**Physical Address**

304 North Cardinal St.

Dorchester Center, MA 02124

Spark provides a machine learning library known as MLlib. Spark MLlib provides various machine learning algorithms such as classification, regression, clustering, and collaborative filtering. It also provides tools such as featurization, pipelines, persistence, and utilities for handling linear algebra operations, statistics and data handling. This course will start you off on your journey and walk you through some of the machine learning libraries and how to use them.

**Module 1 – Spark MLlib Data Types**

**Question: Sparse Data generally contains many non-zero values, and few zero values.**

- True
**False**

**Question: Local matrices are generally stored in distributed systems and rarely on single machines.**

- True
**False**

**Question: Which of the following are distributed matrices?**

- RowMatrix
- ColumnMatrix
- CoordinateMatrix
- SphericalMatrix
- RowMatrix and CoordinateMatrix
- All of the Above

**Module 2 – Review of Algorithms**

**Question: Logistic Regression is an algorithm used for predicting numerical values.**

- True
**False**

**Question: The SVM algorithm maximizes the margins between the generated hyperplane and two clusters of data.**

**True**- False

**Question: Which of the following is true about Gaussian Mixture Clustering?**

- The closer a data point is to a particular centroid, the more likely that data point is to be clustered with that centroid.
- The Gaussian of a centroid determines the probability that a data point is clustered with that centroid.
- The probability of a data point being clustered with a centroid is a function of distance from the point to the centroid.
- Gaussian Mixture Clustering uses multiple centroids to cluster data points.
- All of the Above

**Module 3 – Spark MLlib Decision Trees and Random Forests**

**Question: Which of the following is a stopping parameter in a Decision Tree?**

- The number of nodes in the tree reaches a specific value.
- The depth of the tree reaches a specific value.
- The breadth of the tree reaches a specific value.
- All of the Above

**Question: When using a regression type of Decision Tree or Random Forest, the value for impurity can be measured as either ‘entropy’ or ‘variance’.**

- True
**False**

**Question: In a Random Forest, featureSubsetStrategy is considered a stopping parameter, but not a tunable parameter.**

- True
**False**

**Module 4 – Spark MLlib Clustering**

**Question: In Spark MLlib, the initialization mode for the K-Means training method is called**

- k-means–
- k-means++
- k-means||
- k-means

**Question: In K-Means, the “runs” parameter determines the number of data points allowed in each cluster.**

- True
**False**

**Question: In Gaussian Mixture Clustering, the sum of all values outputted from the “weights” function must equal 1.**

**True**- False

**Final Exam**

**Question: In Gaussian Mixture Clustering, the predictSoft function provides membership values from the top three Gaussians only.**

- True
**False**

**Question: In Decision Trees, what is true about the size of a dataset?**

- Large datasets create “bins” on splits, which can be specified with the maxBins parameter.
- Large datasets sort feature values, then use the ordered values as split calculations.
- Small datasets create split candidates based on quantile calculations on a sample of the data.
- Small datasets split on random values for the feature.

**Question: A Logistic Regression algorithm is ineffective as a binary response predictor.**

- True
**False**

**Question: What is the Row Pointer for a Matrix with the following Row Indices: [5, 1 | 6 | 2, 8, 10]**

- [1, 6]
- [0, 2, 3, 6]
- [0, 2, 3, 5]
- [2, 3]

**Question: For multiclass classification, try to use (M-1) Decision Tree split candidates whenever possible.**

- True
**False**

**Question: In a Decision Tree, choosing a very large maxDepth value can:**

- Increase accuracy
- Increase the risk of overfitting to the training set
- Increase the cost of training
- All of the Above
- Increase the risk of overfitting and increase the cost of training

**Question: In Gaussian Mixture Clustering, a large value returned from the weights function represents a large precedence of that Gaussian.**

**True**- False

**Question: Increasing the value of epsilon when creating the K-Means Clustering model can:**

- Decrease training cost and decrease the number of iterations that the model undergoes
- Decrease training cost and increase the number of iterations that the model undergoes
- Increase training cost and decrease the number of iterations that the model undergoes
- Increase training cost and increase the number of iterations that the model undergoes

**Question: In order to train a machine learning model in Spark MLlib, the dataset must be in the form of a(n)**

- Python List
- Textfile
- CSV file
- RDD

**Question: What is true about Dense and Sparse Vectors?**

- A Dense Vector can be created using a csc_matrix, and a Sparse Vector can be created using a Python List.
- A Dense Vector can be created using a SciPy csc_matrix, and a Sparse Vector can be created using a SciPy NumPy Array.
- A Dense Vector can be created using a Python List, and a Sparse Vector can be created using a SciPy csc_matrix.
- A Dense Vector can be created using a SciPy NumPy Array, and a Sparse Vector can be created using a Python List.

**Question: In a Decision Tree, increaing the maxBins parameter allows for more splitting candidates.**

**True**- False

**Question: In classification models, the value for the numClasses parameter does not depend on the data, and can change to increase model accuracy.**

- True
**False**

**Question: What is true about Labeled Points?**

- A – A labeled point is used with supervised machine learning, and can be made using a dense local vector.
- B – A labeled point is used with unsupervised machine learning, and can be made using a dense local vector.
- C – A labeled point is used with supervised machine learning, and can be made using a sparse local vector.
- D – A labeled point is used with unsupervised machine learning, and can be made using a sparse local vector
- All of the Above
- A and C only

**Question: In the Gaussian Mixture Clustering model, the convergenceTol value is a stopping parameter that can be tuned, similar to epsilon in k-means clustering.**

**True**- False

**Question: In Gaussian Mixture Clustering, the “Gaussians” function outputs the coordinates of the largest Gaussian, as well as the standard deviation for each Gaussian in the mixture.**

- True
**False**

**Question: What is true about the maxDepth parameter for Random Forests?**

- A large maxDepth value is preferred since tree averaging yields a decrease in overall bias.
- A large maxDepth value is preferred since tree averaging yields a decrease in overall variance.
- A large maxDepth value is preferred since tree averaging yields an increase in overall bias.
- A large maxDepth value is preferred since tree averaging yields an increase in overall variance.

We hope you know the correct answers to **Spark MLlIB** If Queslers helped you to find out the correct answers then make sure to bookmark our site for more Course Quiz Answers.

If the options are not the same then make sure to let us know by leaving it in the comments below.

In our experience, we suggest you enroll in this and gain some new skills from Professionals completely free and we assure you will be worth it.

This course is available on **Cognitive Class** for free, if you are stuck anywhere between quiz or graded assessment quiz, just visit Queslers to get all Quiz Answers and Coding Solutions.

**More Courses Quiz Answers >>**

**Building Cloud Native and Multicloud Applications Quiz Answers**

**Accelerating Deep Learning with GPUs Quiz Answers**

**Blockchain Essentials Cognitive Class Quiz Answers**

**Deep Learning Fundamentals Cognitive Class Quiz Answers**

**Hadoop 101 Cognitive Class Answers**