Introduction to Machine Learning with Sound Cognitive Class Exam Quiz Answers

Clear My Certification January 23, 2021 Certification, Cognitive Class, Featured Leave a comment 892 Views

Apply Here: Introduction to Machine Learning with Sound Cognitive Class Exam Quiz Answers

Introduction to Machine Learning with Sound Cognitive Class Certification Answers

Lab 1: Gather and Prepare the Data

Question 1: Data gathering is a key component in machine learning.

True
False

Question 2: For machine learning models, data needs to be quantifiable and not comparable.

True
False

Question 3: Audio files can be compared directly.

True
False

Question 4: Truncating audio files to the same length makes them compatible.

True
False

Question 5: Silence detection can be used to locate the start of a tune or noise even though the noise might already be any number of bars into a tune.

True
False

Lab 2: Build a Machine Learning Model

Question 1: In Watson Studio, which type of project do you create for a machine learning project to make it easy to find associated assets and models?

Watson tools
Data science
Business Analytics
Streams Designer
Basic

Question 2: Which statement is true for a typical machine learning project in Watson Studio?

Comes with everything included
Needs an associated Cloud Object Storage only
Needs an associated Machine Learning service only
Needs an associated Spark service only
Needs Cloud Object Storage, a Machine Learning service, and a Spark service

Question 3: What’s the best way to select columns in a machine learning model in Watson Studio?

Always select all columns as features
Select a prediction column and all remaining columns as features
Select a prediction column and select which remaining, sometimes all, columns are features
Select only a prediction column

Question 4: What does a 60%, 20%, 20% split of data mean?

60% of the data is used to train models, 20% to test the models, and 20% to test for overfitting.
Checks that more that 60% of the predictions are correct, no more than 20% are incorrect, and no more than 20% are borderline.
Takes no more than 60 steps to prepare an estimate and no more than 20 steps to determine the certainty.
20% of the data is used to train the models, 60% of the data is used to test the models, and 20% to test for overfitting

Question 5: How does an overfitting model perform?

Performs well on all data
Performs well on data that is used to select the model but performs poorly with other data
Performs badly on all data
Performs poorly on data that is used to select the model but performs well with other data

Lab 3: Create Predictions in a Node-RED Application

Question 1: Node-RED allows you to import and export flows.

True
False

Question 2: In Node-RED, you can install nodes by using which method?

Deploy option
The Dashboard
Manage Palette option
The Settings option

Question 3: Which combination of Node-RED nodes are required to inject audio into a flow?

file inject and microphone
inject and microphone
file inject and inject
file inject, inject, and microphone

Question 4: You can use the http request node to do which task?

Encode a data buffer
Invoke a REST API
Create a HTTP endpoint
Deliver an API response

Question 5: A machine learning classification prediction response contains which items?

Raw prediction for each class, probability for each class, all classes
Raw prediction for each class, probability for each class,
Raw prediction for each class, probability for each class, prediction, prediction class
Raw prediction for each class, probability for each class, prediction, prediction class, all classes

Lab 4: Create Multiclass Classification Models

Question 1: When do you use a multiclass classification?

When your label column contains two distinct categories
When your label column contains a discrete number of categories
When your label column contains a large number of values
When your label column contains different data types

Question 2: You run machine learning predictions against which type of model or data?

Deployed models
Undeployed models
Generated models
Selected estimators

Question 3: What does each Lite Plan instance of the Watson Machine Learning service allow?

Multiple deployments of only 1 model
Single deployments of any number of models
A maximum total of 5 deployments of any number of models
Any number of deployments of a maximum of 5 models

Question 4: What is an application prediction?

The prediction from one deployed model
An application consolidated prediction from any number of deployed models
The best prediction from up to five deployed models
The best and worst prediction from up to five deployed models

Question 5: An application must consider only the highest probability scoring prediction from a prediction.

True
False

Lab 5: Create UIs and Integrate Visual Recognition

Question 1: The following HTML code in the Node-RED UI application allows the HTML web page to process JavaScript.

<script>{{{payload.script}}}</script>

True
False

Question 2: One way to train the Watson Visual Recognition service is to feed it positive images of what you want to predict, say, domestic cats, and negative images, say, dogs, lions, birds, and other animals, that you don’t want to predict.

True
False

Question 3: Which type of node is a one-way communication link that can update a web page every time the Machine Learning service makes a prediction?

http input
websocket
machine learning
prediction

Question 4: If the Node-RED application for Lab 5 processes nine machine learning models, how many machine learning nodes are required?

Question 5: The Visual Recognition service is simply an API that you can connect to by using http input and output nodes.

True
False

Introduction to Machine Learning with Sound Final Exam – Cognitive Class

Question 1: If you split your data by 70%, 20%, 10%, which percentage is used for the training data?

100%
70%
20%
10%

Question 2: You can use digital signal processing to create numbers for audio files so that you can compare the audio files and then use these numbers as the basis of a machine learning model.

True
False

Question 3: In which Node-RED node do you set the Mode field to run a prediction?

Build Payload Values function node
Hardcoded test node
Watson Machine Learning (WML) node
Prediction Columns node

Question 4: When you create new projects in Watson Studio, a machine learning service is automatically associated with the new project.

True
False

Question 5: The Naïve Bayes estimator does not work with data that contains negative numbers.

True
False

Question 6: In this course, you use Cloud Object Storage to store data files, such as CSV files.

True
False

Question 7: In Lab 3 of this course, you ran a hardcoded prediction test by using Build Payload Values function node. In the code for this function node, why do the columns start with column 2 rather than column 1?

Because column 2 contains the value that the machine learning model will be predicting
Because column 1 includes all the data
Because column 1 is a feature column
Because column 1, which is not a feature column, is the value that the machine learning model will be predicting

Question 8: Why is it necessary to deploy the Python Flask digital signal processing application in Lab 3?

So that you can feed an audio file to the machine learning node in the Node-RED application
So that you can run predictions in IBM Cloud
So that your final application for this course can be provided as an API
So that you can refetch model lists that include sound files

Question 9: After you deploy a model in Watson Studio, you see a deployment ID. You use this ID to call the predictor in Node-RED or other application.

True
False

Question 10: The Watson Visual Recognition service can be trained to recognize both audio and images.

True
False