MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is using SageMaker to host a model for real-time inference. They notice that the endpoint's latency increases over time. The model is stateless and the inference code does not log any errors. What is the MOST likely cause?
⚠ Common exam trap
Watch out — candidates often confuse a 'stateless model' with 'no memory issues' — but a stateless model means no state between requests, not that the container's memory usage is stable; a memory leak in the inference code or framework can still cause latency degradation over time.
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
✓
Memory leak in the inference container
A memory leak in the inference container causes the process's resident memory to grow over time as allocated memory is not freed. Since the model is stateless and no errors are logged, the leak is likely in the inference code or a dependency (e.g., a TensorFlow session or a Python list that grows unbounded). As memory pressure increases, the operating system may swap or the container may be OOM-killed, leading to increased garbage collection pauses and higher latency for each request.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Memory leak in the inference container
Why this is correct
Memory leaks cause slowdown over time.
- ✗
Gradual increase in request payload size
Why it's wrong here
Request size varies; not necessarily gradual.
- ✗
Endpoint auto scaling is adding new instances
Why it's wrong here
Auto scaling would reduce latency, not increase.
- ✗
Model is accumulating state from previous requests
Why it's wrong here
Model is stateless.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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