- A
Consolidate multiple small models into a single Multi-Model Endpoint on a larger instance.
Multi-Model Endpoints reduce cost by sharing an instance among multiple models.
- B
Increase the number of minimum instances to handle traffic spikes without scaling.
Why wrong: Increasing min instances raises base cost and may be wasteful.
- C
Right-size the instances by analyzing CloudWatch metrics and reducing instance size for underutilized endpoints.
Right-sizing reduces instance cost without impacting performance if instances are over-provisioned.
- D
Limit the maximum number of concurrent invocations per endpoint.
Why wrong: Limiting concurrency may cause throttling and degrade user experience.
- E
Use a scheduled scaling to turn off endpoints during non-business hours.
Why wrong: Turning off endpoints causes unavailability; real-time endpoints need to be always up or have minimal downtime.
Quick Answer
The answer is right-sizing instances and using Multi-Model Endpoints. Right-sizing reduces costs by analyzing CloudWatch metrics like CPU and memory utilization to match instance size to actual workload demand, preventing over-provisioning. Multi-Model Endpoints further cut expenses by hosting multiple small models behind a single endpoint, dynamically loading and unloading models based on traffic, which eliminates the need for separate endpoints and underutilized instances. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your ability to optimize inference infrastructure without sacrificing latency or throughput—a common trap is assuming you must always scale up or add instances, when consolidation is often the smarter move. Remember the memory tip: “Right-size the instance, then multi-model the models” to keep costs low and performance high.
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. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
An ML team is running multiple SageMaker endpoints for various models. The monthly cost is higher than expected. Which TWO actions would help reduce costs without negatively impacting performance?
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
Consolidate multiple small models into a single Multi-Model Endpoint on a larger instance.
Option A is correct because SageMaker Multi-Model Endpoints allow you to host multiple small models on a single endpoint behind a common serving container, sharing the underlying instance resources. This reduces the number of endpoints and instances needed, lowering costs without degrading performance, as models are loaded and unloaded dynamically based on traffic.
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.
- ✓
Consolidate multiple small models into a single Multi-Model Endpoint on a larger instance.
Why this is correct
Multi-Model Endpoints reduce cost by sharing an instance among multiple models.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the number of minimum instances to handle traffic spikes without scaling.
Why it's wrong here
Increasing min instances raises base cost and may be wasteful.
- ✓
Right-size the instances by analyzing CloudWatch metrics and reducing instance size for underutilized endpoints.
Why this is correct
Right-sizing reduces instance cost without impacting performance if instances are over-provisioned.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Limit the maximum number of concurrent invocations per endpoint.
Why it's wrong here
Limiting concurrency may cause throttling and degrade user experience.
- ✗
Use a scheduled scaling to turn off endpoints during non-business hours.
Why it's wrong here
Turning off endpoints causes unavailability; real-time endpoints need to be always up or have minimal downtime.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse cost reduction with availability or scaling strategies, incorrectly assuming that reducing instance count or limiting concurrency is always beneficial, without considering the impact on performance or the specific capabilities of SageMaker Multi-Model Endpoints.
Detailed technical explanation
How to think about this question
Multi-Model Endpoints use a shared inference container that loads model artifacts from Amazon S3 on demand, caching them in memory. This is efficient for models with low traffic or sporadic usage, as the endpoint can scale horizontally while the number of models per instance is limited only by memory and disk. Under the hood, SageMaker uses a model cache and eviction policy (LRU) to manage multiple models, ensuring that frequently accessed models remain hot.
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.
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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: Consolidate multiple small models into a single Multi-Model Endpoint on a larger instance. — Option A is correct because SageMaker Multi-Model Endpoints allow you to host multiple small models on a single endpoint behind a common serving container, sharing the underlying instance resources. This reduces the number of endpoints and instances needed, lowering costs without degrading performance, as models are loaded and unloaded dynamically based on traffic.
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
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.
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