- A
Batch transform
Why wrong: Batch transform is suitable for offline processing of large datasets, but it processes all data at once and is not event-driven.
- B
Asynchronous inference
Asynchronous inference handles large payloads via S3 and processes them within minutes, with cost-effective scaling.
- C
Serverless inference
Why wrong: Serverless inference also has payload limits and timeout constraints unsuitable for large video processing.
- D
Real-time endpoint
Why wrong: Real-time endpoints have a 6.5 MB payload limit and are optimized for low latency, not large files.
Question 937 of 1,000
MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 wants to deploy a model that performs inference on large video files (up to 2 GB each) uploaded to an S3 bucket. The inference can tolerate a few minutes of latency. Which SageMaker inference option is most cost-effective?
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
✓
Asynchronous inference
Asynchronous inference is the most cost-effective option for large video files (up to 2 GB) with a tolerance for a few minutes of latency because it queues incoming requests, processes them in the background, and automatically scales down to zero when idle, eliminating the cost of idle compute. It supports payloads up to 1 GB natively and can handle larger files via S3 input, making it ideal for this workload without requiring a continuously running endpoint.
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.
- ✗
Batch transform
Why it's wrong here
Batch transform is suitable for offline processing of large datasets, but it processes all data at once and is not event-driven.
- ✓
Asynchronous inference
Why this is correct
Asynchronous inference handles large payloads via S3 and processes them within minutes, with cost-effective scaling.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Serverless inference
Why it's wrong here
Serverless inference also has payload limits and timeout constraints unsuitable for large video processing.
- ✗
Real-time endpoint
Why it's wrong here
Real-time endpoints have a 6.5 MB payload limit and are optimized for low latency, not large files.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the payload size and timeout limits of Serverless inference (6 MB, 60 seconds) versus Asynchronous inference (1 GB, 15 minutes default timeout) to trick candidates into choosing Serverless for large files, ignoring its hard constraints.
Detailed technical explanation
How to think about this question
Asynchronous inference uses an internal SQS queue to buffer requests and supports payloads up to 1 GB via direct invocation, with larger files handled by passing an S3 URI as input. The endpoint scales down to zero instances after a configurable idle period (default 300 seconds), and you only pay for the compute time during active inference plus a small per-request fee, making it highly cost-effective for bursty, large-payload workloads. A subtle behavior is that the endpoint must be configured with a 'MaxConcurrentInvocationsPerInstance' setting to control throughput, and failed requests are automatically retried up to three times by default.
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.
Visual reference
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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?
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Asynchronous inference — Asynchronous inference is the most cost-effective option for large video files (up to 2 GB) with a tolerance for a few minutes of latency because it queues incoming requests, processes them in the background, and automatically scales down to zero when idle, eliminating the cost of idle compute. It supports payloads up to 1 GB natively and can handle larger files via S3 input, making it ideal for this workload without requiring a continuously running endpoint.
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
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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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