- A
Use a step scaling policy based on invocations per minute with a step that adds two instances at a time.
Why wrong: Step scaling can be used but without warm-up, new instances may still receive traffic before being fully initialized. Warm-up is not a built-in parameter for step scaling.
- B
Use a target tracking scaling policy based on average invocations per minute with a warm-up of 300 seconds.
Target tracking with a warm-up period ensures that newly launched instances are not included in the metric until they are ready, preventing traffic loss.
- C
Use a scheduled scaling action to add instances before business hours and remove them after.
Why wrong: Scheduled scaling only handles predictable traffic; it does not react to sudden spikes outside the schedule.
- D
Use a simple scaling policy based on average CPU utilization with a cooldown period of 5 minutes.
Why wrong: Simple scaling policies do not support warm-up; new instances may receive traffic before they are ready, causing errors.
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 machine learning engineer is configuring auto-scaling for a SageMaker real-time endpoint. The endpoint is expected to have steady traffic during business hours and low traffic at night. The engineer wants to minimize costs by scaling in during low traffic, but the model container has a long start-up time (about 5 minutes). Which scaling policy should the engineer use to prevent request drops during sudden traffic spikes?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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 target tracking scaling policy based on average invocations per minute with a warm-up of 300 seconds.
Option B is correct because target tracking scaling policies in SageMaker automatically adjust capacity to maintain a target metric value, and the warm-up time of 300 seconds accounts for the 5-minute container start-up latency. This prevents request drops during sudden traffic spikes by ensuring new instances are fully initialized before they receive traffic, while still allowing the endpoint to scale in during low traffic to minimize costs.
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 step scaling policy based on invocations per minute with a step that adds two instances at a time.
Why it's wrong here
Step scaling can be used but without warm-up, new instances may still receive traffic before being fully initialized. Warm-up is not a built-in parameter for step scaling.
- ✓
Use a target tracking scaling policy based on average invocations per minute with a warm-up of 300 seconds.
Why this is correct
Target tracking with a warm-up period ensures that newly launched instances are not included in the metric until they are ready, preventing traffic loss.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a scheduled scaling action to add instances before business hours and remove them after.
Why it's wrong here
Scheduled scaling only handles predictable traffic; it does not react to sudden spikes outside the schedule.
- ✗
Use a simple scaling policy based on average CPU utilization with a cooldown period of 5 minutes.
Why it's wrong here
Simple scaling policies do not support warm-up; new instances may receive traffic before they are ready, causing errors.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose a step scaling policy (Option A) because they think adding multiple instances at once handles spikes faster, but they overlook the critical need for a warm-up period to account for container start-up latency, which target tracking with warm-up explicitly addresses.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker target tracking policies use CloudWatch alarms to continuously monitor the specified metric (e.g., invocations per minute) and invoke scaling actions to keep the metric close to the target value. The warm-up time parameter (in seconds) tells SageMaker to wait before allowing newly launched instances to receive traffic, which is critical for containers with long initialization times—without it, the endpoint would route requests to instances that are still loading, causing timeouts or errors. In real-world scenarios, this is especially important for models that load large artifacts (e.g., embeddings or neural network weights) from Amazon S3 during startup, where a 5-minute warm-up can prevent a cascade of failures during a flash crowd.
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.
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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: Use a target tracking scaling policy based on average invocations per minute with a warm-up of 300 seconds. — Option B is correct because target tracking scaling policies in SageMaker automatically adjust capacity to maintain a target metric value, and the warm-up time of 300 seconds accounts for the 5-minute container start-up latency. This prevents request drops during sudden traffic spikes by ensuring new instances are fully initialized before they receive traffic, while still allowing the endpoint to scale in during low traffic to minimize costs.
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.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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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