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Predictive Modeling Fundamentals I Cognitive Class Exam Answers:-

Course Name:- Predictive Modeling Fundamentals I

Module 1. Introduction to Data Mining

Question 1. Which of the following applications would require the use of data mining? Select all that apply.

  • Predicting the outcome of flipping a fair coin
  • Determining which products in a store are likely to be purchased together
  • Predicting future stock prices using historical records
  • Determining the total number of products sold by a store
  • Sorting a student database by gender

Question 2. Which of the following is NOT a section of the Modeler Interface?

  • Stream Canvas
  • Nodes
  • Palettes
  • Stream, Outputs, and Model Manager
  • All of the above are sections of the Modeler Interface

Question 3. Which of the following is NOT a part of the Cross-Industry Process for Data Mining?

  • Data Storage
  • Evaluation
  • Data Preparation
  • Business Understanding
  • Modeling

Module 2. The Data Mining Process

Question 1. Which phase of the data mining process focuses on understanding the project requirements and objectives?

  • Business Understanding
  • Data Exploration
  • Data Preprocessing
  • Data Preparation
  • Data Understanding

Question 2. Which Data Preprocessing task focuses on removing outliers and filling in missing values?

  • Data Reduction
  • Data Integration
  • Data Transformation
  • Data Cleaning
  • None of the above

Question 3. The IBM SPSS Modeler supports which data type?

  • Nominal
  • Ordinal
  • Categorical
  • Continuous
  • All of the above

Module 3. Modeling Techiques

Question 1. Which of the following methods are commonly used for supervised learning tasks? Select all that apply.

  • Neural Networks
  • Decision Trees
  • K-Means
  • CARMA
  • Regression

Question 2. Classification is a subset of supervised learning that focuses on modeling continuous variables. True or false?

  • True
  • False

Question 3. Which of the following algorithms is NOT supported by the SPSS Modeler?

  • K-Means
  • CARMA
  • Apriori
  • Logistic Regression
  • All of the above algorithms are supported

Module 4. Model Evaluation

Question 1. What is the term for a negative data point that is incorrectly classified as positive?

  • True Positive
  • False Positive
  • True Negative
  • False Negative
  • None of the above

Question 2. Which of the following is NOT a cost-sensitive performance metric?

  • Precision
  • Sensitivity
  • Accuracy
  • Specificity
  • All of the above metrics are cost-sensitive

Question 3. What is the formula for the precision metric?

  • (True Positive) / (True Positive + False Negative)
  • (True Positive) / (True Positive + False Positive)
  • (False Positive) / (True Positive + False Positive)
  • (False Positive) / (True Negative + True Positive)
  • (True Negative) / (True Negative + False Positive)

Module 5. Deployment  on IBM Bluemix

Question 1. In general, the testing dataset should be significantly larger than the training dataset. True or false?

  • True
  • False

Question 2. Which of the following is NOT a model deployment solution?

  • CRISP-DM
  • Bluemix
  • SPSS Solution Publisher
  • IBM Collaboration and Deployment Services
  • All of the above are model deployment solutions

Question 3. Which of the following statements are true of IBM Bluemix? Select all that apply.

  • Bluemix generally takes about a week to deploy an app
  • Bluemix is supported by a growing community
  • Bluemix is closed-source
  • Bluemix provides a self-service application-hosting environment
  • Bluemix provides built-in load-balancing capabilities

Predictive Modeling Fundamentals I Cognitive Class Final Exam Answers:-

Question 1. Which of the following suggests that the model is overfitting the data?

  • High accuracy on training data and high accuracy on testing data
  • Low accuracy on training data and high accuracy on testing data
  • Low accuracy on training data and low accuracy on testing data
  • High accuracy on training data and low accuracy on testing data
  • None of the above

Question 2. Which of the following tasks would require the use of data mining?

  • Predicting the outcome of rolling two fair dice
  • Determining which products in a store are likely to be purchased together
  • Sorting a customer database by age
  • Computing the number of products sold over a given time period
  • All of the above

Question 3. Suppose you have collected data on your customers and you wish to determine the demographics they fall into. Which technique is best suited for this task?

  • Neural Network
  • Logistic Regression
  • Clustering
  • Linear Regression
  • Decision Tree

Question 4. Suppose you wish to use data mining in order to determine which customers are most likely to sign up for a new service. Which technique is best suited for this task?

  • Apriori
  • Decision Tree
  • Sequence
  • K-means
  • CARMA

Question 5. Which SPSS Modeler node can be used to determine a model’s performance? Select all that apply.

  • Evaluation Node
  • Analysis Node
  • Table Node
  • Auto Classifier Node
  • Sequence Node

Question 6. Which of the following is NOT a classification or prediction algorithm in SPSS Modeler?

  • Linear Regression
  • Neural Network
  • Logistic Regression
  • Discriminant
  • Apriori

Question 7. Which SPSS Modeler node is used to specify whether a given field is an input or a target?

  • Auto Classifier Node
  • Data Audit Node
  • Table Node
  • Type Node
  • Analysis Node

Question 8. Which SPSS Modeler node is useful for exploratory analysis on a data set?

  • Analysis Node
  • Auto Classifier Node
  • Table Node
  • Data Audit Node
  • Evaluation Node

Question 9. Which SPSS Modeler node is used to both rename fields and exclude fields from the model?

  • Restructure Node
  • Filter Node
  • Partition Node
  • Data Audit Node
  • Evaluation Node

Question 10. What is the formula for the accuracy metric? TP = true positive, TN = true negative, FP = false positive, and FN = false negative.

  • TN / (TN + FP)
  • TP / (TP + FN)
  • TP / (TP + FP)
  • (FP + FN) / (TP + TN + FP + FN)
  • (TP + TN) / (TP + TN + FP + FN)

Question 11. Which major data preprocessing step focuses on feature selection and feature extraction?

  • Data Integration
  • Data Cleaning
  • Data Reduction
  • Data Audit
  • Data Transformation

Question 12. Which SPSS Modeler node is used to identify missing data and screen out potentially problematic fields?

  • Auto Data Preparation Node
  • Auto Classifier Node
  • Restructure Node
  • Evaluation Node
  • Data Audit Node

Question 13. SPSS Modeler provides automated tools that determine the best algorithm to use for an application. True or false?

  • True
  • False

Question 14. Which SPSS Modeler node is used for sampling the data set?

  • Type Node
  • Partition Node
  • Data Audit Node
  • Filter Node
  • Restructure Node

Question 15. Which phase of the data mining process focuses on gathering insights about the data set?

  • Data Integration
  • Data Understanding
  • Business Understanding
  • Data Preparation
  • Data Preprocessing

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