MLS-C01 Modeling Practice Question
A company is building a real-time fraud detection system using Amazon SageMaker. The model is a gradient boosting classifier trained on 500 GB of transactional data. The inference endpoint is deployed as a SageMaker real-time endpoint using an ml.c5.9xlarge instance. The model is serialized using the native format of the framework (XGBoost). The endpoint receives about 100 requests per second with an average payload size of 10 KB. The company observes that the endpoint's latency is around 200 ms, but they need under 100 ms. The data scientist profiles the endpoint and finds that the model inference time is 50 ms, but the remaining time is spent on data preprocessing and serialization/deserialization. The preprocessing involves converting JSON input to a NumPy array and then to a DMatrix. Which action is most likely to reduce latency to meet the requirement?
⚠ Common exam trap
Candidates often assume latency is due to model complexity or instance size, but the question explicitly states inference is only 50 ms, so the fix must address the preprocessing/serialization bottleneck, not the model or compute resources.
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
✓
Use a more efficient serialization format such as Apache Arrow or Protocol Buffers for the input data
The bottleneck is data preprocessing and serialization/deserialization, not model inference. Using a more efficient serialization format like Apache Arrow or Protocol Buffers reduces the overhead of converting JSON to NumPy arrays and DMatrix, directly cutting the 150 ms spent outside inference. This targets the root cause without changing the model or infrastructure.
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 more efficient serialization format such as Apache Arrow or Protocol Buffers for the input data
Why this is correct
Reducing serialization/deserialization overhead directly addresses the bottleneck.
- ✗
Switch to SageMaker Batch Transform to process requests in batches
Why it's wrong here
Batch Transform is for asynchronous processing, not real-time.
- ✗
Use a larger instance type such as ml.c5.18xlarge
Why it's wrong here
Larger instance increases compute but does not address serialization overhead.
- ✗
Reduce the number of trees in the model
Why it's wrong here
Reducing model complexity may reduce accuracy and is not targeted at serialization.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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