(Week 4) NPTEL Foundation of Cloud IoT Edge ML - Assignment Answer 2023
NPTEL Foundation of Cloud IoT Edge ML - Assignment 4 Answer 2023 .In this article we are going to share answers for NPTEL Foundation of Cloud IoT Edge ML . All the Answers are provided below to help students as a reference. This will help you with the answers to NPTEL (National Programme on Technology Enhanced Learning)
Week : 4 Assignment 2023 nptel
Course Name: Foundation of Cloud IoT Edge ML
Below you will find answers for Foundation of Cloud IoT Edge ML Assignment 4 Answer 2023:-
NTPEL Foundation of Cloud IoT Edge ML Assignment answers
Q1. ___________ orchestrates clusters of virtual machines and schedules containers to run on those virtual machines based on their available compute resources and the resource requirements of each container.
a. Kubernetes
b. Docker
c. Orchestrations
Answer:- a. Kubernetes.
Q2. The main functions of kubelet service are:
a. Register the node it's running on by creating a node resource in the API server.
b. Continuously monitor the API server for pods that got scheduled to the node.
c. Start the pod's containers by using the configured container runtime.
d. All of the above
Answer:- d. All of the above.
Q3. Container runtime takes care of:
a. Pulls the required container image from an image registry if it's not available locally.
b. Prepares a container mount point.
c. Alerts the kernel to assign some resource limits like CPU or memory limits.
d. All of the above
Answer:- d. All of the above.
Q4. ___________ is an open platform for developing, shipping, and running applications that enables to separate your applications from infrastructure to deliver software quickly.
a. Kubernetes
b. Docker
c. Orchestrations
Answer:- b. Docker.
Q5. The __________ listens for docker API requests and manages docker objects such as images, containers, networks, and volumes. The ___________ is the primary way that many docker users interact with docker.
a. Docker daemon, Docker client
b. Docker daemon, Docker registry
c. Docker object, Docker registry
d. Docker object, Docker client
Answer:- a. Docker daemon, Docker client.
Q6. Analysis of a time series data involves decomposing the series into its constituent parts which include __________.
a. Only trend
b. Only seasonal effect
c. Neither trend nor seasonal trend
d. Both trend and seasonal effects
Answer:- d. Both trend and seasonal effects.
Q7. What is the basic concept of Recurrent Neural Network?
a. Use a loop between inputs and outputs in order to achieve the better prediction.
b. Use previous inputs to find the next output according to the training set.
c. Use loops between the most important features to predict next output.
Answer:- a. Use a loop between inputs and outputs in order to achieve better prediction.
Q8. What is Exploding Gradients?
a. When the algorithm assigns a stupidly high importance to the weights, when your dataset is too big.
b. When the algorithm assigns a stupidly high importance to the weights, because the better features.
c. When the algorithm assigns a stupidly high importance to the weights, when your data is too small.
d. When the algorithm assigns a stupidly high importance to the weights, without much reason.
Answer:- c. When the algorithm assigns a stupidly high importance to the weights, when your data is too small.
Q9. What is Vanishing Gradients?
a. When the values of a gradient are too big and the model stops learning or takes way too long because of that.
b. When the values of a gradient are too big and the model joins in a loop because of that.
c. When the values of a gradient are too small and the model joins in a loop because of that.
d. When the values of a gradient are too small and the model stops learning or takes way too long because of that.
Answer:- d. When the values of a gradient are too small and the model stops learning or takes way too long because of that.
Q10. What is LSTM?
a. LSTM networks are an extension for recurrent neural networks, which basically extends their memory. Therefore it is well suited to learn from important experiences that have very low time lags in between.
b. LSTM networks are an extension for recurrent neural networks, which basically shorten their memory. Therefore it is well suited to learn from important experiences that have very low time lags in between.
c. LSTM networks are an extension for recurrent neural networks, which basically extends their memory. Therefore it is not recommended to use it, unless you are using a small Dataset.
d. LSTM networks are an extension for recurrent neural networks, which basically extends their memory. Therefore it is well suited to learn from important experiences that have very long time lags in between.
Answer:- a. LSTM networks are an extension for recurrent neural networks, which basically extends their memory. Therefore it is well suited to learn from important experiences that have very low time lags in between.
