Question 1

You use the Azure Machine Learning SDK to run a training experiment that trains a classification model and

calculates its accuracy metric.

The model will be retrained each month as new data is available.

You must register the model for use in a batch inference pipeline.

You need to register the model and ensure that the models created by subsequent retraining experiments are

registered only if their accuracy is higher than the currently registered model.

What are two possible ways to achieve this goal? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

Options :

A : Specify a different name for the model each time you register it.

B :

Register the model with the same name each time regardless of accuracy, and always use the latest version of the model in the batch inferencing pipeline

C :

Specify the model framework version when registering the model, and only register subsequent models if this value is higher.

D :

Specify a property named accuracy with the accuracy metric as a value when registering the model, and only register subsequent models if their accuracy is higher than the accuracy property value of the currently registered model.

E :

Specify a tag named accuracy with the accuracy metric as a value when registering the model, and only register subsequent models if their accuracy is higher than the accuracy tag value of the currently registered model.

Answer: D,E

Question 2

You register a model in an Azure Machine Learning workspace by running the following code:

You are creating a scoring script to use in a real-time service for the model.

You need to write code in the scoring script to set the path of the registered model so that it can be loaded by the service. You include the necessary import statements.

Which code segment should you use?

Options :

A : path = Model.get_model_path(‘loan_model’)

B : path = ‘model.pkl’

C : path = ws.models(‘loan_model’)

D : path = ‘outputs/model.pkl’

Answer: A

Question 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 a data scientist using Azure Machine Learning Studio.

You need to normalize values to produce an output column into bins to predict a target column.

Solution: Apply an Equal Width with Custom Start and Stop binning mode.

Does the solution meet the goal?

Options :

A : Yes

B : No

Answer: B

Question 4

You are performing clustering by using the K-means algorithm.

You need to define the possible termination conditions.

Which three conditions can you use? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

Options :

A : Centroids do not change between iterations.

B : The residual sum of squares (RSS) rises above a threshold.

C : The residual sum of squares (RSS) falls below a threshold.

D : A fixed number of iterations is executed.

E : The sum of distances between centroids reaches a maximum.

Answer: A,C,D

Question 5

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?

Options :

A : TensorFlow

B : PyTorch

C : SKLearn

D : Estimator

Answer: B

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