MLA-C01 Exam Dumps : AWS Certified Machine Learning Engineer - Associate

Option A is correct because Amazon SageMaker AI supports automatic scaling for hosted models, and AWS documentation states that auto scaling dynamically adjusts the number of instances provisioned for a model in response to workload changes. When traffic increases, auto scaling brings more instances online; when traffic decreases, it removes unneeded capacity. This directly matches the requirement to keep the recommendation endpoint available during an expected increase in user traffic.

AWS also documents that SageMaker integrates with Application Auto Scaling, and you can scale SageMaker endpoint variants by using target tracking, step scaling, and scheduled scaling policies. That means the service is purpose-built for this scenario: maintaining availability and capacity as inference demand changes over time. Since the model is already being served from a SageMaker endpoint, the most direct and AWS-recommended solution is to configure endpoint auto scaling rather than redesigning the deployment.

The other options do not address the requirement as effectively. Creating a new endpoint does not automatically handle fluctuating traffic and adds unnecessary operational complexity. SageMaker Neo optimizes models for inference performance on supported hardware, but it does not provide elastic capacity management. Attaching an Auto Scaling group to a SageMaker endpoint is incorrect because SageMaker endpoints do not use EC2 Auto Scaling groups directly; scaling is handled through SageMaker’s integration with Application Auto Scaling. Therefore, to maintain availability during traffic spikes, the best AWS-documented answer is A: configure auto scaling on the SageMaker AI endpoint.

Scenario: The company needs to run a batch job for 90 minutes every weekend over the next 6 months. The processing can handle interruptions, and cost-effectiveness is a priority.

Why Spot Instances?

Cost-Effective: Spot Instances provide up to 90% savings compared to On-Demand Instances, making them the most cost-effective option for batch processing.

Interruption Tolerance: Since the processing can tolerate interruptions, Spot Instances are suitable for this workload.

Batch-Friendly: Spot Instances can be requested for specific durations or automatically re-requested in case of interruptions.

Steps to Implement:

Create a Spot Instance Request:

Use the EC2 console or CLI to request Spot Instances with desired instance type and duration.

Use Auto Scaling: Configure Spot Instances with an Auto Scaling group to handle instance interruptions and ensure job completion.

Run the Batch Job: Use tools like AWS Batch or custom scripts to manage the processing.

Comparison with Other Options:

Reserved Instances: Suitable for predictable, continuous workloads, but less cost-effective for a job that runs only once a week.

On-Demand Instances: More expensive and unnecessary given the tolerance for interruptions.

Dedicated Instances: Best for isolation and compliance but significantly more costly.

Recall measures the ability of a model to correctly identify all positive cases (true positives) out of all actual positives, minimizing false negatives. Since the cost of false negatives is much higher than false positives in this scenario, the ML engineer should prioritize models with high recall to reduce the likelihood of missing positive cases.