DP-100: Designing and Implementing a Data Science Solution on Azure

Question 141

You are developing a deep learning model by using TensorFlow. You plan to run the model training workload on an Azure Machine Learning Compute Instance.
You must use CUDA-based model training.
You need to provision the Compute Instance.

Which two virtual machines sizes can you use?


Question 142

You are preparing to use the Azure ML SDK to run an experiment and need to create compute. You run the following code:

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


Question 143

You are using the Azure Machine Learning Python SDK, and you need to create a reference to the default datastore in your workspace.
You have written the following code:

--
from azureml.core import Workspace
#Get the workspace
ws = Workspace.from_config()
#Get the default datastore
--

Which line of code should you add?

Question 144

You are using the Azure Machine Learning Python SDK to run experiments.
You need to create an environment from a Conda configuration (.yml) file.
Which method of the Environment class should you use?

Question 145

You have registered an environment in your workspace, and retrieved it using the following code:

from azureml.core import Environment, Estimator
env = Environment.get(workspace=ws, name='my_environment')
estimator = Estimator(entry_script='training_script.py', ...)

You want to use the environment as the Python context for an experiment script that is run using an estimator.
Which property of the estimator should you set to assign the environment?

Question 146

You are building an intelligent solution using machine learning models.
The environment must support the following requirements:

• Data scientists must build notebooks in a cloud environment
• Data scientists must use automatic feature engineering and model building in machine learning pipelines.
• Notebooks must be deployed to retrain using Spark instances with dynamic worker allocation.
• Notebooks must be exportable to be version controlled locally.

You need to create the environment.
Which four actions should you perform in sequence?

Question 147

You are performing sentiment analysis using a CSV file that includes 12,000 customer reviews written in a short sentence format. You add the CSV file to Azure

Machine Learning Studio and configure it as the starting point dataset of an experiment. You add the Extract N-Gram Features from Text module to the experiment to extract key phrases from the customer review column in the dataset.

You must create a new n-gram dictionary from the customer review text and set the maximum n-gram size to trigrams.
What should you select? To answer, select the appropriate options in the answer area.


Question 148

You configure a Deep Learning Virtual Machine for Windows.
You need to recommend tools and frameworks to perform the following:

• Build deep neural network (DNN) models
• Perform interactive data exploration and visualization

Which two tools and frameworks should you recommend?


Question 149

You create an experiment in Azure Machine Learning Studio. You add a training dataset that contains 10,000 rows. The first 9,000 rows represent class 0 (90 percent).

The remaining 1,000 rows represent class 1 (10 percent).

The training set is imbalances between two classes. You must increase the number of training examples for class 1 to 4,000 by using 5 data rows. You add the Synthetic Minority Oversampling Technique (SMOTE) module to the experiment.

You need to configure the module.
Which values should you use?


Question 150

You are creating an experiment by using Azure Machine Learning Studio.
You must divide the data into four subsets for evaluation. There is a high degree of missing values in the data. You must prepare the data for analysis.
You need to select appropriate methods for producing the experiment.

Which three modules should you run in sequence?

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