Machine Learning with Apache SystemML Cognitive Class Exam Quiz Answers

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Enroll Here: Machine Learning with Apache SystemML Cognitive Class Exam Quiz Answers

Machine Learning with Apache SystemML Cognitive Class Certification Answers

Module 1 – What is SystemML Quiz Answers – Cognitive Class

Question 1: In machine learning, as analytical models are exposed to new data, they are able to independently adapt. True or false?

True
False

Question 2: Which of the following are types of alternatives to SystemML?

R
MLlib
Spark R
Mahout
All of the above

Question 3: The R language was designed for machine learning and works great for big data. True or false?

True
False

Module 2 – SystemML and the Spark MLContext Quiz Answers – Cognitive Class

Question 1: What the ways you can use SystemML’s Spark MLContext?

spark-shell
Through an application using the API
Through the SystemML console
A notebook interface
None of the above

Question 2: You must pass in the reference of the SparkContext to the MLContext constructor. True or false?

True
False

Question 3: Why would you use the Spark MLContext?

Programmatic interface into SystemML’s libraries
To benefit from the optimizations that come with SystemML
When you need to convert the data to a binary block matrix
A and B only
None of the above

Module 3 – SystemML Algorithms Quiz Answers – Cognitive Class

Question 1: The Regression algorithm is an ensemble learning method that creates a model composed of a set of tree models for classification. True or false?

True
False

Question 2: K-means is an unsupervised learning algorithm used to assign a category label to each record so that each similar record tend to get the same label. True or false.

True
False

Question 3: The Kaplan-Meier algorithm predicts how likely it is someone will purchase a product of similar category. True or false?

True
False

Module 4 – Declarative Machine Learning (DML) Quiz Answers – Cognitive Class

Question 1: What does DML stand for?

Data manipulation language
Data machine language
Declarative machine learning
Declarative machine language

Question 2: To run a DML script, which of the following jar file is required at runtime?

MLContext.jar
DML.jar
SystemML.jar
spark-context.jar

Question 3: Which of the following way to pass command-line arguments is recommended?

positional arguments
named arguments
a comma separated list
a file

Module 5 – SystemML Architecture and Optimization Quiz Answers – Cognitive Class

Question 1: In the ALS performance comparison, at which dataset does the MLlib code run out of memory??

Large
Medium
Small
None

Question 2: Which of the following does NOT belong to the SystemML Optimizer stack?

Create the RDDs for the high-level algorithm
Compute memory estimates
Generate runtime program
Live variable analysis

Question 3: How does SystemML know it is better to run the code on one machine?

Advanced Rewrites
Propagation of statistics
Live variable analysis
Efficient runtime
The developer tells it to

Machine Learning with Apache SystemML Final Exam Answers – Cognitive Class

Question 1: What is machine learning?

Artificial intelligence for machines to make decisions
Same as data science to gather insight using machines
Enable computers to learn without being explicitly programmed
Learning about how machines operate

Question 2: What is the purpose of SystemML?

Programming language for big data
In-memory analytics engine
Machine learning for spark
Machine learning on hadoop
All of the above

Question 3: What are the challenges of machine learning on big data using R?

Programmers are needed to convert the high-level code to low level code for parallel computing
Each iteration of the code takes time to be rewritten and recompile
Chances for errors are higher during the translation of the algorithms
All of the above

Question 4: What is the vision of SystemML?

Run the same algorithm developed for small data on big data
Provide flexible algorithm of ML algorithms
Automatic generation of hybrid runtime plans
All of the above

Question 5: Which of the following languages is SystemML most similar?

R
Python
Java
Scala
Perl
R and Python
Java and Scala

Question 6: Which of the following line of code will launch the Spark shell with SystemML?

./bin/spark-shell –jars SystemML.jar
./bin/spark-shell –executor-memory 4G –jars SystemML.jar
./bin/spark-shell –driver-memory 4G –jars SystemML.jar
./bin/spark-shell –executor-memory 4G –driver-memory 4G –jars SystemML.jar
All of the above

Question 7: Why would you convert a DataFrame to a binary-block matrix?

To enable parallelization within the Spark engine
To use the rich set of APIs provided by the binary-block matrix
Allows algorithm performance to be measured separately from data conversion time
Allows a more efficient runtime processing of the data

Question 8: Which of the following is TRUE with regards to helper methods in SystemML?

SystemML’s output is encapsulated in the MLContext object
SystemML’s output is encapsulated in the MLOutput object
Helper methods retrieves the values from the MLOutput object
Helper methods retrieves the values from the MLContext object
A and D only
B and C only

Question 9: Which is NOT a benefit of using SystemML algorithms?

Run in parallel
It is faster than all other algorithms
No need for translation into a lower-level language
Algorithms are optimized based on data and cluster characteristics

Question 10: Which of the following classes of algorithms provide a recommendation?

Regression
Classification
Matrix Factorization
Descriptive statistics

Question 11: Which of the following algorithm can group a set of data into known categories?

Regression
Clustering
Survival Analysis
Classification

Question 12: Which of the following algorithm can be used for prediction, forecasting, or error reduction?

Clustering
Regression
Survival Analysis
Descriptive statistics

Question 13: Which of the following value typesis NOT supported in the DML language?

String
Double
Varchar
Boolean

Question 14: Matrix-vector operations avoids the need for creating replicated matrix for a certain subset of operations. True or false?

True
False

Question 15: Global variables cannot be access within a function. True or false?

True
False

Question 16: Which of the following are NOT types of categories of built-in functions in DML?

Derivative built-in functions
Matrix built-in functions
Statistical built-in functions
Casting built-in functions

Question 17: In the statistics propagation phase of the SystemML optimizer, what exactly is happening?

To determine the confidence level of the computed results
All the statistics is propagated to the top node to determine the most efficient runtime for query execution
To determine of probability of the operation succeeding within a given period of time
Find the widest matrix required and determine if it all fits into the heap.

Question 18: What is the benefit of doing the matrix rewrite?

Reduce the line of code needed to represent the matrix
To determine the confidence level of the computed results
Clean up and unused memory from the matrix
To enable parallelization of the given matrixithin a given period of time
Represent the final matrix without computing the intermediate matrices

Question 19: Which is NOT part of the SystemML runtime for Spark?

Automates critical performance decisions
Distributed vs. local runtime
Efficient linear algebra optimizations
Automated RDD caching
None of the above

Question 20: SystemML is an Apache open-source project. True or false

True
False

Introduction to Machine Learning with Apache SystemML

Apache SystemML (DML – Declarative Machine Learning) is an open-source machine learning platform that provides a high-level language for expressing machine learning algorithms. It is designed for scalable and distributed machine learning on big data platforms, such as Apache Hadoop and Apache Spark. Below are the key concepts and steps for performing machine learning with Apache SystemML:

1. Installation:

Install Apache SystemML on your cluster or local machine. You can download the binary distribution or build it from the source code.

2. Integration with Apache Spark:

Apache SystemML is often used in conjunction with Apache Spark. Ensure that your Spark environment is set up and integrated with SystemML.

3. Declarative Machine Learning (DML):

SystemML uses a high-level language called DML to express machine learning algorithms in a declarative manner. DML scripts define the workflow and operations of the machine learning tasks.

4. Script Execution:

Write DML scripts for your machine learning tasks. DML scripts can be executed in different modes, including local mode, Spark mode, and Hadoop mode. The execution mode depends on the scale and distribution of your data.

5. Distributed and Scalable Execution:

SystemML is designed for distributed and scalable execution. It automatically optimizes the execution plan based on the available resources and data distribution.

6. Built-in Algorithms:

SystemML provides a set of built-in algorithms that cover various machine learning tasks, such as linear regression, logistic regression, k-means clustering, and more.

7. Extensibility with Custom Functions:

You can extend SystemML by incorporating custom functions written in Java or Scala. This allows you to integrate your own algorithms or enhance existing functionalities.

8. Optimization Techniques:

SystemML employs various optimization techniques, including dynamic recompilation, statistics-based optimization, and cost-based optimization, to enhance the efficiency of machine learning tasks.