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 is a flexible and scalable machine learning platform designed to automate the process of creating and deploying machine learning models. It’s an open-source project developed by the Apache Software Foundation, aimed at providing a high-level declarative language for machine learning algorithms.

Here’s a brief overview of key aspects:

1. Declarative Language: SystemML introduces a declarative language called Declarative Machine Learning (DML). DML allows users to specify machine learning algorithms at a high level of abstraction, making it easier to express complex algorithms without worrying about low-level implementation details.
2. Scalability and Efficiency: SystemML is built to handle large-scale machine learning tasks efficiently. It achieves scalability through its ability to automatically parallelize and distribute computations across clusters of computers. This makes it suitable for processing massive datasets commonly encountered in real-world machine learning applications.
3. Flexibility: One of the core strengths of SystemML is its flexibility. It supports a wide range of machine learning algorithms, including regression, classification, clustering, and more. Additionally, users can extend SystemML by implementing custom functions and algorithms using its flexible API.
4. Integration with Apache Spark: SystemML seamlessly integrates with Apache Spark, a popular distributed computing framework. By leveraging Spark’s distributed processing capabilities, SystemML can efficiently execute machine learning algorithms on large datasets stored in distributed file systems like Hadoop HDFS or cloud storage systems.
5. Ease of Use: Despite its power and scalability, SystemML strives to maintain ease of use for both novice and experienced users. Its high-level declarative language allows users to express machine learning algorithms concisely, while its integration with Apache Spark simplifies deployment on distributed clusters.
6. Community and Support: Being an Apache Software Foundation project, SystemML benefits from a large and active community of developers and users. This community support ensures that the platform is continuously improved, and users can find help and resources readily available.

In summary, Apache SystemML provides a powerful, scalable, and flexible platform for machine learning tasks, with a focus on ease of use and integration with existing big data infrastructure.