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. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 deploying a real-time inference endpoint for a natural language processing model using Amazon SageMaker. The model requires GPU acceleration and must handle variable traffic patterns, including sudden spikes. The team wants to minimize costs while maintaining low latency during spikes. Which endpoint configuration strategy should they use?
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 the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a multi-model endpoint on a GPU instance with Auto Scaling based on invocation count.
Option D is correct because a multi-model endpoint on a GPU instance with Auto Scaling based on invocation count allows multiple models to share a single GPU, maximizing utilization and reducing cost. Auto Scaling based on invocation count dynamically adjusts the number of instances to handle traffic spikes while maintaining low latency, as it scales out quickly when the invocation count exceeds a threshold.
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 single large GPU instance with provisioned concurrency.
Why it's wrong here
Provisioned concurrency keeps resources warm but is expensive for variable traffic.
✗
Use a serverless endpoint with GPU support.
Why it's wrong here
SageMaker serverless inference does not support GPU instances.
✗
Use a single GPU instance in multiple Availability Zones with an Application Load Balancer.
Why it's wrong here
Multi-AZ improves availability but does not optimize cost for variable traffic.
✓
Use a multi-model endpoint on a GPU instance with Auto Scaling based on invocation count.
Why this is correct
Multi-model endpoints share instances across models, and Auto Scaling adjusts capacity for spikes.
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume serverless endpoints support GPU acceleration, but SageMaker serverless endpoints are CPU-only, making Option B invalid despite its cost-saving appeal.
Detailed technical explanation
How to think about this question
A multi-model endpoint loads multiple models into memory on a single GPU instance, switching between them based on invocation, which reduces the number of instances needed. Auto Scaling based on invocation count uses CloudWatch metrics to trigger scale-out events when the number of invocations per instance exceeds a target value, ensuring that new instances are provisioned before latency degrades. Under the hood, SageMaker uses a model cache on the instance to avoid reloading models from Amazon S3 for every request, which keeps inference latency low even during model switching.
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.
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 multi-model endpoint on a GPU instance with Auto Scaling based on invocation count. — Option D is correct because a multi-model endpoint on a GPU instance with Auto Scaling based on invocation count allows multiple models to share a single GPU, maximizing utilization and reducing cost. Auto Scaling based on invocation count dynamically adjusts the number of instances to handle traffic spikes while maintaining low latency, as it scales out quickly when the invocation count exceeds a threshold.
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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Question Discussion
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