Microsoft Azure Test 3

Your manager have provided you a dataset created for multiclass classification tasks that contains a normalized numerical feature set with 10,000 data points and 150 features.
You use 75 percent of the data points for training and 25 percent for testing.
You are using the scikit-learn machine learning library in Python. You use X to denote the feature set and Y to denote class labels.
You create the following Python data frames:

You need to apply the Principal Component Analysis (PCA) method to reduce the dimensionality of the feature set to 10 features in both training and testing sets.
How should you complete the code segment?


Select 1 option(s):

Your manager have provided you a feature set containing the following numerical features: X, Y, and Z.
The Poisson correlation coefficient (r-value) of X, Y, and Z features is shown in the following image:

Select the correct answer from the following box:


Select 1 option(s):

You are Azure Machine Learning expert.
You are using a decision tree algorithm. You have trained a model that generalizes well at a tree depth equal to 10.
You need to select the bias and variance properties of the model with varying tree depth values.
Which properties should you select for each tree depth?

Select 1 option(s):

You are data scientist of your company and you use Azure Machine Learning Studio.
You are developing a linear regression model in Azure Machine Learning Studio. You run an experiment to compare different algorithms.
The following image displays the results dataset output:

Choose appropriate answer from the following box


Select 1 option(s):

You are asked to use C-Support Vector classification to do a multi-class classification with an unbalanced training dataset.
The C-Support Vector classification using Python code shown below:

You need to evaluate the C-Support Vector classification code.
Which evaluation statement should you use?

You are junior Machine Learning associate of a company named XYZ.
You are performing feature scaling by using the scikit-learn Python library for x.1 x2, and x3 features.
Original and scaled data is shown in the following image.

Select the answer based on the information presented in the graphic.


Select 1 option(s):

You are junior Machine Learning associate of a company named XYZ.
You are performing feature scaling by using the scikit-learn Python library for x.1 x2, and x3 features.
Original and scaled data is shown in the following image.

Select the answer based on the information presented in the graphic.

You are junior Machine Learning associate of a company named XYZ.
You are performing feature scaling by using the scikit-learn Python library for x.1 x2, and x3 features.
Original and scaled data is shown in the following image.

Select the answer based on the information presented in the graphic.

You start by creating a linear regression model.
You need to evaluate the linear regression model.
Solution:
Use the following metrics: Relative Squared Error, Coefficient of Determination, Accuracy, Precision, Recall, F1 score, and AUC.
Does the solution meet the goal?

Select 1 option(s):

You need to evaluate the model results for imbalance.
Which evaluation metric should you use?

Select 1 option(s):

You have the following compute environments. The organization does not want to create another compute environment.

Environment name Compute type nb server Compute Instance aks cluster Azure Kubernetes Service mlc cluster Machine Learning Compute
You need to determine which compute environment to use for the following scenarios.
Which compute types should you use? To answer, drag the appropriate compute environments to the correct scenarios. Each compute environment may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.


Select 1 option(s):

You plan to provision an Azure Machine Learning Basic edition workspace for a data science project.
You need to identify the tasks you will be able to perform in the workspace.
Which three tasks will you be able to perform? Each correct answer presents a complete solution.

Select 3 option(s):

You have a dataset that contains 2,000 rows. You are building a machine learning classification model by using Azure Learning Studio. You add a Partition and
Sample module to the experiment.
You need to configure the module. You must meet the following requirements:
? Divide the data into subsets
? Assign the rows into folds using a round-robin method
? Allow rows in the dataset to be reused

How should you configure the module?


Select 1 option(s):

Each CSV file contains the sales record for a particular month and has the filename sales.csv. Each file in stored in a folder that indicates the month and year when the data was recorded. The folders are in an Azure blob container for which a datastore has been defined in an Azure Machine Learning workspace. The folders are organized in a parent folder named sales to create the following hierarchical structure:

At the end of each month, a new folder with that month’s sales file is added to the sales folder.
You plan to use the sales data to train a machine learning model based on the following requirements:
• You must define a dataset that loads all of the sales data to date into a structure that can be easily converted to a dataframe.
• You must be able to create experiments that use only data that was created before a specific previous month, ignoring any data that was added after that month.
• You must register the minimum number of datasets possible.
You need to register the sales data as a dataset in Azure Machine Learning service workspace.
What should you do?

Select 1 option(s):

You are expert in Azure Machine Learning
You are tuning a hyperparameter for an algorithm. The following table shows a data set with different hyperparameter, training error, and validation errors.

Select the answer from the following boxes:

You must identify the output files that are generated by the experiment run. You need to add code to retrieve the output file names. Which code segment should you add to the script?

Select 1 option(s):

Which module you should use for below step?
Step:
Define the metric

Select 1 option(s):

You need to register the container as a datastore in an Azure Machine Learning workspace and ensure that an error will be raised if the container does not exist.
How should you complete the code?
To answer, select the appropriate options in the answer area.


Select 1 option(s):

You train a model and register it in your Azure Machine Learning workspace.
You are ready to deploy the model as a real-time web service.
You deploy the model to an Azure Kubernetes Service (AKS) inference cluster, but the deployment fails because an error occurs when the service runs the entry script that is associated with the model deployment.
You need to debug the error by iteratively modifying the code and reloading the service, without requiring a re-deployment of the service for each code update.
What should you do?

Select 1 option(s):

You are creating a binary classification by using a two-class logistic regression model.
You need to evaluate the model results for imbalance.
Which evaluation metric should you use?

Select 1 option(s):

You configure the Hyperdrive experiment by running the following code:

For each of the following statements, select Yes if the statement is true. Otherwise, select No.


Select 1 option(s):

The project you are working in tracks the health and migration of birds. You create a multi-class image classification deep learning model that uses a set of labeled bird photographs collected by experts.
You have 100,000 photographs of birds. All photographs use the JPG format and are stored in an Azure blob container in an Azure subscription.
You need to access the bird photograph files in the Azure blob container from the Azure Machine Learning service workspace that will be used for deep learning model training.
You must minimize data movement. What should you do?

Select 1 option(s):

You want to understand how your model is making selections and must be sure that the model does not violate government regulations such as denying loans based on where an applicant lives.
You need to determine the extent to which each feature in the customer data is influencing predictions.
What should you do?

Select 1 option(s):

You create a deep learning model for image recognition on Azure Machine Learning service using GPU-based training.
You must deploy the model to a context that allows for real-time GPU-based inferencing.
You need to configure compute resources for model inferencing.
Which compute type should you use?

Select 1 option(s):

You are creating a classification model for a banking company to identify possible instances of credit card fraud.
You plan to create the model in Azure Machine Learning by using automated machine learning.
The training dataset that you are using is highly unbalanced.
You need to evaluate the classification model.
Which primary metric should you use?

Select 1 option(s):

You start by creating a linear regression model.
You need to evaluate the linear regression model.
Solution:
Use the following metrics: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error, Accuracy, Precision, Recall, F1 score, and AUC.

Does the solution meet the goal?

Select 1 option(s):

You use the Two-Class Neural Network module in Azure Machine Learning Studio to build a binary classification model.
You use the Tune Model Hyperparameters module to tune accuracy for the model.
You need to select the hyperparameters that should be tuned using the Tune Model Hyperparameters module.
Which two hyperparameters should you use? Each correct answer presents part of the solution.

Select 2 option(s):

You are with a time series dataset in Azure Machine Learning Studio.
You need to split your dataset into training and testing subsets by using the Split Data module.
Which splitting mode should you use?

Select 1 option(s):

So you plan to use automated machine learning to train a regression model.
You have data that has features which have missing values, and categorical features with few distinct values.
You need to configure automated machine learning to automatically impute missing values and encode categorical features as part of the training task.
Which parameter and value pair should you use in the AutoMLConfig class?

Select 1 option(s):

You define a Python variable named was that references the Azure Machine Learning workspace. You run the following Python code:

For each of the following statements, select Yes if the statement is true. Otherwise, select No.


Select 1 option(s):

You create a multi-class image classification deep learning model that uses the PyTorch deep learning framework.
You must configure Azure Machine Learning Hyperdrive to optimize the hyperparameters for the classification model.
You need to define a primary metric to determine the hyperparameter values that result in the model with the best accuracy score.
Which three actions must you perform? Each correct answer presents part of the solution.

Select 3 option(s):

The CNN model you build shows signs of overfitting.
You need to reduce overfitting and converge the model to an optimal fit.
Which two actions should you perform? Each correct answer presents a complete solution.

Select 2 option(s):

You create a batch inference pipeline by using the Azure ML SDK.
You run the pipeline by using the following code:

You need to monitor the progress of the pipeline execution.

What are two possible ways to achieve this goal?  Each correct answer presents a complete solution.
Option A:
Run the following code in a notebook:

Option B:
Use the Inference Clusters tab in Machine Learning Studio.
Option C:
Use the Activity log in the Azure portal for the Machine Learning workspace.
Option D:
Run the following code in a notebook:

Option E:
Run the following code and monitor the console output from the PipelineRun object:


Select 2 option(s):

You want to use Hyperdrive to find parameters that optimize the AUC metric for the model.
You configure a HyperDriveConfig for the experiment by running the following code:

You plan to use this configuration to run a script that trains a random forest model and then tests it with validation data.
The label values for the validation data are stored in a variable named y_test variable, and the predicted probabilities from the model are stored in a variable named y_predicted.
You need to add logging to the script to allow Hyperdrive to optimize hyperparameters for the AUC metric.
Solution: Run the following code:

Does the solution meet the goal?

Select 1 option(s):

You create a multi-class image classification deep learning model that uses a set of labeled images.
You create a script file named train.py that uses the PyTorch 1.3 framework to train the model.
You must run the script by using an estimator. The code must not require any additional Python libraries to be installed in the environment for the estimator.
The time required for model training must be minimized.
You need to define the estimator that will be used to run the script. Which estimator type should you use?

Select 1 option(s):

You are very much familiar with Azure Machine Learning Studio
You are performing a classification task in Azure Machine Learning Studio.
You must prepare balanced testing and training samples based on a provided data set.
You need to split the data with a 0.75:0.25 ratio.
Which value should you use for each parameter?

Select 1 option(s):

You need to write code to access the datastore from a notebook. How should you complete the code segment?
To answer, select the appropriate options in the answer area.

Select 1 option(s):

Select 2 option(s):

You are creating a new Azure Machine Learning pipeline using the designer.
The pipeline must train a model using data in a comma-separated values (CSV) file that is published on a website. You have not created a dataset for this file.
You need to ingest the data from the CSV file into the designer pipeline using the minimal administrative effort.
Which module should you add to the pipeline in Designer?

Select 1 option(s):

You are building a recurrent neural network to perform a binary classification.
The training loss, validation loss, training accuracy, and validation accuracy of each training epoch has been provided.
You need to identify whether the classification model is overfitted.
Which of the following is correct?

Select 1 option(s):

You have a comma-separated values (CSV) file containing data from which you want to train a classification model.
You are using the Automated Machine Learning interface in Azure Machine Learning studio to train the classification model.
You set the task type to Classification.
You need to ensure that the Automated Machine Learning process evaluates only linear models.
What should you do?

Select 1 option(s):

Overview
You are a data scientist in a company that provides data science for professional sporting events. Models will use global and local market data to meet the following business goals:
· Understand sentiment of mobile device users at sporting events based on audio from crowd reactions.
· Assess a user’s tendency to respond to an advertisement.
· Customize styles of ads served on mobile devices.
· Use video to detect penalty events

Current environment
· Media used for penalty event detection will be provided by consumer devices. Media may include images and videos captured during the sporting event and shared using social media. The images and videos will have varying sizes and formats.
· The data available for model building comprises of seven years of sporting event media. The sporting event media includes; recorded video transcripts or radio commentary, and logs from related social media feeds captured during the sporting events.
· Crowd sentiment will include audio recordings submitted by event attendees in both mono and stereo formats.

Penalty detection and sentiment
· Data scientists must build an intelligent solution by using multiple machine learning models for penalty event detection.
· Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines.
· Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation.
· Notebooks must execute with the same code on new Spark instances to recode only the source of the data.
· Global penalty detection models must be trained by using dynamic runtime graph computation during training.
· Local penalty detection models must be written by using BrainScript.
· Experiments for local crowd sentiment models must combine local penalty detection data.
· Crowd sentiment models must identify known sounds such as cheers and known catch phrases. Individual crowd sentiment models will detect similar sounds.
· All shared features for local models are continuous variables.
· Shared features must use double precision. Subsequent layers must have aggregate running mean and standard deviation metrics available.

Advertisements
During the initial weeks in production, the following was observed:
· Ad response rated declined.
· Drops were not consistent across ad styles.
· The distribution of features across training and production data are not consistent
Analysis shows that, of the 100 numeric features on user location and behavior, the 47 features that come from location sources are being used as raw features. A suggested experiment to remedy the bias and variance issue is to engineer 10 linearly uncorrelated features.
· Initial data discovery shows a wide range of densities of target states in training data used for crowd sentiment models.
· All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are running too slow.
· Audio samples show that the length of a catch phrase varies between 25%-47% depending on region
· The performance of the global penalty detection models shows lower variance but higher bias when comparing training and validation sets. Before implementing any feature changes, you must confirm the bias and variance using all training and validation cases.
· Ad response models must be trained at the beginning of each event and applied during the sporting event.
· Market segmentation models must optimize for similar ad response history.
· Sampling must guarantee mutual and collective exclusively between local and global segmentation models that share the same features.
· Local market segmentation models will be applied before determining a user’s propensity to respond to an advertisement.
· Ad response models must support non-linear boundaries of features.
· The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviated from 0.1 +1- 5%.
The ad propensity model uses cost factors shown in the following diagram:

The ad propensity model uses proposed cost factors shown in the following diagram:

Performance curves of current and proposed cost factor scenarios are shown in the following diagram:

You need to use the Python language to build a sampling strategy for the global penalty detection models.
How should you complete the code segment? To answer, select the appropriate options in the answer area.


Select 1 option(s):

You create a binary classification model by using Azure Machine Learning Studio.
You must tune hyperparameters by performing a parameter sweep of the model.
The parameter sweep must meet the following requirements:
  § iterate all possible combinations of hyperparameters
  § minimize computing resources required to perform the sweep

You need to perform a parameter sweep of the model. Which parameter sweep mode should you use?

Select 1 option(s):

Does the solution meet the goal?

Which module you should use for below step?
Step:
Train, evaluate, and compare

Select 1 option(s):

You are implementing a machine learning model to predict stock prices.
The model uses a PostgreSQL database and requires GPU processing.
You need to create a virtual machine that is pre-configured with the required tools.
What should you do?

Select 1 option(s):

You use the Azure Machine Learning service to create a tabular dataset named training_data.
You plan to use this dataset in a training script.
You create a variable that references the dataset using the following code:
training_ds = workspace.datasets.get(“training_data”)
You define an estimator to run the script.
You need to set the correct property of the estimator to ensure that your script can access the training_data dataset.
Which property should you set?

Select 1 option(s):

You train and register a model in your Azure Machine Learning workspace.
You must publish a pipeline that enables client applications to use the model for batch inferencing. You must use a pipeline with a single ParallelRunStep step that runs a Python inferencing script to get predictions from the input data.
You need to create the inferencing script for the ParallelRunStep pipeline step.
Which two functions should you include? Each correct answer presents part of the solution.

Select 2 option(s):

You create a multi-class image classification deep learning model.
You train the model by using PyTorch version 1.2.
You need to ensure that the correct version of PyTorch can be identified for the inferencing environment when the model is deployed.
What should you do?

Select 1 option(s):

You have a dataset that contains null rows.
You need to use the Clean Missing Data module in Azure Machine Learning Studio to identify and resolve the null and missing data in the dataset.
Which parameter should you use?

Select 1 option(s):