The Secret Of Microsoft DP-100 Practice Exam

NEW QUESTION 1

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?

• A. Yes
• B. No

Answer: B

Explanation:
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

NEW QUESTION 2

You use the Azure Machine Learning designer to create and run a training pipeline.
The pipeline must be run every night to inference predictions from a large volume of files. The folder where the files will be stored is defined as a dataset.
You need to publish the pipeline as a REST service that can be used for the nightly inferencing run. What should you do?

• A. Create a batch inference pipeline
• B. Set the compute target for the pipeline to an inference cluster
• C. Create a real-time inference pipeline
• D. Clone the pipeline

Answer: A

Explanation:
Azure Machine Learning Batch Inference targets large inference jobs that are not time-sensitive. Batch Inference provides cost-effective inference compute scaling, with unparalleled throughput for asynchronous applications. It is optimized for high-throughput, fire-and-forget inference over large collections of data.
You can submit a batch inference job by pipeline_run, or through REST calls with a published pipeline. Reference:
https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/machine-learning-pipeli

NEW QUESTION 3

You use Data Science Virtual Machines (DSVMs) for Windows and Linux in Azure. You need to access the DSVMs.
Which utilities should you use? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

• A. Yes
• B. Not Mastered

Answer: A

NEW QUESTION 4

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.
NOTE: Each correct selection is worth one point.

• A. run(mini_batch) D
• B. main()
• C. batch()
• D. init()
• E. score(mini_batch)

Answer: AD

Explanation:
Reference:
https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/machine-learningpipelin

NEW QUESTION 5

You need to identify the methods for dividing the data according, to the testing requirements.
Which properties should you select? To answer, select the appropriate option-, m the answer area. NOTE: Each correct selection is worth one point.

• A. Yes
• B. Not Mastered

Answer: A

NEW QUESTION 6

You arc I mating a deep learning model to identify cats and dogs. You have 25,000 color images. You must meet the following requirements:
• Reduce the number of training epochs.
• Reduce the size of the neural network.
• Reduce over-fitting of the neural network.
You need to select the image modification values.
Which value should you use? To answer, select the appropriate Options in the answer area. NOTE: Each correct selection is worth one point.

• A. Yes
• B. Not Mastered

Answer: A

NEW QUESTION 7

You use the Azure Machine Learning designer to create and run a training pipeline. You then create a real-time inference pipeline.
You must deploy the real-time inference pipeline as a web service.
What must you do before you deploy the real-time inference pipeline?

• A. Run the real-time inference pipeline.
• B. Create a batch inference pipeline.
• C. Clone the training pipeline.
• D. Create an Azure Machine Learning compute cluster.

Answer: D

Explanation:
You need to create an inferencing cluster. Deploy the real-time endpoint
After your AKS service has finished provisioning, return to the real-time inferencing pipeline to complete deployment.
Select Deploy above the canvas.
Select Deploy new real-time endpoint.
Select the AKS cluster you created.
Select Deploy. Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-deploy

NEW QUESTION 8

You create a binary classification model. You need to evaluate the model performance.
Which two metrics can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

• A. relative absolute error
• B. precision
• C. accuracy
• D. mean absolute error
• E. coefficient of determination

Answer: BC

Explanation:
The evaluation metrics available for binary classification models are: Accuracy, Precision, Recall, F1 Score, and AUC.
Note: A very natural question is: ‘Out of the individuals whom the model, how many were classified correctly (TP)?’
This question can be answered by looking at the Precision of the model, which is the proportion of positives that are classified correctly.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance

NEW QUESTION 9

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?

• A. TensorFlow
• B. PyTorch
• C. SKLearn
• D. Estimator

Answer: B

Explanation:
For PyTorch, TensorFlow and Chainer tasks, Azure Machine Learning provides respective PyTorch, TensorFlow, and Chainer estimators to simplify using these frameworks.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-ml-models

NEW QUESTION 10

You write five Python scripts that must be processed in the order specified in Exhibit A – which allows the same modules to run in parallel, but will wait for modules with dependencies.
You must create an Azure Machine Learning pipeline using the Python SDK, because you want to script to create the pipeline to be tracked in your version control system. You have created five PythonScriptSteps and have named the variables to match the module names.

You need to create the pipeline shown. Assume all relevant imports have been done. Which Python code segment should you use?

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

Answer: A

Explanation:
The steps parameter is an array of steps. To build pipelines that have multiple steps, place the steps in order in this array.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-parallel-run-step

NEW QUESTION 11

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.

• A. Yes
• B. Not Mastered

Answer: A

NEW QUESTION 12

You need to build a feature extraction strategy for the local models.
How should you complete the code segment? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

• A. Yes
• B. Not Mastered

Answer: A

NEW QUESTION 13

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 new experiment in Azure Machine Learning Studio.
One class has a much smaller number of observations than the other classes in the training set. You need to select an appropriate data sampling strategy to compensate for the class imbalance. Solution: You use the Stratified split for the sampling mode.
Does the solution meet the goal?

• A. Yes
• B. No

Answer: B

Explanation:
Instead use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.
Note: SMOTE is used to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote

NEW QUESTION 14

You are using a Git repository to track work in an Azure Machine Learning workspace. You need to authenticate a Git account by using SSH.
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.

• A. Yes
• B. Not Mastered

Answer: A

NEW QUESTION 15

You need to define a modeling strategy for ad response.
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.

• A. Yes
• B. Not Mastered

Answer: A

NEW QUESTION 16

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?

• A. normalized_mean_absolute_error
• B. [spearman_correlation
• C. AUC.weighted
• D. accuracy
• E. normalized_root_mean_squared_error

Answer: C

Explanation:
AUC_weighted is a Classification metric.
Note: AUC is the Area under the Receiver Operating Characteristic Curve. Weighted is the arithmetic mean of the score for each class, weighted by the number of true instances in each class.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml

NEW QUESTION 17

You are planning to register a trained model in an Azure Machine Learning workspace.
You must store additional metadata about the model in a key-value format. You must be able to add new metadata and modify or delete metadata after creation.
You need to register the model. Which parameter should you use?

• A. description
• B. model_framework
• C. cags
• D. properties

Answer: D

Explanation:
azureml.core.Model.properties:
Dictionary of key value properties for the Model. These properties cannot be changed after registration, however new key value pairs can be added.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model

NEW QUESTION 18
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