The answer is to add a scaling policy based on the number of concurrent requests per instance. This is correct because CPU utilization alone is a lagging indicator for inference workloads; by the time CPU spikes, queued requests already cause high latency and 503 errors. A custom metric like concurrent requests per instance provides a leading signal that triggers scale-out before saturation, aligning with actual demand on the model container. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this tests your understanding that SageMaker endpoint auto scaling with custom metrics can combine multiple target tracking policies—a common trap is relying solely on CPU or memory, which works for training but not for latency-sensitive serving. Remember the memory tip: “CPU lags, concurrency leads”—if you want to beat latency, track the requests at the door, not the heat in the engine.
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 is deploying a TensorFlow model on a SageMaker real-time endpoint with automatic scaling. They set the scaling policy to target an average CPU utilization of 50%. However, during traffic spikes, the endpoint experiences high latency and 503 errors. The instance type is ml.c5.large. What should the team do to resolve this while minimizing cost?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Add a scaling policy based on the number of concurrent requests per instance
Option D is correct because scaling based on CPU utilization alone is often insufficient for inference workloads where latency is the primary concern. By adding a scaling policy based on the number of concurrent requests per instance, the team can proactively scale out before CPU saturation occurs, reducing latency and eliminating 503 errors. SageMaker's automatic scaling supports multiple target tracking metrics, and using concurrent requests per instance aligns more closely with the actual demand on the model serving container.
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.
✗
Pre-warm the endpoint by keeping a fixed number of additional instances
Why it's wrong here
Pre-warming is manual and not cost-efficient.
✗
Increase the scale-in cooldown period to avoid frequent downsizing
Why it's wrong here
Increasing cooldown would delay scaling in but not help with scaling out during spikes.
✗
Change the instance type to a larger one like ml.c5.xlarge to handle the spikes
Why it's wrong here
Larger instances increase cost and may still not handle spikes without scaling out.
✓
Add a scaling policy based on the number of concurrent requests per instance
Why this is correct
Concurrent requests metric often provides faster and more accurate scaling for ML endpoints.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume larger instances (Option C) are the only way to handle spikes, but the exam tests understanding that scaling policies based on the right metric (concurrent requests) can be more cost-effective and responsive than simply scaling up instance size.
Detailed technical explanation
How to think about this question
SageMaker's target tracking scaling policies use CloudWatch metrics to adjust capacity. CPU utilization is a lagging indicator for inference workloads because requests can queue up in the container before CPU usage rises. The 'ConcurrentRequestsPerInstance' metric (available via SageMaker's built-in metrics) directly reflects the number of active invocations, allowing the scaling policy to react faster. In practice, setting a target value for concurrent requests (e.g., 1000 per instance) ensures that new instances are provisioned before the existing ones become overloaded, reducing tail latency and preventing 503s.
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.
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: Add a scaling policy based on the number of concurrent requests per instance — Option D is correct because scaling based on CPU utilization alone is often insufficient for inference workloads where latency is the primary concern. By adding a scaling policy based on the number of concurrent requests per instance, the team can proactively scale out before CPU saturation occurs, reducing latency and eliminating 503 errors. SageMaker's automatic scaling supports multiple target tracking metrics, and using concurrent requests per instance aligns more closely with the actual demand on the model serving container.
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
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 →
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 deploying a model using SageMaker and needs to ensure that the endpoint can automatically scale based on traffic patterns. Which TWO actions should the engineer take? (Choose two.)
medium
✓ A.Define a scaling policy using Application Auto Scaling for the SageMaker endpoint variant.
✓ B.Set up an Amazon CloudWatch alarm to trigger scaling based on the InvocationsPerInstance metric.
C.Enable SageMaker Model Monitor to detect data drift.
D.Configure a multi-model endpoint to serve multiple models.
E.Use SageMaker batch transform to handle variable traffic.
Why A: Option A is correct because SageMaker endpoints use Application Auto Scaling to automatically adjust the number of instances based on traffic. You define a scaling policy (e.g., target tracking, step scaling) that references a CloudWatch metric. Option B is correct because the InvocationsPerInstance metric is a standard SageMaker endpoint metric that reflects the load per instance, and a CloudWatch alarm on this metric can trigger the scaling policy to add or remove instances as traffic changes.
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.