MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is using Amazon SageMaker to train and deploy a fraud detection model. The model is a gradient boosting machine (GBM) trained on a dataset with 10 million rows and 50 features. The training job runs on an ml.m5.2xlarge instance with 8 vCPUs and 32 GB memory. The training completes successfully, and the model is deployed to a real-time endpoint. After deployment, the inference latency is around 200 ms per request, which is acceptable. However, after a week, the company observes that latency increases to over 1 second during peak hours (12:00-13:00 UTC). CloudWatch metrics show CPU utilization on the endpoint instance reaches 95% during these peaks. The endpoint is configured with a single ml.m5.large instance. The company wants to maintain latency under 500 ms during peak hours without incurring unnecessary cost during off-peak hours. Which solution should the company implement?
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
✓
Configure SageMaker endpoint auto scaling with a target CPU utilization of 70% and a minimum instance count of 1
Configuring auto scaling based on CPU utilization dynamically adds instances during peak hours (when CPU exceeds target) and removes them during off-peak, maintaining latency under 500 ms while minimizing cost. Option A is incorrect because reducing instances to zero and manually launching a new endpoint is not automated, causes downtime, and is impractical. Option B is incorrect because Batch Transform is designed for offline, batch inference, not for real-time requests. Option D is incorrect because replacing the instance with a larger, always-on type (ml.m5.4xlarge) would handle peak load but incur higher cost during off-peak hours, unlike auto scaling which scales in.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the number of instances to zero during off-peak hours and manually launch a new endpoint every day at 12:00
Why it's wrong here
Manual intervention is error-prone and causes downtime during scaling.
- ✗
Switch to SageMaker Batch Transform and have the application send requests in batches
Why it's wrong here
Batch Transform is for offline predictions, not real-time.
- ✓
Configure SageMaker endpoint auto scaling with a target CPU utilization of 70% and a minimum instance count of 1
Why this is correct
Auto scaling dynamically adjusts instance count to handle load, keeping latency low and cost efficient.
- ✗
Replace the endpoint instance type with ml.m5.4xlarge to handle peak load
Why it's wrong here
Vertical scaling is less cost-effective than horizontal scaling, as it runs a large instance all the time.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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