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QUESTION 41
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 have an Azure ML experiment that contains an intermediate dataset.
You need to explore data from the intermediate dataset by using Jupyter.
Solution: You add a Convert to CSV module to the Azure ML experiment and then open the module output in a new notebook.
Does this meet the goal?

A. Yes
B. No

Answer: A
Explanation:
Access intermediate datasets from Machine Learning experiments The following steps show an example that creates an experiment, runs it and accesses the intermediate dataset.
1. Create a new experiment.
2. Insert an Adult Census Income Binary Classification dataset module.
3. Insert a Split module, and connect its input to the dataset module output.
4. Insert a Convert to CSV module and connect its input to one of the Split module outputs.
5. Save the experiment, run it, and wait for it to finish running.
6. Click the output node on the Convert to CSV module.
7. When the context menu appears, select Generate Data Access Code
8. Select the code snippet and copy it to your clipboard from the window that appears..
9. Paste the code in your notebook.
Note: After an experiment is run in the Machine Learning Studio, it is possible to access the intermediate datasets from the output nodes of modules. Intermediate datasets are data that has been created and used for intermediate steps when a model tool has been run.
Intermediate datasets can be accessed as long as the data format is compatible with the Python client library.
The following formats are supported (constants for these are in the azureml.DataTypeIds class):
PlainText
GenericCSV
GenericTSV
GenericCSVNoHeader
GenericTSVNoHeader
https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/python-data-access
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QUESTION 42
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 have an Azure ML experiment that contains an intermediate dataset.
You need to explore data from the intermediate dataset by using Jupyter.
Solution: You add a web service input to retrieve the data for the data source, and then add the Execute R Script module.
Does this meet the goal?

A. Yes
B. No

Answer: B
Explanation:
Use a Convert to CSV module.
https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/python-data-access

QUESTION 43
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 have an Azure ML experiment that contains an intermediate dataset.
You need to explore data from the intermediate dataset by using Jupyter.
Solution: In Azure ML Studio, you use the Save as dataset option, and then open the output in a new notebook?
Does this meet the goal?

A. Yes
B. No

Answer: B
Explanation:
Use a Convert to CSV module.
https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/python-data-access

QUESTION 44
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 have an Azure ML experiment that contains an intermediate dataset.
You need to explore data from the intermediate dataset by using Jupyter.
Solution: You add a Convert to ARFF module, and then add the Execute R Script module.
Does this meet the goal?

A. Yes
B. No

Answer: B
Explanation:
Use a Convert to CSV module.
https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/python-data-access

QUESTION 45
Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
Start of repeated scenario
You plan to use Azure platform tools to detect and analyze food items in smart refrigerators. To provide families with an integrated experience for grocery shopping and cooking, the refrigerators will connect to other smart appliances, such as stoves and microwave ovens, on a LAN.
You plan to build an object recognition model by using the Microsoft Cognitive Toolkit. The object recognition model will receive input from the connected devices and send results to applications.
The training data will be derived from more than 10 TB of images. You will convert the raw images to the sparse format.
End of repeated scenario.
You need to preprocess the training data by using a Principal Component Analysis (PCA) algorithm in the least amount of time possible.
Which implementation method should you use?

A. Azure HDInsight using HiveML
B. Azure Machine Learning Studio and a custom Execute Python Script module
C. Azure HDInsight using Microsoft R Server
D. Azure Machine Learning Studio with a custom Execute R Script module

Answer: D
Explanation:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/principal- component-analysis

QUESTION 46
Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
Start of repeated scenario
You plan to use Azure platform tools to detect and analyze food items in smart refrigerators. To provide families with an integrated experience for grocery shopping and cooking, the refrigerators will connect to other smart appliances, such as stoves and microwave ovens, on a LAN.
You plan to build an object recognition model by using the Microsoft Cognitive Toolkit. The object recognition model will receive input from the connected devices and send results to applications.
The training data will be derived from more than 10 TB of images. You will convert the raw images to the sparse format.
End of repeated scenario.
You need to ensure that a web service endpoint can receive image data and use an object recognition model to return the expected object and the confidence level of the model.
The solution must minimize the effort required to generate the client code to access the web service.
Which resource should you use?

A. the edX Data Science Learning Dashboard
B. Azure Machine Learning Studio
C. Cortana Intelligence Gallery
D. the Data Science Virtual Machine

Answer: B
Explanation:
There are two ways to add an endpoint to a Web service.
Programmatically
Through the Azure Machine Learning Web Services portal
Adding an endpoint using the Azure Machine Learning Web Services portal
1. In Machine Learning Studio, on the left navigation column, click Web Services.
2. At the bottom of the Web service dashboard, click Manage endpoints. The Azure Machine Learning Web Services portal opens to the endpoints page for the Web service.
3. Click New.
4. Type a name and description for the new endpoint.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/create-endpoint

QUESTION 47
Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
Start of repeated scenario
You plan to use Azure platform tools to detect and analyze food items in smart refrigerators. To provide families with an integrated experience for grocery shopping and cooking, the refrigerators will connect to other smart appliances, such as stoves and microwave ovens, on a LAN.
You plan to build an object recognition model by using the Microsoft Cognitive Toolkit. The object recognition model will receive input from the connected devices and send results to applications.
The training data will be derived from more than 10 TB of images. You will convert the raw images to the sparse format.
End of repeated scenario.
You need to deploy a multiple-service solution that was developed already and published by other users in the Microsoft development community.
What should you use?

A. the edX Data Science Learning Dashboard
B. the Data Science Virtual Machine
C. an Azure Machine Learning experiment
D. Cortana Intelligence Gallery

Answer: D
Explanation:
Azure AI Gallery enables our growing community of developers and data scientists to share their analytics solutions.
https://gallery.azure.ai/

QUESTION 48
Note: This question is part of a series of questions that use the same or similar answer choices. An answer choice may be correct for more than one question in the series. Each question is independent of the other questions in this series. Information and details provided in a question apply only to that question.
You process some data by using Azure Machine Learning Studio. You have an intermediate dataset. The dataset has a column that contains date values stored in a format of MM/DD/YYYY.
You need to split the column into three separate columns by year, month, and day.
Which module should you use?

A. Edit Metadata
B. Normalize Data
C. Clean Missing Data
D. Import Data
E. Execute Python Script
F. Clip Values
G. Tune Model Hyperparameters
H. Select Columns in Dataset

Answer: E
Explanation:
You can use Python code to create a new column.
Incorrect Answers:
F: The Clip Values module in Azure Machine Learning Studio, is used to identify and optionally replace data values that are above or below a specified threshold.
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-transform-data

QUESTION 49
Note: This question is part of a series of questions that use the same or similar answer choices. An answer choice may be correct for more than one question in the series. Each question is independent of the other questions in this series. Information and details provided in a question apply only to that question.
You need to transform the columns in a dataset. The resulting data must be mean centered and have a variance of 1. The solution must use a native module.
Which module should you use?

A. Execute Python Script
B. Import Data
C. Edit Metadata
D. Select Columns in Dataset
E. Clean Missing Data
F. Tune Model Hyperparameters
G. Clip Values
H. Normalize Data

Answer: H

QUESTION 50
Note: This question is part of a series of questions that use the same or similar answer choices. An answer choice may be correct for more than one question in the series. Each question is independent of the other questions in this series. Information and details provided in a question apply only to that question.
You need to change a column name to a friendly name. The solution must use a native module.
Which module should you use?

A. Execute Python Script
B. Import Data
C. Edit Metadata
D. Select Columns in Dataset
E. Clean Missing Data
F. Tune Model Hyperparameters
G. Clip Values
H. Normalize Data

Answer: D
Explanation:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/select-columns-in-dataset

QUESTION 51
Note: This question is part of a series of questions that use the same or similar answer choices. An answer choice may be correct for more than one question in the series. Each question is independent of the other questions in this series. Information and details provided in a question apply only to that question.
You have a dataset that contains a column named Column1. Some of the values in Column1 are empty.
You need to replace the empty values by using probabilistic Principal Component Analysis (PCA). The solution must use a native module.
Which module should you use?

A. Execute Python Script
B. Import Data
C. Edit Metadata
D. Select Columns in Dataset
E. Clean Missing Data
F. Tune Model Hyperparameters
G. Clip Values
H. Normalize Data

Answer: E
Explanation:
You can use the Clean Missing Data module in Azure Machine Learning Studio, to remove, replace, or infer missing values.
This module supports multiple type of operations for “cleaning” missing values, including:
Replacing missing values with a placeholder, mean, or other value Completely removing rows and columns that have missing values Inferring values based on statistical methods
You can choose to replace using Probabilistic PCA: Replaces the missing values by using a linear model that analyzes the correlations between the columns and estimates a low-dimensional approximation of the data, from which the full data is reconstructed.
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data


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