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. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 has deployed a SageMaker real-time endpoint for a model that predicts customer churn. The endpoint uses a single ml.m5.large instance. After deployment, the team notices that during peak hours, the endpoint returns 5xx errors for about 20% of requests. The endpoint has not been configured with any scaling policy. The team needs to resolve this issue with minimal cost increase. Which solution should the team implement?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable Auto Scaling for the endpoint with a target tracking policy based on the average InvocationsPerInstance metric.
Option B is correct because enabling Auto Scaling with a target tracking policy based on the average InvocationsPerInstance metric dynamically adjusts the number of instances in response to traffic spikes, preventing 5xx errors during peak hours without over-provisioning. This approach minimizes cost by scaling only when needed, unlike manual instance upgrades or batch transforms that either increase baseline cost or introduce latency.
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.
✗
Deploy the model to a multi-model endpoint to reduce resource utilization.
Why it's wrong here
Multi-model endpoints share resources among models but do not automatically scale to handle traffic spikes.
✓
Enable Auto Scaling for the endpoint with a target tracking policy based on the average InvocationsPerInstance metric.
Why this is correct
Auto Scaling adds instances only when needed, minimizing cost while handling peak load.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the instance type to ml.m5.xlarge to handle more concurrent requests.
Why it's wrong here
This increases cost even during low traffic, not minimal cost increase.
✗
Use SageMaker batch transform instead of real-time inference to process peak traffic asynchronously.
Why it's wrong here
Batch transform is not suitable for real-time applications requiring low latency.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'scaling up' (increasing instance size) with 'scaling out' (adding more instances), and overlook that Auto Scaling with a target tracking policy is the most cost-effective way to handle variable traffic, as it matches capacity to demand in real time.
Detailed technical explanation
How to think about this question
SageMaker Auto Scaling uses Application Auto Scaling with a target tracking policy that adjusts the desired instance count based on the InvocationsPerInstance metric, which is emitted by the endpoint every 1 minute. The scaling policy maintains a target value (e.g., 1000 invocations per instance) by adding or removing instances, with a cooldown period (default 300 seconds) to prevent thrashing. Under the hood, this integrates with CloudWatch alarms and the SageMaker UpdateEndpoint API to modify the production variant's instance count, ensuring the endpoint can handle bursts without manual intervention.
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: Enable Auto Scaling for the endpoint with a target tracking policy based on the average InvocationsPerInstance metric. — Option B is correct because enabling Auto Scaling with a target tracking policy based on the average InvocationsPerInstance metric dynamically adjusts the number of instances in response to traffic spikes, preventing 5xx errors during peak hours without over-provisioning. This approach minimizes cost by scaling only when needed, unlike manual instance upgrades or batch transforms that either increase baseline cost or introduce latency.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.