question 520 of 1,755
Deploying Large Deep Learning Models on SageMaker Endpoints – Key Considerations
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 are valid considerations when deploying a large deep learning model (10 GB) on a SageMaker endpoint? (Choose 3.)
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 Data Compression for network transfer.
Option A is correct because SageMaker Data Compression uses HTTP compression (e.g., gzip) to reduce the payload size during network transfer between the client and endpoint, which is critical for a 10 GB model to minimize latency and bandwidth consumption. This is especially beneficial when the model is large and inference requests involve substantial input or output data.
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 Data Compression for network transfer.
Why this is correct
Compression reduces data transfer time.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use GPU instances (e.g., p3, inf1) for faster inference.
Why this is correct
GPUs accelerate deep learning inference.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use SageMaker Multi-Model Endpoints to serve multiple models.
Why this is correct
Multi-model endpoints can share resources efficiently.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use SageMaker Serverless Inference to avoid managing instances.
Why it's wrong here
Serverless has payload limits (6 MB) and cold starts.
- ✗
Attach Elastic Inference accelerators.
Why it's wrong here
Elastic Inference is deprecated.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may assume Serverless Inference or Elastic Inference can handle any model size, but both have hard limits (1 GB for Elastic Inference, 1 GB model size and 6 MB payload for Serverless) that make them invalid for a 10 GB model.
Detailed technical explanation
How to think about this question
SageMaker Data Compression works by compressing the request/response payloads using gzip at the application layer, which is transparent to the model but requires the client to set the 'Content-Encoding' header. For large models, this reduces network I/O bottlenecks, but the trade-off is increased CPU overhead on both client and endpoint for compression/decompression. In practice, this is most effective when the model outputs large tensors or when the endpoint is accessed over high-latency networks.
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 healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
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: Enable SageMaker Data Compression for network transfer. — Option A is correct because SageMaker Data Compression uses HTTP compression (e.g., gzip) to reduce the payload size during network transfer between the client and endpoint, which is critical for a 10 GB model to minimize latency and bandwidth consumption. This is especially beneficial when the model is large and inference requests involve substantial input or output data.
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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Last reviewed: Jul 4, 2026
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