Forecasting housing prices based on historical data is an example of anomaly detection?
No
Identifying suspicious sign-ins by looking for deviations from usual pattern is an example of anomaly detection?
Yes
Predicting whether a pantient will develop diabetes based on the patient’s medical history is an example of anomaly detection?
No
The handling is unusual or missing values provided to an AI system is a consideration for the Microsoft ___ principle for responsible AI
Reliability and Safety
Match AI Workload to Scenario
- Automated chat to answer questions about refunds and exchanges
- Determining whether a photo contains a person
- Determining whether a review is positive or negative
- Conversational AI
- Computer vision
- NLP
Principle?
An AI system that empowers everyone, including people who have hearing, visual and other impairments
Inclusiveness
Principle:
Ensure that AI systems operate as they were originally designed, respond to unanticipated conditions and resist harmful manipulation
Reliability and safety
Principle:
Implementing processes to ensure that decisions made by AI systems can be overridden by humans
Accountability
Principle:
Provide consumers with information and controls over the collection, use and storage of their data
Privacy and security
Principle:
When developing an AI system for self-driving cars, the Microsoft responsible AI should be applied to ensure consistent operation system during unexpected circumstances
Reliability and safety
Which services should you use to extract text, key/value pairs and table data automatically from scanned documents?
- Form Recongizer
- Text Analytics
- Ink Recognizer
- Custom Vision
- FORM RECOGNIZER
The ability to extract subtotals and totals from a receipt is a capability of the ___ service
Form Recognizer
You use Azure Machine Learning designer to publish an inference pipeline.
Which two parameters should you use to consume the pipeline? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. the model name
B. the training endpoint
C. the authentication key
D. the REST endpoint
C. the authentication key
D. the REST endpoint
From Azure Machine Learning Designer, to deploy a real-time inference pipeline as a services for others to consume, you must deploy the model to
- a local web service
- Azure Container Instances
- Azure Kubernetes Services
- Azure Machine Learning compute
Azure Kubernetes Service
NOTE:
For Production use AKS
For Dev/Text use Azure Container Instances
Predicting how many hours of overtime a delivery person will work based on the number of order received in an example of ____
- Classification
- Clustering
- Regression
- Regression
NOTE:
In the most basic sense, regression refers to prediction of a numeric target.
Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable. You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
Incorrect Answers: ✑ Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data. ✑ Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation.
Difference between classification, clustering and regression?
✑ Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data. ✑ Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation. ✑ Regression, in the most basic sense, refers to prediction of a numeric target
Yes or No:
- Azure Machine Learning designer provides a drag-and-drop visual canvas to build, test and deploy machine Learning models?
- Azure Machine Learning designer enables you to save your progress as a pipeline draft
- Azure Machine Learning designer enables you to include custom JavaScript functions
Box 1: Yes -
Azure Machine Learning designer lets you visually connect datasets and modules on an interactive canvas to create machine learning models.
Box 2: Yes -
With the designer you can connect the modules to create a pipeline draft.
As you edit a pipeline in the designer, your progress is saved as a pipeline draft.
Box 3: No -
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designe
Household Income:
20 000
23 000
80 000
House Price Category:
Low
Medium
High
Which is a feature and which is a label?
Household Income = Feature
House Price Category = Label
NOTE:
Feature = Input
Label = Output
Which metric can you use to evaluate a classification model?
A. true positive rate
B. mean absolute error (MAE)
C. coefficient of determination (R2)
D. root mean squared error (RMSE
A. True Positive Rate
What does a good model look like?
An ROC curve that approaches the top left corner with 100% true positive rate and 0% false positive rate will be the best model.
A random model would display as a flat line from the bottom left to the top right corner.
Worse than random would dip below the y=x line
Which two components can you drag onto a canvas in Azure Machine Learning designer?
A. dataset
B. compute
C. pipeline
D. module
A. Dataset
D. Module
You need to create a training dataset and validation dataset from an existing dataset. Which module in the Azure Machine Learning designer should you use?
A. Select Columns in Dataset
B. Add Rows
C. Split Data
D. Join Data
C. Split Data
A common way of evaluating a model is to divide the data into a training and test set by using Split Data, and then validate the model on the training data.
Use the Split Data module to divide a dataset into two distinct sets.
The studio currently supports training/validation data splits
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-cross-validation-data-splits
Match the types of machine learning to the appropriate scenarios.
- Predict how many minutes late a flight will arrive based on the amount of snowfall at an airport
- Segment customers into different groups to support a marketing department
- Predict whether aa student will complete a university course
Box 1: Regression -
In the most basic sense, regression refers to prediction of a numeric target.
Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.
You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
Box 2: Classification - Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data.
Box 3: Clustering - (Similarities)
Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation.
Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment
Match the machine learning tasks to the appropriate scenarios.
- Examining the values of a confusion matrix
- Splitting a date into month, day, and year fields
- Picking temperature and pressure to train a weather model
Choose from Learning Types:
- Feature engineering
- Feature selection
- Model Deployment
- Model evaluation
- Model training
Answer:
Box 1: Model evaluation - The Model evaluation module outputs a confusion matrix showing the number of true positives, false negatives, false positives, and true negatives, as well as ROC, Precision/Recall, and Lift curves.
Box 2: Feature engineering -
Feature engineering is the process of using domain knowledge of the data to create features that help ML algorithms learn better. In Azure Machine Learning, scaling and normalization techniques are applied to facilitate feature engineering. Collectively, these techniques and feature engineering are referred to as featurization.
Note: Often, features are created from raw data through a process of feature engineering. For example, a time stamp in itself might not be useful for modeling until the information is transformed into units of days, months, or categories that are relevant to the problem, such as holiday versus working day.
Box 3: Feature selection -
In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable inputs. Narrowing the field of data helps reduce noise and improve training performance
Data values that influence the prediction of a model are called __
- dependent variables
- features
- identifiers
- labels
Answer:
- Labels
In machine learning, if you have labeled data, that means your data is marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict.
In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription, or processing.
Incorrect Answers:
Not features: In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable inputs. Narrowing the field of data helps reduce noise and improve training performance
