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: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute

Error, Accuracy, Precision, Recall, F1 score, and AUC.

Does the solution meet the goal?

Options :

A : Yes

B : No

Answer: B

Question 2

You create a pipeline in designer to train a model that predicts automobile prices.

Because of non-linear relationships in the data, the pipeline calculates the natural log (Ln) of the prices in the

training data, trains a model to predict this natural log of price value, and then calculates the exponential of the

scored label to get the predicted price.

The training pipeline is shown in the exhibit. (Click the Training pipeline tab.)

Training pipeline

You create a real-time inference pipeline from the training pipeline, as shown in the exhibit. (Click the

Real-time pipeline tab.)

Real-time pipeline

You need to modify the inference pipeline to ensure that the web service returns the exponential of the scored

label as the predicted automobile price and that client applications are not required to include a price value in

the input values.

Which three modifications must you make to the inference pipeline? Each correct answer presents part of the

solution.

NOTE: Each correct selection is worth one point.

Options :

A : Connect the output of the Apply SQL Transformation to the Web Service Output module.

B : Replace the Web Service Input module with a data input that does not include the price column.

C : Add a Select Columns module before the Score Model module to select all columns other than price.

D : Replace the training dataset module with a data input that does not include the price column.

E : Remove the Apply Math Operation module that replaces price with its natural log from the data flow.

F : Remove the Apply SQL Transformation module from the data flow.

Answer: A,C,E

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 a model to forecast weather conditions based on historical data.

You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed

data to a machine learning model training script.

Solution: Run the following code:

Does the solution meet the goal?

Options :

A : Yes

B : No

Answer: B

Question 4

You need to select an environment that will meet the business and data requirements.

Which environment should you use?

Options :

A : Azure HDInsight with Spark MLlib

B :

Azure Cognitive Services

C : Azure Machine Learning Studio

D : Microsoft Machine Learning Server

Answer: D

Question 5

This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements. You have been tasked with evaluating your model on a partial data sample via k-fold cross-validation. You have already configured a k parameter as the number of splits. You now have to configure the k parameter for the cross-validation with the usual value choice. Recommendation: You configure the use of the value k=1. Will the requirements be satisfied?

Options :

A : Yes

B : No

Answer: B