- (Exam Topic 3)
You are using Azure Machine Learning to train machine learning models. You need a compute target on which to remotely run the training script. You run the following Python code:
Solution:
Box 1: Yes
The compute is created within your workspace region as a resource that can be shared with other users. Box 2: Yes
It is displayed as a compute cluster. View compute targets
* 1. To see all compute targets for your workspace, use the following steps:
* 2. Navigate to Azure Machine Learning studio.
* 3. Under Manage, select Compute.
* 4. Select tabs at the top to show each type of compute target.
Box 3: Yes
min_nodes is not specified, so it defaults to 0. Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.amlcompute.amlcomputeprovi https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio
Does this meet the goal?
Correct Answer:
A
- (Exam Topic 3)
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are creating a model to predict the price of a student’s artwork depending on the following variables: the student’s length of education, degree type, and art form.
You start by creating a linear regression model. You need to evaluate the linear regression model.
Solution: Use the following metrics: Accuracy, Precision, Recall, F1 score and AUC. Does the solution meet the goal?
Correct Answer:
B
Those are metrics for evaluating classification models, instead use: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error, Relative Squared Error, and the Coefficient of Determination.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model
- (Exam Topic 3)
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are using Azure Machine Learning to run an experiment that trains a classification model.
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.
Solution: Run the following code:
Does the solution meet the goal?
Correct Answer:
A
- (Exam Topic 3)
You plan to create a speech recognition deep learning model. The model must support the latest version of Python.
You need to recommend a deep learning framework for speech recognition to include in the Data Science Virtual Machine (DSVM).
What should you recommend?
Correct Answer:
B
TensorFlow is an open source library for numerical computation and large-scale machine learning. It uses Python to provide a convenient front-end API for building applications with the framework
TensorFlow can train and run deep neural networks for handwritten digit classification, image recognition, word embeddings, recurrent neural networks, sequence-to-sequence models for machine translation, natural language processing, and PDE (partial differential equation) based simulations.
References:
https://www.infoworld.com/article/3278008/what-is-tensorflow-the-machine-learning-library-explained.html
- (Exam Topic 3)
You are building an experiment using the Azure Machine Learning designer.
You split a dataset into training and testing sets. You select the Two-Class Boosted Decision Tree as the algorithm.
You need to determine the Area Under the Curve (AUC) of the model.
Which three modules should you use in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.
Solution:
Step 1: Train Model
Two-Class Boosted Decision Tree
First, set up the boosted decision tree model.
* 1. Find the Two-Class Boosted Decision Tree module in the module palette and drag it onto the canvas.
* 2. Find the Train Model module, drag it onto the canvas, and then connect the output of the Two-Class Boosted Decision Tree module to the left input port of the Train Model module.
The Two-Class Boosted Decision Tree module initializes the generic model, and Train Model uses training data to train the model.
* 3. Connect the left output of the left Execute R Script module to the right input port of the Train Model
module (in this tutorial you used the data coming from the left side of the Split Data module for training). This portion of the experiment now looks something like this:
Step 2: Score Model
Score and evaluate the models
You use the testing data that was separated out by the Split Data module to score our trained models. You can then compare the results of the two models to see which generated better results.
Add the Score Model modules
* 1. Find the Score Model module and drag it onto the canvas.
* 2. Connect the Train Model module that's connected to the Two-Class Boosted Decision Tree module to the left input port of the Score Model module.
* 3. Connect the right Execute R Script module (our testing data) to the right input port of the Score Model module.
Step 3: Evaluate Model
To evaluate the two scoring results and compare them, you use an Evaluate Model module.
* 1. Find the Evaluate Model module and drag it onto the canvas.
* 2. Connect the output port of the Score Model module associated with the boosted decision tree model to the left input port of the Evaluate Model module.
* 3. Connect the other Score Model module to the right input port.
Does this meet the goal?
Correct Answer:
A