- A
Deploy on one ml.c5.large instance with an Application Auto Scaling target tracking policy based on memory utilization
ml.c5.large has 4 GB memory, suitable; one instance can handle 100 RPS; auto-scaling handles peak.
- B
Deploy on one ml.t2.medium instance with an Application Auto Scaling target tracking policy based on CPU utilization
Why wrong: ml.t2.medium has burstable CPU, may cause latency spikes under sustained load.
- C
Deploy on one ml.p3.2xlarge instance with provisioned concurrency
Why wrong: GPU instance is expensive and unnecessary for this model; provisioned concurrency is for Lambda, not SageMaker.
- D
Deploy on two ml.m5.large instances behind a load balancer with manual scaling
Why wrong: Two instances add cost; manual scaling doesn't handle peak automatically.
Quick Answer
The answer is to deploy on one ml.c5.large instance with an Application Auto Scaling target tracking policy based on memory utilization. This configuration directly addresses the need for auto-scaling SageMaker endpoints for low latency and cost because the ml.c5.large offers exactly 4 GB of memory to fit the large ensemble model, while its compute-optimized design keeps inference under 100ms. The target tracking policy on memory utilization ensures the endpoint scales out only when memory pressure rises during traffic spikes up to 200 requests per second, avoiding over-provisioning and minimizing cost during normal load. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your ability to balance instance sizing with scaling policies for real-time inference—a common trap is choosing a larger instance type like ml.m5.xlarge, which wastes cost, or using CPU-based scaling when memory is the bottleneck. Remember the memory tip: “Match the memory, scale on the pressure” to keep latency low and costs lean.
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. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 is deploying a machine learning model using Amazon SageMaker. They need to serve predictions with sub-100ms latency for a real-time application. The model is a large ensemble that requires 4 GB of memory. The team expects traffic of 100 requests per second initially, but it may double during peak hours. Which instance type and deployment configuration should the team choose to minimize cost while meeting the latency requirement?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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 on one ml.c5.large instance with an Application Auto Scaling target tracking policy based on memory utilization
Option A is correct because the ml.c5.large instance provides 4 GB of memory, which meets the model's requirement, and its compute-optimized nature ensures low-latency inference. Using Application Auto Scaling with a target tracking policy based on memory utilization allows the instance to scale out during traffic spikes (up to 200 requests per second) while minimizing cost by running a single instance during normal load.
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 on one ml.c5.large instance with an Application Auto Scaling target tracking policy based on memory utilization
Why this is correct
ml.c5.large has 4 GB memory, suitable; one instance can handle 100 RPS; auto-scaling handles peak.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Deploy on one ml.t2.medium instance with an Application Auto Scaling target tracking policy based on CPU utilization
Why it's wrong here
ml.t2.medium has burstable CPU, may cause latency spikes under sustained load.
- ✗
Deploy on one ml.p3.2xlarge instance with provisioned concurrency
Why it's wrong here
GPU instance is expensive and unnecessary for this model; provisioned concurrency is for Lambda, not SageMaker.
- ✗
Deploy on two ml.m5.large instances behind a load balancer with manual scaling
Why it's wrong here
Two instances add cost; manual scaling doesn't handle peak automatically.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose GPU instances (like p3) for any 'large' model, but the question specifies memory and latency requirements, not GPU compute needs, and they overlook that burstable instances (t2) cannot sustain low latency under continuous load due to CPU credit exhaustion.
Detailed technical explanation
How to think about this question
SageMaker real-time endpoints use synchronous HTTP inference, and latency is heavily influenced by instance memory bandwidth and CPU clock speed; the ml.c5.large uses Intel Xeon Platinum processors with up to 3.4 GHz turbo, ideal for compute-bound ensemble models. Application Auto Scaling target tracking policies adjust the desired instance count based on a CloudWatch metric (e.g., memory utilization), with a cooldown period to prevent thrashing, ensuring cost efficiency during variable traffic patterns.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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Deployment and Orchestration of ML Workflows — study guide chapter
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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 on one ml.c5.large instance with an Application Auto Scaling target tracking policy based on memory utilization — Option A is correct because the ml.c5.large instance provides 4 GB of memory, which meets the model's requirement, and its compute-optimized nature ensures low-latency inference. Using Application Auto Scaling with a target tracking policy based on memory utilization allows the instance to scale out during traffic spikes (up to 200 requests per second) while minimizing cost by running a single instance during normal load.
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.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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 →
Same concept, more angles
1 more ways this is tested on MLA-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. A machine learning engineer is configuring auto-scaling for a SageMaker real-time endpoint. The endpoint is expected to have steady traffic during business hours and low traffic at night. The engineer wants to minimize costs by scaling in during low traffic, but the model container has a long start-up time (about 5 minutes). Which scaling policy should the engineer use to prevent request drops during sudden traffic spikes?
medium- A.Use a step scaling policy based on invocations per minute with a step that adds two instances at a time.
- ✓ B.Use a target tracking scaling policy based on average invocations per minute with a warm-up of 300 seconds.
- C.Use a scheduled scaling action to add instances before business hours and remove them after.
- D.Use a simple scaling policy based on average CPU utilization with a cooldown period of 5 minutes.
Why B: Option B is correct because target tracking scaling policies in SageMaker automatically adjust capacity to maintain a target metric value, and the warm-up time of 300 seconds accounts for the 5-minute container start-up latency. This prevents request drops during sudden traffic spikes by ensuring new instances are fully initialized before they receive traffic, while still allowing the endpoint to scale in during low traffic to minimize costs.
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Last reviewed: Jun 24, 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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