- A
Switch to a smaller instance type.
Why wrong: Smaller instances may scale faster but could be underpowered for the workload.
- B
Use a pre-warmed endpoint with a target tracking scaling policy.
Correct. Pre-warmed endpoints keep a minimum number of instances ready, and target tracking proactively scales based on metrics.
- C
Enable SageMaker Inference Recommender to optimize instance type.
Why wrong: Inference Recommender helps choose instance type but does not directly reduce scaling time.
- D
Implement a canary deployment with a blue/green strategy.
Why wrong: Canary deployments are for updating models, not scaling latency.
- E
Set a lower scaling cooldown period.
Why wrong: Lower cooldown can help scale in faster but does not reduce the time for new instances to become healthy.
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. 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 runs a real-time inference endpoint with an auto-scaling policy based on average CPU utilization. During a traffic spike, the endpoint scales out but takes several minutes to become healthy, causing increased latency. The endpoint uses a large instance type. Which change would MOST effectively reduce the time to scale out?
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
Use a pre-warmed endpoint with a target tracking scaling policy.
Option B is correct because a pre-warmed endpoint with a target tracking scaling policy ensures that a baseline number of instances are always ready to handle traffic, eliminating the cold-start delay during scale-out. The target tracking policy dynamically adjusts the number of instances to maintain a target average CPU utilization, which reduces the time to scale out by avoiding the need to provision and initialize new instances from scratch during a spike.
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.
- ✗
Switch to a smaller instance type.
Why it's wrong here
Smaller instances may scale faster but could be underpowered for the workload.
- ✓
Use a pre-warmed endpoint with a target tracking scaling policy.
Why this is correct
Correct. Pre-warmed endpoints keep a minimum number of instances ready, and target tracking proactively scales based on metrics.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable SageMaker Inference Recommender to optimize instance type.
Why it's wrong here
Inference Recommender helps choose instance type but does not directly reduce scaling time.
- ✗
Implement a canary deployment with a blue/green strategy.
Why it's wrong here
Canary deployments are for updating models, not scaling latency.
- ✗
Set a lower scaling cooldown period.
Why it's wrong here
Lower cooldown can help scale in faster but does not reduce the time for new instances to become healthy.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse scaling policies (like target tracking) with deployment strategies (like canary or blue/green), or assume that reducing instance size or cooldown periods will solve initialization delays, when the core issue is the cold-start time of large instances.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker endpoints use Amazon EC2 instances that require time to download the model artifacts, load the container, and run health checks before becoming healthy. Pre-warming maintains a minimum number of instances in the 'InService' state, so when a target tracking scaling policy triggers a scale-out, the new instances are already initialized and can immediately accept traffic, reducing the latency spike. In real-world scenarios, this is critical for applications with unpredictable traffic patterns, such as e-commerce flash sales or streaming event processing.
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
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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 — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a pre-warmed endpoint with a target tracking scaling policy. — Option B is correct because a pre-warmed endpoint with a target tracking scaling policy ensures that a baseline number of instances are always ready to handle traffic, eliminating the cold-start delay during scale-out. The target tracking policy dynamically adjusts the number of instances to maintain a target average CPU utilization, which reduces the time to scale out by avoiding the need to provision and initialize new instances from scratch during a spike.
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: Jul 4, 2026
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