- A
Deploy a single model endpoint on an ml.c5.large instance with Auto Scaling configured using a target tracking policy based on invocations per minute
The ml.c5.large provides sufficient compute for the latency requirement, and Auto Scaling scales out during spikes. This is the most cost-effective approach.
- B
Deploy a single model endpoint on an ml.c5.large instance with a Multi-Model endpoint
Why wrong: Multi-Model endpoints are for serving multiple models on the same instance; but the scenario has only one ensemble, so it adds unnecessary complexity without benefit.
- C
Use SageMaker batch transform with multiple ml.c5.large instances to process all requests offline
Why wrong: Batch transform is for asynchronous, offline inference, not for real-time low latency needs.
- D
Deploy a single model endpoint on an ml.c5.xlarge instance with provisioned concurrency
Why wrong: ml.c5.xlarge is over-provisioned; the cost is higher without need. Provisioned concurrency is not a SageMaker feature.
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 data science team wants to deploy a real-time inference endpoint on Amazon SageMaker for a model that requires low latency (under 100 ms). The model is a small ensemble of three tree-based models, each about 50 MB. The team expects around 1000 requests per minute, with occasional spikes to 5000 requests per minute. Which instance type and deployment strategy would be MOST cost-effective while meeting the latency requirement?
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
Deploy a single model endpoint on an ml.c5.large instance with Auto Scaling configured using a target tracking policy based on invocations per minute
Option A is correct because deploying a single model endpoint on an ml.c5.large instance with Auto Scaling based on invocations per minute provides the necessary compute capacity for the expected 1000 requests per minute while scaling up to handle spikes up to 5000 requests per minute. The ml.c5.large instance offers sufficient memory (4 GB) and compute for three 50 MB tree-based models, and the target tracking policy ensures low latency by maintaining a buffer of capacity without over-provisioning, keeping inference under 100 ms.
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.
- ✓
Deploy a single model endpoint on an ml.c5.large instance with Auto Scaling configured using a target tracking policy based on invocations per minute
Why this is correct
The ml.c5.large provides sufficient compute for the latency requirement, and Auto Scaling scales out during spikes. This is the most cost-effective approach.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Deploy a single model endpoint on an ml.c5.large instance with a Multi-Model endpoint
Why it's wrong here
Multi-Model endpoints are for serving multiple models on the same instance; but the scenario has only one ensemble, so it adds unnecessary complexity without benefit.
- ✗
Use SageMaker batch transform with multiple ml.c5.large instances to process all requests offline
Why it's wrong here
Batch transform is for asynchronous, offline inference, not for real-time low latency needs.
- ✗
Deploy a single model endpoint on an ml.c5.xlarge instance with provisioned concurrency
Why it's wrong here
ml.c5.xlarge is over-provisioned; the cost is higher without need. Provisioned concurrency is not a SageMaker feature.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates might confuse provisioned concurrency (a Lambda concept) with SageMaker's scaling options, or incorrectly assume Multi-Model endpoints are suitable for ensemble models, leading to choosing B or D without considering the real-time latency constraint.
Trap categories for this question
Scenario analysis trap
Multi-Model endpoints are for serving multiple models on the same instance; but the scenario has only one ensemble, so it adds unnecessary complexity without benefit.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker Auto Scaling with a target tracking policy adjusts the number of instances based on a predefined metric (e.g., invocations per minute) using the Application Auto Scaling service, which polls CloudWatch metrics every minute. For tree-based models, inference is CPU-bound, and the ml.c5.large instance (2 vCPUs, 4 GB RAM) can handle the ensemble's serialized predictions (e.g., via XGBoost or sklearn) with minimal overhead, as each model is about 50 MB and fits in memory. In a real-world scenario, if the spike to 5000 requests per minute is sustained, Auto Scaling would add instances (e.g., up to 5) to maintain latency, but the cost is optimized by scaling down during low traffic.
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.
What to study next
Got this wrong? Here's your next step.
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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: Deploy a single model endpoint on an ml.c5.large instance with Auto Scaling configured using a target tracking policy based on invocations per minute — Option A is correct because deploying a single model endpoint on an ml.c5.large instance with Auto Scaling based on invocations per minute provides the necessary compute capacity for the expected 1000 requests per minute while scaling up to handle spikes up to 5000 requests per minute. The ml.c5.large instance offers sufficient memory (4 GB) and compute for three 50 MB tree-based models, and the target tracking policy ensures low latency by maintaining a buffer of capacity without over-provisioning, keeping inference under 100 ms.
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: Jun 24, 2026
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