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MLA-C01 Practice Question: A team is deploying a machine learning model…
A team is deploying a machine learning model using Amazon SageMaker. They need to serve predictions with sub-100ms latency for a real-time application. The model is a large ensemble that requires 4 GB of memory. The team expects traffic of 100 requests per second initially, but it may double during peak hours. Which instance type and deployment configuration should the team choose to minimize cost while meeting the latency requirement?
⚠ Common exam trap
It's easy for candidates to choose GPU instances (like p3) for any 'large' model, but the question specifies memory and latency requirements, not GPU compute needs, and they overlook that burstable instances (t2) cannot sustain low latency under continuous load due to CPU credit exhaustion.
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
✓
Deploy on one ml.c5.large instance with an Application Auto Scaling target tracking policy based on memory utilization
The ml.c5.large instance provides 4 GB of memory, which meets the model's requirement, and its compute-optimized nature ensures low-latency inference. Using Application Auto Scaling with a target tracking policy based on memory utilization allows the instance to scale out during traffic spikes (up to 200 requests per second) while minimizing cost by running a single instance during normal load.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Deploy on one ml.c5.large instance with an Application Auto Scaling target tracking policy based on memory utilization
Why this is correct
ml.c5.large has 4 GB memory, suitable; one instance can handle 100 RPS; auto-scaling handles peak.
- ✗
Deploy on one ml.t2.medium instance with an Application Auto Scaling target tracking policy based on CPU utilization
Why it's wrong here
ml.t2.medium has burstable CPU, may cause latency spikes under sustained load.
- ✗
Deploy on one ml.p3.2xlarge instance with provisioned concurrency
Why it's wrong here
GPU instance is expensive and unnecessary for this model; provisioned concurrency is for Lambda, not SageMaker.
- ✗
Deploy on two ml.m5.large instances behind a load balancer with manual scaling
Why it's wrong here
Two instances add cost; manual scaling doesn't handle peak automatically.
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