DP-100 V2

HOTSPOT

You plan to preprocess text from CSV files. You load the Azure Machine Learning Studio default stop words list.

You need to configure the Preprocess Text module to meet the following requirements:

✑ Ensure that multiple related words from a single canonical form.

✑ Remove pipe characters from text.

✑ Remove words to optimize information retrieval.

Which three options should you select? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

View Answer

Answer:

Explanation:

Box 1: Remove stop words

Remove words to optimize information retrieval.

Remove stop words: Select this option if you want to apply a predefined stopword list to the text column. Stop word removal is performed before any other processes.

Box 2: Lemmatization

Ensure that multiple related words from a single canonical form. Lemmatization converts multiple related words to a single canonical form

Box 3: Remove special characters

Remove special characters: Use this option to replace any non-alphanumeric special characters with the pipe | character.

References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-

reference/preprocess-text

DRAG DROP

You have an existing GitHub repository containing Azure Machine Learning project files.

You need to clone the repository to your Azure Machine Learning shared workspace file system.

Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order. NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.

View Answer

Answer:

HOTSPOT

You need to set up the Permutation Feature Importance module according to the model training requirements.

Which properties should you select? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

View Answer

Answer:

Explanation:

Box 1: Accuracy

Scenario: You want to configure hyperparameters in the model learning process to speed the learning phase by using hyperparameters. In addition, this configuration should cancel the lowest performing runs at each evaluation interval, thereby directing effort and resources towards models that are more likely to be successful.

Box 2: R-Squared

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: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error, Relative Squared Error, and the Coefficient of Determination.

Does the solution meet the goal?
A . Yes
B. No

View Answer

Answer: A

Explanation:

The following metrics are reported for evaluating regression models. When you compare models, they are ranked by the metric you select for evaluation.

Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.

Root mean squared error (RMSE) creates a single value that summarizes the error in the model. By squaring the difference, the metric disregards the difference between over-prediction and under-prediction.

Relative absolute error (RAE) is the relative absolute difference between expected and actual values; relative because the mean difference is divided by the arithmetic mean.

Relative squared error (RSE) similarly normalizes the total squared error of the predicted values by dividing by the total squared error of the actual values.

Mean Zero One Error (MZOE) indicates whether the prediction was correct or not. In other words: ZeroOneLoss(x,y) = 1 when x!=y; otherwise 0.

Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect fit. However, caution should be used in interpreting R2 values, as low values can be entirely normal and high values can be suspect. AUC.

References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model

DRAG DROP

You need to implement source control for scripts in an Azure Machine Learning workspace. You use a terminal window in the Azure Machine Learning Notebook tab

You must authenticate your Git account with SSH.

You need to generate a new SSH key.

Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them m the correct order.

View Answer

Answer:

HOTSPOT

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? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

View Answer

Answer:

Explanation:

Box 1: Split rows

Use the Split Rows option if you just want to divide the data into two parts. You can specify the percentage of data to put in each split, but by default, the data is divided 50-50.

You can also randomize the selection of rows in each group, and use stratified sampling. In stratified sampling, you must select a single column of data for which you want values to be apportioned equally among the two result datasets.

Box 2: 0.75

If you specify a number as a percentage, or if you use a string that contains the "%" character, the value is interpreted as a percentage. All percentage values must be within the range (0, 100), not including the values 0 and 100.

Box 3: Yes

To ensure splits are balanced.

Box 4: No

If you use the option for a stratified split, the output datasets can be further divided by subgroups, by selecting a strata column.

You deploy a model as an Azure Machine Learning real-time web service using the following code.

The deployment fails.

You need to troubleshoot the deployment failure by determining the actions that were performed during deployment and identifying the specific action that failed.

Which code segment should you run?
A . service.get_logs()
B. service.state
C. service.serialize()
D. service.update_deployment_state()

View Answer

Answer: A

Explanation:

You can print out detailed Docker engine log messages from the service object. You can view the log for ACI, AKS, and Local deployments. The following example demonstrates how to print the logs.

# if you already have the service object handy print(service.get_logs())

# if you only know the name of the service (note there might be multiple services with the same name but different version number) print(ws.webservices[‘mysvc’].get_logs())

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment

You use the following code to define the steps for a pipeline:

from azureml.core import Workspace, Experiment, Run

from azureml.pipeline.core import Pipeline

from azureml.pipeline.steps import PythonScriptStep

ws = Workspace.from_config()

. . .

step1 = PythonScriptStep(name="step1", …)

step2 = PythonScriptsStep(name="step2", …)

pipeline_steps = [step1, step2]

You need to add code to run the steps.

Which two code segments can you use to achieve this goal? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
A . experiment = Experiment(workspace=ws,
name=’pipeline-experiment’)
run = experiment.submit(config=pipeline_steps)
B. run = Run(pipeline_steps)
C. pipeline = Pipeline(workspace=ws, steps=pipeline_steps) experiment = Experiment(workspace=ws, name=’pipeline-experiment’) run = experiment.submit(pipeline)
D. pipeline = Pipeline(workspace=ws, steps=pipeline_steps)
run = pipeline.submit(experiment_name=’pipeline-experiment’)

View Answer

Answer: C,D

Explanation:

After you define your steps, you build the pipeline by using some or all of those steps.

# Build the pipeline. Example:

pipeline1 = Pipeline(workspace=ws, steps=[compare_models])

# Submit the pipeline to be run

pipeline_run1 = Experiment(ws, ‘Compare_Models_Exp’).submit(pipeline1)

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-machine-learning-pipelines

You create an Azure Machine Learning workspace.

You must create a custom role named DataScientist that meets the following requirements:

✑ Role members must not be able to delete the workspace.

✑ Role members must not be able to create, update, or delete compute resource in the workspace.

✑ Role members must not be able to add new users to the workspace.

You need to create a JSON file for the DataScientist role in the Azure Machine Learning workspace.

The custom role must enforce the restrictions specified by the IT Operations team.

Which JSON code segment should you use?

A)

B)

C)

D)

A . Option A
B. Option B
C. Option C
D. Option D

View Answer

Answer: A

Explanation:

The following custom role can do everything in the workspace except for the following actions:

✑ It can’t create or update a compute resource.

✑ It can’t delete a compute resource.

✑ It can’t add, delete, or alter role assignments.

✑ It can’t delete the workspace.

To create a custom role, first construct a role definition JSON file that specifies the permission and scope for the role.

The following example defines a custom role named "Data Scientist Custom" scoped at a specific workspace level:

data_scientist_custom_role.json :

{

"Name": "Data Scientist Custom",

"IsCustom": true,

"Description": "Can run experiment but can’t create or delete compute.",

"Actions": ["*"],

"NotActions": [

"Microsoft.MachineLearningServices/workspaces/*/delete",

"Microsoft.MachineLearningServices/workspaces/write",

"Microsoft.MachineLearningServices/workspaces/computes/*/write",

"Microsoft.MachineLearningServices/workspaces/computes/*/delete",

"Microsoft.Authorization/*/write"

],

"AssignableScopes": [

"/subscriptions/<subscription_id>/resourceGroups/<resource_group_name>/providers/Micr osoft.MachineLearningServices/workspaces/<workspace_name>" ]

}

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-assign-roles

DRAG DROP

You need to correct the model fit issue.

Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

View Answer

Answer:

Explanation:

Step 1: Augment the data

Scenario: Columns in each dataset contain missing and null values. The datasets also contain many outliers.

Step 2: Add the Bayesian Linear Regression module.

Scenario: You produce a regression model to predict property prices by using the Linear Regression and Bayesian Linear Regression modules.

Step 3: Configure the regularization weight.

Regularization typically is used to avoid overfitting. For example, in L2 regularization weight, type the value to use as the weight for L2 regularization. We recommend that you use a non-zero value to avoid overfitting.

Scenario:

Model fit: The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.