DP-100 Online Practice Questions and Answers

Correct Answer:

Step 1: Augment the data

Scenario: Columns in each dataset contain missing and null values. The datasets also contain many outliers.

Step 2: Add the Bayesian Linear Regression module.

Scenario: You produce a regression model to predict property prices by using the Linear Regression and Bayesian Linear Regression modules.

Step 3: Configure the regularization weight.

Regularization typically is used to avoid overfitting. For example, in L2 regularization weight, type the value to use as the weight for L2 regularization. We recommend that you use a non-zero value to avoid overfitting.

Scenario:

Model fit: The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.

Incorrect Answers:

Multiclass Decision Jungle module:

Decision jungles are a recent extension to decision forests. A decision jungle consists of an ensemble of decision directed acyclic graphs (DAGs).

L-BFGS:

L-BFGS stands for "limited memory Broyden-Fletcher-Goldfarb-Shanno". It can be found in the wwo-Class Logistic Regression module, which is used to create a logistic regression model that can be used to predict two (and only two)

outcomes.

References:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regr ession

Correct Answer: C

Use the Convert to ARFF module in Azure Machine Learning Studio, to convert datasets and results in Azure Machine Learning to the attribute-relation file format used by the Weka toolset. This format is known as ARFF.

The ARFF data specification for Weka supports multiple machine learning tasks, including data preprocessing, classification, and feature selection. In this format, data is organized by entites and their attributes, and is contained in a single text file.

References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/convert-to-arff

Correct Answer: B

You can list all of the files that are associated with this run record by called run.get_file_names()

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-track-experiments

Correct Answer: B

One can inspect the true positive rate vs. the false positive rate in the Receiver Operating Characteristic (ROC) curve and the corresponding Area Under the Curve (AUC) value. The closer this curve is to the upper left corner, the better the

classifier's performance is (that is maximizing the true positive rate while minimizing the false positive rate). Curves that are close to the diagonal of the plot, result from classifiers that tend to make predictions that are close to random

guessing.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance#evaluating- a-binary-classification-model

Correct Answer: B

Correct Answer: A

Use the SMOTE module in Azure Machine Learning Studio to increase the number of underrepresented 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.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote

Correct Answer: A

Correct Answer: D

Correct Answer: D

Correct Answer: C

Manage packages

Since your compute instance has multiple kernels, make sure use %pip or %conda magic functions, which install packages into the currently running kernel. Don't use !pip or !conda, which refers to all packages (including packages outside

the currently running kernel).

Note: Code completion (IntelliSense)

IntelliSense is a code-completion aid that includes many features: List Members, Parameter Info, Quick Info, and Complete Word. With only a few keystrokes, you can:

Learn more about the code you're using

Keep track of the parameters you're typing

Add calls to properties and methods

Reference:

https://learn.microsoft.com/en-us/azure/machine-learning/how-to-run-jupyter-notebooks

https://code.visualstudio.com/docs/editor/intellisense