The answer is to use a target tracking scaling policy with a target value of 70% for the SageMakerVariantInvocationsPerInstance metric. This is correct because target tracking automatically adjusts the number of instances to keep the average CPU or invocation utilization close to your specified target, such as 70%, which directly addresses the need to handle occasional spikes without throttling while minimizing cost by scaling down during low traffic. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of dynamic scaling for real-time endpoints versus simple scheduled scaling or manual adjustments, which are less efficient for unpredictable traffic. A common trap is choosing a step scaling policy, but target tracking is simpler and more responsive for auto-scaling SageMaker real-time endpoints because it requires no custom alarms. Memory tip: think "70% target tracking" as the sweet spot for balancing cost and performance—like a thermostat maintaining a comfortable room temperature.
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 company is using SageMaker to host a model that performs real-time fraud detection. The model receives high request volumes with occasional spikes. The company wants to ensure that the endpoint can handle spikes without throttling while minimizing cost. Which scaling strategy should be used?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a target tracking scaling policy with a target value of 70% for the SageMakerVariantInvocationsPerInstance metric.
A target tracking scaling policy with the SageMakerVariantInvocationsPerInstance metric is the correct choice because it automatically adjusts the instance count to maintain a target utilization (e.g., 70%), handling spikes without manual intervention while minimizing cost by scaling down during low traffic. This is the recommended approach for real-time endpoints with variable traffic, as it aligns with AWS best practices for dynamic scaling.
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.
✓
Use a target tracking scaling policy with a target value of 70% for the SageMakerVariantInvocationsPerInstance metric.
Why this is correct
Automatically scales based on utilization.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a simple scaling policy with a step adjustment based on the InvocationsPerInstance metric.
Why it's wrong here
May not react quickly to spikes.
✗
Manually adjust the instance count based on monitoring dashboards.
Why it's wrong here
Not automated, may miss spikes.
✗
Use a scheduled scaling action to add instances during peak hours.
Why it's wrong here
Spikes are unpredictable.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse simple scaling (step adjustments) with target tracking, assuming any metric-based policy works, but target tracking is specifically designed for maintaining a utilization target and is the only option that handles irregular spikes without manual or scheduled intervention.
Detailed technical explanation
How to think about this question
Target tracking scaling uses a built-in metric (SageMakerVariantInvocationsPerInstance) and a target value (e.g., 70%) to dynamically add or remove instances via AWS Application Auto Scaling, which adjusts the desired count based on a proportional-integral-derivative (PID) controller algorithm. This avoids the cooldown limitations of step scaling and ensures the endpoint stays within the target utilization, even under bursty traffic, by preemptively provisioning capacity. In practice, setting the target too low (e.g., 30%) wastes cost, while too high (e.g., 90%) risks throttling during spikes, so 70% balances responsiveness and efficiency.
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.
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a target tracking scaling policy with a target value of 70% for the SageMakerVariantInvocationsPerInstance metric. — A target tracking scaling policy with the SageMakerVariantInvocationsPerInstance metric is the correct choice because it automatically adjusts the instance count to maintain a target utilization (e.g., 70%), handling spikes without manual intervention while minimizing cost by scaling down during low traffic. This is the recommended approach for real-time endpoints with variable traffic, as it aligns with AWS best practices for dynamic scaling.
What should I do if I get this MLS-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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Question Discussion
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