MLS-C01 Modeling Practice Question
A company runs a real-time recommendation system on SageMaker with a model that uses a deep neural network. The endpoint uses a single ml.p3.2xlarge instance. Recently, the number of users has grown, and the endpoint's latency has increased from 50ms to 200ms, exceeding the SLA of 100ms. The model inference code is optimized and cannot be improved further. The company wants to reduce latency while minimizing cost. The data scientist has the following options: A. Switch to a larger instance type with more GPU memory, such as ml.p3.8xlarge. B. Use SageMaker's Elastic Inference to attach an EI accelerator to the existing instance. C. Deploy the model on multiple smaller instances (e.g., ml.p3.2xlarge) behind a load balancer and distribute traffic. D. Convert the model to use TensorFlow Lite and deploy on a CPU-based instance. Which option is the MOST cost-effective and meets the latency requirement?
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 SageMaker's Elastic Inference
The most cost-effective option is B (Use SageMaker's Elastic Inference) because it provides dedicated GPU acceleration at a fraction of the cost of a full GPU instance, reducing inference latency without requiring a larger instance. Option C (Switch to a larger instance type) would increase cost significantly. Option D (Deploy on multiple smaller instances behind a load balancer) would increase complexity and cost, and may not guarantee latency reduction. Option A (Convert to TensorFlow Lite on CPU) could reduce cost but may not meet the latency requirement as CPU inference is slower than GPU for deep neural networks, and model conversion might impact accuracy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Convert to TensorFlow Lite on CPU
Why it's wrong here
May lose accuracy and not meet latency on CPU.
- ✓
Use SageMaker's Elastic Inference
Why this is correct
Elastic Inference provides cost-effective GPU acceleration.
- ✗
Switch to a larger instance type, e.g., ml.p3.8xlarge
Why it's wrong here
Larger instance is more expensive and may not be cost-effective.
- ✗
Deploy on multiple smaller instances behind a load balancer
Why it's wrong here
Multiple instances increase cost and complexity.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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