Merge branch 'main' of https://github.com/microsoft/Data-Science-For-…

…Beginners into main

5-Data-Science-In-Cloud/18-tbd/README.md

@@ -25,6 +25,20 @@ Table of contents:
 ## Pre-Lecture Quiz
 
 [Pre-lecture quiz]()
+
+1. What is the cloud?
+   1. A collection of Databases for storing big data.
+   2. TRUE : A collection of pay-as-you-go computing services over the internet.
+   3. A visible mass of particles suspended in the air.
+2. What are some advantages of the cloud?
+   1. TRUE : Flexibility, Scalability, Reliability, Security
+   2. Flexibility, Scalability, Reliability, Variability
+   3. Clarity, Scalability, Reliability, Variability
+3. Which one is not necessarily a good reason for choosing the cloud?
+   1. Using Machine Learning and data intelligence services
+   2. Processing large amounts of data
+   3. TRUE : Storing sensitive/confidential governmental data
+
 ## 1. Introduction
 ### 1.1 What is Azure Machine Learning?
 Data scientists expend a lot of effort exploring and pre-processing data, and trying various types of model-training algorithms to produce accurate models, which is time consuming, and often makes inefficient use of expensive compute hardware.
@@ -311,13 +325,31 @@ The script should return :
     ```python
     b'"{\\"result\\": [true, false]}"'
     ```
+
+Congratulations! You just consumed the model deployed and trained on Azure ML !
+
+> **_NOTE:_** Once you are done with the project, don't forget to delete all the resources.
 ## 🚀 Challenge
 
 Look more closely at the model explanations and details that AutoML generated for the top models. Try to understand why the best model is better than the other ones. What algorithms were compared? What are the differences between them? Why is the best one performing better in this case?
 
 ## Post-Lecture Quiz
 
 [Post-lecture quiz]()
+1. What do I need to create before accessing Azure ML Studio?
+   1. TRUE: A workspace
+   2. A compute instance
+   3. A compute cluster
+
+2. Which of the following are the different tasks supported by Automated ML?
+   1. Image generation
+   2. TRUE : Classification
+   3. Natural Language generation
+
+3. In which case do you need GPU over CPU?
+   1. When you have tabular data
+   2. When you have enough money to afford it
+   3. TRUE: When you do Deep Learning
 
 ## Review & Self Study
 

5-Data-Science-In-Cloud/19-tbd/README.md

@@ -27,6 +27,18 @@ Table of contents:
 
 [Pre-lecture quiz]()
 
+1. On what increasing the cluster size would NOT have an impact?
+   1.  Responsiveness
+   2.  Cost
+   3.  TRUE: Model performance 
+2. What is a benefit of using low code tools?
+   1. TRUE: No expertise of code required
+   2. Automatically label the dataset
+   3. Better security of the model
+3. What is AutoML?
+   1. A tool for automating the preprocessing of data
+   2. A tool for automating the deployment of models
+   3. TRUE: A tool for automating the development of models
 
 ## 1. Introduction
 
@@ -276,7 +288,9 @@ response
 ```
 This should output `'{"result": [false]}'`. This means that the patient input we sent to the endpoint generated the prediction `false` which means this person is not likely to have a heart attack.
 
-Congratulations! You just consumed the model deployed and trained on Azure ML!
+Congratulations! You just consumed the model deployed and trained on Azure ML with the Azure ML SDK!
+
+> **_NOTE:_** Once you are done with the project, don't forget to delete all the resources.
 
 ## 🚀 Challenge
 
@@ -288,12 +302,24 @@ Congratulations! You just consumed the model deployed and trained on Azure ML!
 
 [Post-lecture quiz]()
 
+1. What is the reason for creating an AutoMLConfig?
+   1. It is where the training and the testing data are split
+   2. TRUE : It provides all the details of your AutoML experiment
+   3. It is where you specify the model to be trained
+2. Which of the following metrics is supported by Automated ML for a classification task?
+   1. TRUE : accuracy
+   2. r2_score
+   3. normalized_root_mean_error
+3. What is NOT an advantage of using the SDK?
+   1. It can be used to automate multiple tasks and runs
+   2. It makes it easier to programmatically edit runs
+   3. It can be used throught a Graphical User Interface
 ## Review & Self Study
 
 In this lesson, you learned how to train, deploy and consume a model to predict heart failure risk with the Azure ML SDK in the cloud. Check this [documentation](https://docs.microsoft.com/en-us/python/api/overview/azure/ml/?view=azure-ml-py) for further information about the Azure ML SDK. Try to create your own model with the Azure ML SDK. 
 
 ## Assignment
 
-[Assignment Title](assignment.md)
+[Data Science project using Azure ML SDK](assignment.md)
 
 

5-Data-Science-In-Cloud/19-tbd/assignment.md

@@ -1,4 +1,4 @@
-# Data Science in the Cloud: The "Azure ML SDK" way 
+# Data Science project using Azure ML SDK
 
 ## Instructions