MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is using Amazon SageMaker to deploy a model for real-time inference. The model endpoint is behind an Application Load Balancer (ALB) for A/B testing. The data scientist notices that the endpoint is returning HTTP 503 errors intermittently. The CloudWatch metrics show that the endpoint's Invocations metric is within limits, but the ModelLatency metric has high variance. What is the most likely cause?
⚠ Common exam trap
A common mix-up: candidates confuse 'Invocations within limits' with 'sufficient capacity,' overlooking that memory exhaustion can cause failures even when request rate is low, and they incorrectly attribute 503s solely to scaling issues (Option D) rather than resource constraints on each instance.
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
✓
The endpoint instance type does not have enough memory for the model.
High variance in ModelLatency combined with intermittent 503 errors strongly indicates that the model container is running out of memory under load. When memory is insufficient, the inference process may be killed by the kernel (OOM killer) or the container may be throttled, causing sporadic failures that manifest as 503s even though the Invocations metric (request count) appears within limits. The latency spikes occur because the container struggles to allocate memory for each request, leading to timeouts or crashes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model container is using a custom inference code that has a bug.
Why it's wrong here
A bug might cause errors, but 503 indicates resource exhaustion.
- ✗
The ALB health check is misconfigured and marking instances unhealthy.
Why it's wrong here
ALB health checks do not cause 503 from the endpoint; they affect traffic routing.
- ✓
The endpoint instance type does not have enough memory for the model.
Why this is correct
Insufficient memory can cause the model to fail to respond, leading to 503 errors.
- ✗
The endpoint is configured with too few instances; increase the instance count.
Why it's wrong here
While scaling out can mitigate, the root cause is memory; adding instances without fixing memory may not help if each instance is overloaded.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-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 MLS-C01 exam.