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Question: 34
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 using Azure Machine learning Studio to perform feature engineering on a dataset. You need to normalize values to produce a feature column grouped into bins.
Solution: Apply an Entropy Minimum Description Length (MDI) binning mode. Does the solution meet the goal?
A. Yes
B. No
Answer: A
Explanation:
Entropy MDL binning mode: This method requires that you select the column you want to predict and the column or columns that you want to group into bins. It then makes a pass over the data and attempts to determine the number of bins that minimizes the entropy. In other words, it chooses a number of bins that allows the data column to best predict the target column. It then returns the bin number associated with each row of your data in a column named <colname>quantized.
References:https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into- bins
Question: 35
You are conducting feature engineering to prepuce data for further analysis. The data includes seasonal patterns on inventory requirements.
You need to select the appropriate method to conduct feature engineering on the data. Which method should you use?
A. Exponential Smoothing (ETS) function.
B. One Class Support Vector Machine module
C. Time Series Anomaly Detection module
D. Finite Impulse Response (FIR) Filter module.
Answer: D
Question: 36
You are solving a classification task. The dataset is imbalanced.
You need to select an Azure Machine Learning Studio module to improve the classification accuracy. Which module should you use?
A. Fisher Linear Discriminant Analysis.
B. Filter Based Feature Selection
C. Synthetic Minority Oversampling Teachnique (SMOTE)
D. Permutation Feature Importance
Answer: A
Question: 37
DRAG DROP
You are producing a multiple linear regression model in Azure Machine learning Studio. Several independent variables are highly correlated.
You need to select appropriate methods for conducting elective feature engineering on all the data. 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.
Answer:
Question: 38
You are performing a filter based feature selection for a dataset 10 build a multi class classifies by using Azure Machine Learning Studio.
The dataset contains categorical features that are highly correlated to the output label column.
You need to select the appropriate feature scoring statistical method to identify the key predictors. Which method should you use?
A. Chi-squared
B. Spearman correlation
C. Kendall correlation
D. Person correlation
Answer: A
Question: 39
DRAG DROP
YOU have a data-set that contains over 150 features. You use the dataset to train a Support Vector Machine (SVM) binary classifirer.
You need to use the Permutation Feature Importance module in Azure Machine Learning Studio to compute a set of feature importance scores for the dataset.
In which order should you perform the actions? To answer move al actions from from the list of Actions to the answer area and arrange them in the correct order.
Answer:
Question: 40
HOTSPOT
You are creating a machine learning model in Python. The provided dataset contains several numerical columns and one text column.
•Biker
•Cars
•Vans
•Boats
You are building a regression model using the scikit- learn Python package.
You need to transform the text data to be compatible with the scikit-learn Python package
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.
Answer:
Question: 41
HOTSPOT
You create a binary classification model to predict whether a person has a disease. You need to detect possible classification errors.
Which error type should you choose for each description? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Question: 42
You plan to use a Data Science Virtual Machine (DSVM) with the open source deep learning frameworks Caffe2 and Theano. You need to select a pre configured DSVM to support the framework.
What should you create?
A. Data Science Virtual Machine for Linux (CentOS)
B. Data Science Virtual Machine for Windows 2012
C. Data Science Virtual Machine for Windows 2016
D. Geo AI Data Science Virtual Machine with ArcGIS
E. Data Science Virtual Machine for Linux (Ubuntu)
Answer: A
Question: 43
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?
A. Coefficient of determination
B. Recall
C. Precision
D. Mean absolute error
E. Root Mean Square Error
Answer: A
Explanation:
Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect fit. However, caution should be used in interpreting R2 values, as low values can be entirely normal and high values can be suspect.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model
Question: 44
You plan to use .a Deep learning Virtual Machine (DLVM) to train deep learning models using Compute Unified Device Architecture (CUDA) computations.
You need to configure the IXVM to support CUOA What should you implement?
A. Intel Software Guard Extensions (Intel SGX) technology
B. Solid State Drives (SSD)
C. Graphic Processing Unit (GPU)
D. Computer Processing Unit (CPU) speed increase by using overcloking
E. High Random Access Memory (RAM) configuration
Answer: B
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