Data Science with Scala Cognitive Class Exam Quiz Answers

Related Articles

Saylor Academy English as a Second Language Certification Exam Answers

2 weeks ago

Saylor Academy ESL005: Business-Proficient English as a Second Language Exam Answers

2 weeks ago

Saylor Academy ESL004: Advanced English as a Second Language Certification Exam Answers

2 weeks ago

Enroll Now: Data Science with Scala Cognitive Class Exam Quiz Answers

Data Science with Scala Cognitive Class Certification Answersw

Module 1: Basic Statistics and Data Types

Question 1: You import MLlib’s vectors from?

• org.apache.spark.mllib.TF
• org.apache.spark.mllib.numpy
• org.apache.spark.mllib.linalg
• org.apache.spark.mllib.pandas

Question 2: Select the types of distributed Matrices:

• RowMatrix
• IndexedRowMatrix
• CoordinateMatrix

Question 3: How would you caculate the mean of the following?

val observations: RDD[Vector] = sc.parallelize(Array(

Vectors.dense(1.0, 2.0),

Vectors.dense(4.0, 5.0),

Vectors.dense(7.0, 8.0)))

val summary: MultivariateStatisticalSummary = Statistics.colStats(observations)

• summary.normL1
• summary.numNonzeros
• summary.mean
• summary.normL2

Question 4: What task does the following lines of code?

import org.apache.spark.mllib.random.RandomRDDs._

val million = poissonRDD(sc, mean=1.0, size=1000000L, numPartitions=10)

• Calculate the variance
• calculate the mean
• generate random samples
• Calculate the variance

Question 5: MLlib uses the compressed sparse column format for sparse matrices, as Such it only keeps the non-zero entrees?

• True
• False

Module 2: Preparing Data

Question 1: For a dataframe object the method describe calculates the?

• count
• mean
• standard deviation
• max
• min
• all of the above

Question 2: What line of code drops the rows that contain null values, select the best answer?

• val dfnan = df.withColumn(“nanUniform”, halfTonNaN(df(“uniform”)))
• dfnan.na.replace(“uniform”, Map(Double.NaN -> 0.0))
• dfnan.na.drop(minNonNulls = 3)
• dfnan.na.fill(0.0)

Question 3: What task does the following lines of code perform?

val lr = new LogisticRegression()

lr.setMaxIter(10).setRegParam(0.01)

val model1 = lr.fit(training)

• perform one hot encoding
• Train a linear regression model
• Train a Logistic regression model
• Perform PCA on the data

Question 4: The StandardScaleModel transforms the data such that?

• each feature has a max value of 1
• each feature is Orthogonal
• each feature to have a unit standard deviation and zero mean
• each feature has a min value of -1

Module 3: Feature Engineering

Question 1: Spark ML works with?

• tensors
• vectors
• dataframes
• lists

Question 2: The function IndexToString() performs One hot encoding?

• True
• False

Question 3: Principal Component Analysis is Primarily used for?

• to convert categorical variables to integers
• to predict discrete values
• dimensionality reduction

Question 4: One import set prior to using PCA is?

• normalizing your data
• making sure every feature is not correlated
• taking the log for your data
• subtracting the mean

Module 4: Fitting a Model

Question 1: You can use decision trees for?

• regression
• classification
• classification and regression
• data normalization

Question 2: The following lines of code: val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))

• split the data into training and testing data
• train the model
• use 70% of the data for testing
• use 30% of the data for training
• make a prediction

Question 3: In the Random Forest Classifier constructor .setNumTrees()?

• sets the max depth of trees
• sets the minimum number of classes before a split
• set the number of trees

Question 4: Elastic net regularization uses?

• L0-norm
• L1-norm
• L2-norm
• a convex combination of the L1 norm and L2 norm

Module 5: Pipeline and Grid Search

Question 1: What task does the following code perform: withColumn(“paperscore”, data(“A2”) * 4 + data(“A”) * 3)?

• add 4 colunms to A2
• add 3 colunms to A1
• add 4 to each elment in colunm A2
• assign a higher weight to A2 and A journals

Question 2: In an estimator?

• there is no need to call the method fit
• fit function is called
• transform fuction is only called

Question 3: Which is not a valid type of Evaluator in MLlib?

• RegressionEvaluator
• MultiClassClassificationEvaluator
• MultiLabelClassificationEvaluator
• BinaryClassificationEvaluator
• All are valid

Question 4: In the following lines of code, the last transform in the pipeline is a:

val rf = new RandomForestClassifier().setFeaturesCol(“assembled”).setLabelCol(“status”).setSeed(42)

import org.apache.spark.ml.Pipeline

val pipeline = new Pipeline().setStages(Array(value_band_indexer,category_indexer,label_indexer,assembler,rf))

• principal component analysis
• Vector Assembler
• String Indexer
• Vector Assembler
• Random Forest Classifier

Data Science with Scala Final Exam Answers – Cognitive Class

Question 1: What is not true about labeled points?

• They associate sparse vectors with a corresponding label/response
• They associate dense vectors with a corresponding label/response
• They are used in unsupervised machine learning algorithms
• All are true
• None are true

Question 2: Which is true about column pointers in sparse matrices?

• By themselves, they do not represent the specific physical location of a value in the matrix
• They never repeat values
• They have the same number of values as the number of columns
• All are true
• None are true

Question 3: What is the name of the most basic type of distributed matrix?

• CoordinateMatrix
• IndexedRowMatrix
• SparseMatrix
• SimpleMatrix
• RowMatrix

Question 4: A perfect correlation is represented by what value?

• 3
• 1
• -1
• 100
• 0

Question 5: A MinMaxScaler is a transformer which:

• Rescales each feature to a specific range
• Takes no parameters
• Makes zero values remain untransformed
• All are true
• None are true

Question 6: Which is not a supported Random Data Generation distribution?

• Poisson
• Uniform
• Exponential
• Delta
• Normal

Question 7: Sampling without replacement means:

• The expected number of times each element is chosen is randomized
• The expected size of the sample is a fraction of the RDDs size
• The expected number of times each element is chosen
• The expected size of the sample is unknown
• The expected size of the sample is the same as the RDDs size

Question 8: What are the supported types of hypothesis testing?

• Pearson’s Chi-Squared Test for goodness of fit
• Pearson’s Chi-Squared Test for independence
• Kolmogorov-Smirnov test for equality of distribution
• All are supported
• None are supported

Question 9: For Kernel Density Estimation, which kernel is supported by Spark?

• KDEMultivariate
• KDEUnivariate
• Gaussian
• KernelDensity
• All are supported

Question 10: Which DataFrames statistics method computes the pairwise frequency table of the given columns?

• freqItems()
• cov()
• crosstab()
• pairwiseFreq()
• corr()

Question 11: Which is not true about the fill method for DataFrame NA functions?

• It is used for replacing NaN values
• It is used for replacing nil values
• It is used for replacing null values
• All are true
• None are true

Question 12: Which transformer listed below is used for Natural Language processing?

• StandardScaler
• OneHotEncoder
• ElementwiseProduct
• Normalizer
• None are used for Natural Language processing

Question 13: Which is true about the Mahalanobis Distance?

• It is a scale-variant distance
• It does not take into account the correlations of the dataset
• It is measured along each Principle Component axis
• It is a multi-dimensional generalization of measuring how many standard deviations a point is away from the median
• It has units of distance

Question 14: Which is true about OneHotEncoder?

• It must be told which column to create for its output
• It creates a Sparse Vector
• It must be told which column is its input
• All are true
• None are true

Question 15: Principle Component Analysis is:

• Is never used for feature engineering
• Used for supervised machine learning
• A dimension reduction technique
• All are true
• None are true

Question 16: MLlib’s implementation of decision trees:

• Supports only multiclass classification
• Does not support regressions
• Partitions data by rows, allowing distributed training
• Supports only continuous features
• None are true

Question 17: Which is not a tunable of SparkML decision trees?

• maxBins
• maxMemoryInMB
• minInstancesPerNode
• minDepth
• minInfoGain

Question 18: Which is true about Random Forests?

• They support non-categorical features
• They combine many decision trees in order to reduce the risk of overfitting
• They do not support regression
• They only support binary classification
• None are true

Question 19: When comparing Random Forest versus Gradient-Based Trees, what must you consider?

• How the number of trees affects the outcome
• Depth of Trees
• Parallelization abilities
• All of these
• None of these

Question 20: Which is not a valid type of Evaluator in MLlib?

• MultiLabelClassificationEvaluator
• RegressionEvaluator
• BinaryClassificationEvaluator
• MultiClassClassificationEvaluator
• All are valid

Introduction to Data Science with Scala

Data science with Scala can be quite powerful, leveraging Scala’s strong functional programming capabilities and compatibility with the Java ecosystem. While Python is more commonly associated with data science due to its extensive libraries and ease of use, Scala can be a great choice for certain use cases, especially when working with large-scale distributed systems or when integration with existing Java codebases is important.

Here are some key aspects of doing data science with Scala:

1. Libraries: Scala has several libraries for data manipulation, analysis, and machine learning. Apache Spark is perhaps the most prominent, providing a distributed computing framework that is highly scalable and efficient. Scala also has libraries like Breeze for numerical computing and ScalaNLP for natural language processing tasks.
2. Functional Programming: Scala’s functional programming features, such as immutability and higher-order functions, can make code more concise and maintainable. This can be particularly useful when dealing with complex data transformations and analysis pipelines.
3. Integration with Java: Scala runs on the Java Virtual Machine (JVM), which means it seamlessly interoperates with Java libraries and frameworks. This can be advantageous when working in environments where Java is already heavily used, or when leveraging existing Java code for data processing tasks.
4. Type Safety: Scala’s static typing system can help catch errors at compile time, reducing the likelihood of runtime errors in data processing pipelines. This can be especially valuable when working with large, complex datasets where errors can be costly.
5. Concurrency and Parallelism: Scala provides powerful abstractions for concurrent and parallel programming, which can be beneficial when dealing with large-scale data processing tasks. This is particularly important in the context of distributed computing frameworks like Apache Spark, where efficient parallelism is crucial for performance.
6. Tooling: While Python has a richer ecosystem of data science libraries and tools, Scala is supported by popular integrated development environments (IDEs) like IntelliJ IDEA and Scala-specific tools like sbt for build automation. Additionally, there are emerging tools and libraries aimed at making data science workflows in Scala more productive.

Overall, while Scala may not be as commonly associated with data science as Python, it offers a compelling set of features and capabilities for building robust and scalable data processing pipelines, especially in environments where integration with existing Java codebases or efficient distributed computing is required.