- A
Create a SageMaker endpoint with multiple instances behind a load balancer and configure automatic scaling based on CPUUtilization or InvocationsPerSecond
Scaling out with multiple instances distributes the load, reducing latency and eliminating 503 errors. Automatic scaling adjusts the number of instances based on demand, optimizing cost.
- B
Enable SageMaker Data Capture to collect inference data for later analysis to identify slow requests
Why wrong: Data Capture is used for monitoring and debugging, not for reducing latency or eliminating 503 errors.
- C
Replace the endpoint instance type with a more powerful compute-optimized instance, such as ml.c5.4xlarge
Why wrong: A larger instance provides more CPU capacity, but scaling out (multiple smaller instances) is often more cost-effective and provides better availability. A single larger instance still presents a single point of failure.
- D
Increase the endpoint invocation timeout from 60 seconds to 120 seconds in the application configuration
Why wrong: Increasing the timeout may reduce the number of 503 errors temporarily but does not solve the underlying performance issue; requests may still take too long and time out later.
Quick Answer
The answer is to create a SageMaker endpoint with multiple instances behind a load balancer and configure automatic scaling based on CPUUtilization or InvocationsPerSecond. This directly addresses the auto scaling endpoint CPU overload scenario because a 95% CPU utilization signals that the single ml.c5.2xlarge instance is saturated, causing high ModelLatency and 503 errors during peak hours. By scaling out horizontally, you distribute the inference load across multiple instances, reducing per-instance CPU pressure and eliminating service unavailability, while auto scaling ensures you only pay for the capacity needed at any given time. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of horizontal versus vertical scaling trade-offs—a common trap is choosing a larger instance (vertical scaling), which is less cost-effective and still leaves a single point of failure. Remember the memory tip: “Scale out, not up, to stop the 503 hiccup.”
MLA-C01 Practice Question: ML Solution Monitoring, Maintenance and Security
This MLA-C01 practice question tests your understanding of ml solution monitoring, maintenance and security. 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.
A financial services company uses an Amazon SageMaker endpoint for real-time credit scoring. The endpoint is deployed with an ml.c5.2xlarge instance. Recently, the data science team has received complaints from users about slow response times. The team monitors the endpoint using CloudWatch metrics. They observe that the InvocationsPerSecond metric averages 50, the ModelLatency metric averages 200 milliseconds, and the CPUUtilization metric averages 95%. The team has also noticed that the endpoint occasionally returns HTTP 503 (Service Unavailable) errors during peak hours. The team needs to reduce latency and eliminate 503 errors while minimizing cost increase. Which solution should the team implement?
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
Create a SageMaker endpoint with multiple instances behind a load balancer and configure automatic scaling based on CPUUtilization or InvocationsPerSecond
CPUUtilization at 95% indicates that the instance is overloaded, causing high latency and 503 errors. Scaling out (adding more instances) will distribute the load and reduce latency, and using automatic scaling ensures that the number of instances adjusts to demand, minimizing cost by scaling down when traffic is low. Option A (larger instance) may not be as cost-effective as scaling out, and Option B (enable data capture) would not help latency. Option D (increase timeout) does not address the root cause of overloading.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create a SageMaker endpoint with multiple instances behind a load balancer and configure automatic scaling based on CPUUtilization or InvocationsPerSecond
Why this is correct
Scaling out with multiple instances distributes the load, reducing latency and eliminating 503 errors. Automatic scaling adjusts the number of instances based on demand, optimizing cost.
Related concept
Static NAT maps one inside address to one outside address.
- ✗
Enable SageMaker Data Capture to collect inference data for later analysis to identify slow requests
Why it's wrong here
Data Capture is used for monitoring and debugging, not for reducing latency or eliminating 503 errors.
- ✗
Replace the endpoint instance type with a more powerful compute-optimized instance, such as ml.c5.4xlarge
Why it's wrong here
A larger instance provides more CPU capacity, but scaling out (multiple smaller instances) is often more cost-effective and provides better availability. A single larger instance still presents a single point of failure.
- ✗
Increase the endpoint invocation timeout from 60 seconds to 120 seconds in the application configuration
Why it's wrong here
Increasing the timeout may reduce the number of 503 errors temporarily but does not solve the underlying performance issue; requests may still take too long and time out later.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLA-C01 NAT questions on configuration and troubleshooting.
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ML Solution Monitoring, Maintenance and Security — study guide chapter
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
ML Solution Monitoring, Maintenance and Security — This question tests ML Solution Monitoring, Maintenance and Security — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Create a SageMaker endpoint with multiple instances behind a load balancer and configure automatic scaling based on CPUUtilization or InvocationsPerSecond — CPUUtilization at 95% indicates that the instance is overloaded, causing high latency and 503 errors. Scaling out (adding more instances) will distribute the load and reduce latency, and using automatic scaling ensures that the number of instances adjusts to demand, minimizing cost by scaling down when traffic is low. Option A (larger instance) may not be as cost-effective as scaling out, and Option B (enable data capture) would not help latency. Option D (increase timeout) does not address the root cause of overloading.
What should I do if I get this MLA-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLA-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
About these practice questions
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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 company uses Amazon SageMaker to deploy a real-time inference endpoint. They notice increased latency in predictions during peak hours. Which should they investigate first to address the issue?
easy- ✓ A.Review the endpoint auto-scaling policy
- B.Check the data labeling job status
- C.Modify the training instance type
- D.Increase the model artifact size
Why A: Option B is correct because latency typically increases when the endpoint is under-provisioned; auto-scaling policies control scaling behavior. Option A is about training, not inference. Option C is unrelated to inference latency. Option D may affect latency but is not the first thing to investigate.
Last reviewed: Jun 23, 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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