MLS-C01 Question #122: Real Exam Question with Answer & Explanation

Question

A manufacturer is operating a large number of factories with a complex supply chain relationship where unexpected downtime of a machine can cause production to stop at several factories. A data scientist wants to analyze sensor data from the factories to identify equipment in need of preemptive maintenance and then dispatch a service team to prevent unplanned downtime. The sensor readings from a single machine can include up to 200 data points including temperatures, voltages, vibrations, RPMs, and pressure readings. To collect this sensor data, the manufacturer deployed Wi-Fi and LANs across the factories. Even though many factory locations do not have reliable or high-speed internet connectivity, the manufacturer would like to maintain near-real-time inference capabilities. Which deployment architecture for the model will address these business requirements?

Options

• ADeploy the model in Amazon SageMaker.
• BDeploy the model on AWS IoT Greengrass in each factory.
• CDeploy the model to an Amazon SageMaker batch transformation job.
• DDeploy the model in Amazon SageMaker and use an IoT rule to write data to an Amazon

Explanation

For latency-sensitive use cases and for use-cases that require analyzing large amounts of streaming data, it may not be possible to run ML inference in the cloud. Besides, cloud- connectivity may not be available all the time. For these use cases, you need to deploy the ML model close to the data source. SageMaker Neo + IoT GreenGrass To design and push something to edge: 1. design something to do the job, say TF model 2. compile it for the edge device using SageMaker Neo, say Nvidia Jetson 3. run it on the edge using IoT GreenGrass

No community discussion yet for this question.