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Practice exam covering Course 1: Create machine learning models Quiz Answers

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In this article i am gone to share Prepare for DP-100: Data Science on Microsoft Azure Exam | Week 2 | Practice exam covering Course 1: Create machine learning models Quiz Answers with you..

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Practice exam covering Course 1: Create machine learning models Quiz Answers

Question 1)
Your manager has asked you to 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 the following description?
“A person has a disease. The model classifies the case as having a disease”.

  • True negatives
  • False positives
  • False negatives
  • True positives

Question 2)
Your manager has asked you to 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 the following description?
“A person does not have a disease. The model classifies the case as having a disease”.

  • False positives
  • True positives
  • False negatives
  • True negatives

Question 3)
You are tasked to analyze a dataset containing historical data from a local taxi company. You are developing a regression model for this. Your goal is to predict the fare of a taxi trip. You need to select performance metrics to correctly evaluate the regression model.

Which two metrics can you use?

  • A Root Mean Square Error value that is low
  • An R-Squared value close to 0
  • An F1 score that is low
  • An R-Squared value close to 1

 

Question 4)
You are a data scientist of a company and you are tasked with building a deep convolutional neural network (CNN) for image classification. The CNN model you built shows signs of overfitting. You need to reduce overfitting and converge the model to an optimal fit.

Which two actions should you perform?

  • Add an additional dense layer with 512 input units
  • Reduce the amount of training data
  • Use training data augmentation
  • Add an additional dense layer with 64 input units
  • Add L1/L2 regularization

 

Question 5)
Your manager has provided you a dataset created for multiclass classification tasks that contains a normalized numerical feature set with 10,000 data points and 150 features. You use 75 percent of the data points for training and 25 percent for testing.
You need to apply the Principal Component Analysis (PCA) method to reduce the dimensionality of the feature set to 10 features in both training and testing sets.
You are using the scikit-learn machine learning library in Python.
You use X to denote the feature set and Y to denote class labels.
You create the following Python data frames:

From sklearn.decomposition import PCA
pca – [...]
x_train=[...] .fit_transform(X_train)
x_test = pca.[...]

How should you complete the code segment?

  • Box1: PCA(n_components=10);
    Box2: pca;
    Box3: transform(x_test)

 

Question 6)
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: Relative Squared Error, Coefficient of Determination, Accuracy, Precision, Recall, F1 score, and AUC:

Does the solution meet the goal?

  • Yes
  • No

 

Question 7)
What happens when a NumPy array is multiplied by 5?

  • The new array will be 5 times longer, with the sequence repeated 5 times and also all the elements are multiplied by 5.
  • The new array will be 5 times longer, with the sequence repeated 5 times.
  • Array stays the same size, but each element is multiplied by 5.

 

Question 8)
You are creating a model and you want to evaluate it. One metric yields an absolute metric in the same unit as the label.

Which metric is described?

  • Root Mean Square Error (RMSE)
  • Mean Square Error (MSE)
  • Coefficient of Determination (known as R-squared or R2)

Question 9)
Complete the sentence:
Decision trees algorithms are examples of machine learning __________ type model.

  • Regression
  • Clustering
  • Classification

Question 10)
It is well known that Python provides extensive functionality with powerful and statistical numerical libraries. What is Scikit-learn useful for?

  • Providing attractive data visualizations
  • Analyzing and manipulating data
  • Supplying machine learning and deep learning capabilities
  • Offering simple and effective predictive data analysis

Question 11)
You are asked to use C-Support Vector classification to do a multi-class classification with an unbalanced training dataset. The C-Support Vector classification is using the Python code shown below:

from sklearn.svm import svc
import numpy as np
svc = SVC(kernel = ‘linear’, class_weight= ‘balanced’, c-1.0, random_state-0)
model1 = svc.fit(X_train, y)

You need to evaluate the C-Support Vector classification code. Which evaluation statement should you use?

  • class_weight=balanced: Automatically adjust weights inversely proportional to class frequencies in the input data
    C parameter: Penalty parameter

 

Question 12)
You are creating a model and you want to evaluate it. For this, you take a look on a specific metric which is direct proportional with how well the model fits.

Which evaluation model is described?

  • Mean Square Error (MSE)
  • Coefficient of Determination (known as R-squared or R2)
  • Root Mean Square Error (RMSE)

Question 13)
You are creating a binary classification by using a two-class logistic regression model. You need to evaluate the model results for imbalance. Which evaluation metric should you use?

  • Mean Absolute Error
  • Relative Squared Error
  • AUC Curve
  • Relative Absolute Error

Question 14)
What happens when a list is multiplied by 5?

  • The new list created has the length 5 times the original length with the sequence repeated 5 times.
  • The new list created has the length 5 times the original length with the sequence repeated 5 times and also all the elements are also multiplied by 5.
  • The new list remains the same size, but the elements are multiplied by 5.

Question 15)
You are a senior data scientist in the company and you are tasked with evaluating a completed binary classification machine learning model.
You need to use the precision as the evaluation metric. Which visualization should you use?

  • Gradient descent
  • Receiver Operating Characteristic (ROC) curve
  • Scatter plot
  • Violin plot

Question 16)
It is well known that Python provides extensive functionality with powerful and statistical numerical libraries. What is TensorFlow useful for?

  • Analyzing and manipulating data
  • Providing attractive data visualizations
  • Supplying machine learning and deep learning capabilities
  • Offering simple and effective predictive data analysis

Question 17)
Your manager has asked you to 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 the following description?
“A person does not have a disease. The model classifies the case as having no disease”.

  • False positives
  • True negatives
  • True positives
  • False negatives

Question 18)
Your manager has asked you to 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 the following description?
“A person has a disease. The model classifies the case as having no disease”.

  • True positives
  • False positives
  • True negatives
  • False negatives

Question 19)
Complete the sentence:
The Support Vector Machine algorithm is an example of machine learning __________ type model.

  • Classification
  • Regression
  • Clustering