- A
Create a SageMaker endpoint with an Application Auto Scaling target tracking policy based on the SageMakerVariantInvocationsPerInstance metric
SageMaker endpoints support Application Auto Scaling with target tracking on invocations per instance, handling spikes.
- B
Deploy the model on a multi-model endpoint and manually adjust the number of instances via the AWS Management Console
Why wrong: Manual adjustment contradicts automatic handling of unpredictable spikes.
- C
Deploy the model on an Elastic Inference accelerator and use AWS Auto Scaling with a scheduled policy
Why wrong: Elastic Inference is for cost savings, not auto-scaling; scheduled policy doesn't handle unpredictable spikes.
- D
Create a batch transform job with a scheduled Lambda function to trigger scaling
Why wrong: Batch transform is for offline inference, not real-time; scheduled Lambda doesn't handle unpredictable spikes.
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 has trained a model using SageMaker and wants to deploy it for real-time inference with automatic scaling based on request latency. The deployment must handle unpredictable traffic spikes without manual intervention. Which combination of SageMaker features should the team use?
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 an Application Auto Scaling target tracking policy based on the SageMakerVariantInvocationsPerInstance metric
Option A is correct because it uses a SageMaker endpoint with an Application Auto Scaling target tracking policy based on the SageMakerVariantInvocationsPerInstance metric. This allows the endpoint to automatically scale the number of instances in response to changes in request latency, as the metric directly reflects the load per instance. The target tracking policy adjusts capacity to maintain a target value for the metric, handling unpredictable traffic spikes without manual intervention.
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.
- ✓
Create a SageMaker endpoint with an Application Auto Scaling target tracking policy based on the SageMakerVariantInvocationsPerInstance metric
Why this is correct
SageMaker endpoints support Application Auto Scaling with target tracking on invocations per instance, handling spikes.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Deploy the model on a multi-model endpoint and manually adjust the number of instances via the AWS Management Console
Why it's wrong here
Manual adjustment contradicts automatic handling of unpredictable spikes.
- ✗
Deploy the model on an Elastic Inference accelerator and use AWS Auto Scaling with a scheduled policy
Why it's wrong here
Elastic Inference is for cost savings, not auto-scaling; scheduled policy doesn't handle unpredictable spikes.
- ✗
Create a batch transform job with a scheduled Lambda function to trigger scaling
Why it's wrong here
Batch transform is for offline inference, not real-time; scheduled Lambda doesn't handle unpredictable spikes.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse automatic scaling with manual adjustments or batch processing, or mistakenly think that Elastic Inference or scheduled policies can handle real-time, unpredictable traffic spikes, when only a target tracking policy on a SageMaker endpoint provides the required dynamic, latency-aware scaling.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker integrates with Application Auto Scaling to adjust the number of instances in a production variant based on a target tracking policy. The SageMakerVariantInvocationsPerInstance metric is emitted by CloudWatch, and the policy works by continuously monitoring this metric and scaling out when the metric exceeds the target value (e.g., 1000 invocations per instance) or scaling in when it drops. In a real-world scenario, this is critical for e-commerce platforms where traffic spikes occur during flash sales, ensuring low-latency predictions without over-provisioning resources.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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: Create a SageMaker endpoint with an Application Auto Scaling target tracking policy based on the SageMakerVariantInvocationsPerInstance metric — Option A is correct because it uses a SageMaker endpoint with an Application Auto Scaling target tracking policy based on the SageMakerVariantInvocationsPerInstance metric. This allows the endpoint to automatically scale the number of instances in response to changes in request latency, as the metric directly reflects the load per instance. The target tracking policy adjusts capacity to maintain a target value for the metric, handling unpredictable traffic spikes without manual intervention.
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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