- A
Configure an Application Auto Scaling target tracking scaling policy for the variant based on the 'SageMakerVariantInvocationsPerInstance' metric, with a target value that keeps the inference latency within the SLA.
This auto-scales based on load.
- B
Deploy the model on multiple endpoints behind an Application Load Balancer.
Why wrong: Adds complexity and latency.
- C
Use scheduled scaling to increase the instance count during known peak hours.
Why wrong: Scheduled scaling cannot handle unexpected spikes.
- D
Manually increase the instance count during peak hours.
Why wrong: Manual scaling is not automated.
Question 339 of 1,000
hardMultiple ChoiceObjective-mapped
Configuring Auto-Scaling for SageMaker Real-Time Endpoints
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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 financial services company is deploying a real-time fraud detection model using Amazon SageMaker. The model is a gradient boosting model (XGBoost) trained on historical transaction data. The inference endpoint uses an ml.m5.2xlarge instance with a single variant. Recently, the company has experienced a 3x increase in transaction volume during peak hours, causing inference latency to exceed the 200ms SLA. The data science team has already optimized the model by reducing the number of trees and feature set, but the latency remains high during spikes. The team considers using SageMaker's built-in scaling policies. They currently have a single endpoint with one production variant. The team wants to maintain low latency without over-provisioning resources. They have ruled out model changes. Which approach should the team take?
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
✓
Configure an Application Auto Scaling target tracking scaling policy for the variant based on the 'SageMakerVariantInvocationsPerInstance' metric, with a target value that keeps the inference latency within the SLA.
Option A is correct because SageMaker's built-in target tracking scaling policy using the 'SageMakerVariantInvocationsPerInstance' metric allows the endpoint to automatically adjust the instance count based on real-time invocation load. By setting a target value that correlates with the 200ms SLA, the policy dynamically scales out during traffic spikes and scales in during lulls, preventing over-provisioning while maintaining low latency. This approach directly addresses the 3x peak-hour volume increase without requiring manual intervention or model changes.
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.
- ✓
Configure an Application Auto Scaling target tracking scaling policy for the variant based on the 'SageMakerVariantInvocationsPerInstance' metric, with a target value that keeps the inference latency within the SLA.
Why this is correct
This auto-scales based on load.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Deploy the model on multiple endpoints behind an Application Load Balancer.
Why it's wrong here
Adds complexity and latency.
- ✗
Use scheduled scaling to increase the instance count during known peak hours.
Why it's wrong here
Scheduled scaling cannot handle unexpected spikes.
- ✗
Manually increase the instance count during peak hours.
Why it's wrong here
Manual scaling is not automated.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse scheduled scaling (Option C) as a valid solution for predictable peaks, but the question's emphasis on 'real-time' and 'without over-provisioning' points to dynamic scaling, which target tracking provides; scheduled scaling cannot adapt to unexpected volume variations within the peak window.
Detailed technical explanation
How to think about this question
The 'SageMakerVariantInvocationsPerInstance' metric is a per-instance invocation count that SageMaker emits to CloudWatch every minute. Target tracking scaling uses a predefined or custom metric to maintain a target value (e.g., 1000 invocations per instance) which, when combined with the model's latency profile, ensures each instance handles a load that keeps inference under 200ms. Under the hood, Application Auto Scaling uses a cooldown period (default 300 seconds) to avoid thrashing, so the team should also adjust cooldown settings if rapid scaling is needed during sharp spikes.
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?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Configure an Application Auto Scaling target tracking scaling policy for the variant based on the 'SageMakerVariantInvocationsPerInstance' metric, with a target value that keeps the inference latency within the SLA. — Option A is correct because SageMaker's built-in target tracking scaling policy using the 'SageMakerVariantInvocationsPerInstance' metric allows the endpoint to automatically adjust the instance count based on real-time invocation load. By setting a target value that correlates with the 200ms SLA, the policy dynamically scales out during traffic spikes and scales in during lulls, preventing over-provisioning while maintaining low latency. This approach directly addresses the 3x peak-hour volume increase without requiring manual intervention or model changes.
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 →
Same concept, more angles
3 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 deploys a real-time inference endpoint on SageMaker for a customer-facing application. Traffic patterns are unpredictable and sometimes spike. The endpoint must scale automatically to handle load while minimizing cost. Which approach should the company take?
medium- A.Switch to batch transform for all inference requests.
- B.Use a larger instance type to handle peak traffic.
- ✓ C.Configure a target tracking scaling policy on the endpoint using Amazon CloudWatch metrics.
- D.Deploy multiple models behind an Application Load Balancer.
Why C: Option C is correct because SageMaker endpoints support automatic scaling through target tracking scaling policies based on Amazon CloudWatch metrics like InvocationsPerInstance. This allows the endpoint to dynamically adjust the number of instances in response to real-time traffic spikes, scaling out when demand increases and scaling in when it decreases, which optimizes cost by only paying for the capacity needed at any given time.
Variation 2. A company has a trained machine learning model that needs to be deployed as a real-time inference endpoint on Amazon SageMaker. The endpoint must automatically scale based on incoming traffic. Which SageMaker feature should be used?
easy- ✓ A.SageMaker Endpoint Auto Scaling
- B.SageMaker Elastic Inference
- C.SageMaker Batch Transform
- D.SageMaker Model Monitor
Why A: Amazon SageMaker Endpoint Auto Scaling is the correct feature because it automatically adjusts the number of instances serving a real-time inference endpoint based on the incoming traffic load. It uses Application Auto Scaling policies, which monitor CloudWatch metrics (e.g., InvocationsPerInstance) to scale in or out, ensuring low latency and cost efficiency without manual intervention.
Variation 3. A company is deploying a real-time inference endpoint for a natural language processing model using Amazon SageMaker. The model is a fine-tuned BERT variant. The endpoint has been running for two weeks with acceptable latency (average 200 ms). However, over the past 24 hours, the latency has increased to an average of 800 ms, and the number of simultaneous requests has doubled. The team expects traffic to continue to grow. The current endpoint configuration uses a single ml.m5.large instance. The model is loaded into memory once, and the inference framework is PyTorch. The team needs to maintain latency under 500 ms. Which course of action should the team take to address the latency increase while minimizing cost?
easy- A.Switch to ml.c5.large instances because CPU-optimized instances provide better inference performance for NLP models.
- B.Increase the instance size to ml.m5.xlarge and keep a single instance.
- ✓ C.Enable automatic scaling for the endpoint with a target average latency of 500 ms and use multiple ml.m5.large instances.
- D.Implement a multi-model endpoint with multiple ml.m5.large instances and use Amazon Elastic Inference (EI) accelerators.
Why C: Option C is correct because the latency increase is caused by a doubling of simultaneous requests overwhelming a single ml.m5.large instance. Enabling automatic scaling with a target average latency of 500 ms allows SageMaker to add more ml.m5.large instances as traffic grows, distributing the load and keeping latency under the threshold. This approach minimizes cost by scaling only when needed, rather than over-provisioning a larger instance.
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Last reviewed: Jun 24, 2026
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