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

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..

**Also visit: ** Practice exam covering Course 4: Perform data science with Azure Databricks Quiz Answers

## 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)**

**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