Question 1

You have an Azure Machine Learning workspace.

You plan to tune a model hyperparameter when you train the model.

You need to define a search space that returns a normally distributed value.

Which parameter should you use?

Options :

A : QUniform

B : LogUniform

C : Uniform

D : LogNormal

Answer: D

Question 2

You are a data scientist working for a bank and have used Azure ML to train and register a machine learning

model that predicts whether a customer is likely to repay a loan.

You want to understand how your model is making selections and must be sure that the model does not violate

government regulations such as denying loans based on where an applicant lives.

You need to determine the extent to which each feature in the customer data is influencing predictions.

What should you do?

Options :

A : Enable data drift monitoring for the model and its training dataset.

B : Score the model against some test data with known label values and use the results to calculate a confusion matrix.

C : Use the Hyperdrive library to test the model with multiple hyperparameter values.

D : Use the interpretability package to generate an explainer for the model.

E : Add tags to the model registration indicating the names of the features in the training dataset.

Answer: D

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 create an Azure Machine Learning service datastore in a workspace. The datastore contains the following files:

✑ /data/2018/Q1.csv

✑ /data/2018/Q2.csv

✑ /data/2018/Q3.csv

✑ /data/2018/Q4.csv

✑ /data/2019/Q1.csv

All files store data in the following format:

id,f1,f2,I

1,1,2,0

2,1,1,1

3,2,1,0

4,2,2,1

You run the following code:

You need to create a dataset named training_data and load the data from all files into a single data frame by using the following code:

Solution: Run the following code:

Does the solution meet the goal?

Options :

A : Yes

B : No

Answer: A

Question 4

You use the following code to run a script as an experiment in Azure Machine Learning:

You must identify the output files that are generated by the experiment run.

You need to add code to retrieve the output file names.

Which code segment should you add to the script?

Options :

A : files = run.get_properties()

B : files= run.get_file_names()

C : files = run.get_details_with_logs()

D : files = run.get_metrics()

E :

files = run.get_details()

Answer: B

Question 5

You are a data scientist creating a linear regression model.

You need to determine how closely the data fits the regression line.

Which metric should you review?

Options :

A : Coefficient of determination

B : Recall

C : Precision

D : Mean absolute error

E : Root Mean Square Error

Answer: A

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