Fundamentals of Cloud IoT Edge ML Assignment 8 Answers
1) What does AWS stand for?
- Amazing Web Sphere
- Amazing World Services
- Amazon Web Services
- Amazing Web Services
Answer: Amazon Web Services
2) _________ acts as an interface between the applications and similarly a device that is sending sensor data to the cloud.
- AWS IoT core
- AWS device management
- AWS greengrass
- AWS IoT device SDK
Answer: AWS IoT core
3) IoT device management supports bulk onboarding of devices and also has properties like over-the-air software updates, maintenance and performing bulk jobs.
- True
- False
Answer: True
4) __________ raise an alert if there is a drift between the preferred configuration and the policy. It also maintains a highly secure footprint of all the devices and if there is any anomaly it raise an alert so that is the fleet audit or protection service.
- AWS IoT device defender
- AWS IoT analytics
- AWS IoT device SDK
- AWS IoT core
Answer: AWS IoT device defender
5) Which of the following allows developers to easily build complex workflows on AWS IoT Greengrass without having to worry about understanding device protocols, managing credentials, or interacting with external APIs.
- Lambda function
- Subscriptions
- Connectors
- ML inferencing
Answer: Connectors
6) In federated learning, a network of nodes with all nodes have their own central server but instead of sharing data with the central server, send the model to server.
- True
- False
Answer: True
7) Following are the advantages of gradient descent
- Easy computation
- Easy to implement
- Easy to understand
- All of the above
Answer: All of the above
8) ________ is a method of ML that trains an ML algorithm with the local data samples distributed over multiple edge devices or servers without any exchange of data.
- Federated learning
- Deep learning
- Reinforcement learning
- None of the above
Answer: Federated learning
9) Today, all autonomous vehicles on the road utilize edge computing AI programs, which are often trained using data center machine learning models. Autonomous car machine learning models are only made possible by the incredible computing power of modern data centers capable of hundreds of petaflops.
- True
- False
Answer: True
10) What are the key components of ML for self-driving cars.
- Scene Representation
- Perform semantic object segmentation
- Time tracking using embeddings
- All of the above
Answer: All of the above
Disclaimer: CHANDRAS EDU does not guarantee the correctness of the answers. These answers are based on the data provided by the NPTEL video lectures, are just for reference, and request students to complete the assignments independently.
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