Cloudera Cloudera Certifications DS-200 Questions & Answers

• Question 31:

  You have a large m x n data matrix M. You decide you want to perform dimension reduction/clustering on your data and have decide to use the singular value decomposition (SVD; also called principal components analysis PCA)

  Refer to the passage above.

  What represents the SVD of the Matrix standard M given the following information:

  U is m x m unitary V is n x n unitary S is m x n diagonal Q is n x n invertible D is n x n diagonal L is m x m lower triangular U is m x m upper triangular

  A. M = U S V

  B. M = U P

  C. M = Q D Q-1

  D. M = L U

  Correct Answer: A

• Question 32:

  You have a large m x n data matrix M. You decide you want to perform dimension reduction/clustering on your data and have decide to use the singular value decomposition (SVD; also called principal components analysis PCA)

  For the moment, assume that your data matrix M is 500 x 2. The figure below shows a plot of the data.

  

  Which line represents the second principal component?

  A. Blue

  B. Yellow

  Correct Answer: A

• Question 33:

  Many machine learning algorithm involve finding the Global minimum of a convex loss function, primarily because:

  A. The additive inverse of a convex function is concave

  B. The derivative of convex function is always defined

  C. The second derivative of a convex function is a constant

  D. Any local minimum of a convex is also a global minimum

  Correct Answer: B

• Question 34:

  Which two techniques should you use to avoid overfitting a classification model to a data set?

  A. Include a small number "noise" features that are not through to be correlated with the dependent variable.

  B. Replicate features that are through to be significant predicators of the dependent variable multiple time for each observation.

  C. Separate your input data into a training set that is used for fitting and a test set that is used for evaluating the model's performance

  D. Include a regularization term in the model's objective function to control how precisely the model fits the data

  E. Preprocess the data to exclude a typical observation from the model input

  Correct Answer: AE

• Question 35:

  You are building a k-nearest neighbor classifier (k-NN) on a labeled set of points in a high- dimensional space. You determine that the classifier has a large error on the training data. What is the most likely problem?

  A. High-dimensional spaces effectively make local neighborhoods global

  B. k-NN compotation does not coverage in high dimensions

  C. k was too small

  D. The VC-dimension of a k-NN classifier is too high

  Correct Answer: B

• Question 36:

  Which best describes the primary function of Flume?

  A. Flume is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with an infrastructure consisting of sources and sinks for importing and evaluating large data sets

  B. Flume acts as a Hadoop filesystem for log files

  C. Flume Imports data from SQL/relational database into your Hadoop cluster

  D. Flume provides a query languages for Hadoop similar to SQL

  E. Flume is a distributed server for collecting and moving large amount of data into HDFS as it's produced from streaming data flows

  Correct Answer: D

• Question 37:

  You have a directory containing a number of comma-separated files. Each file has three columns and each filename has a .csv extension. You want to have a single tab-separated file (all .tsv) that contains all the rows from all the files.

  Which command is guaranteed to produce the desired output if you have more than 20,000 files to process?

  A. Find . name `*, CSV' print0 | sargs -0 cat | tr `,' `\t' > all.tsv

  B. Find . name `name * .CSV' | cat | awk `BEGIN {FS = "," OFS = "\t"} {print $1, $2, $3}' > all.tsv

  C. Find . name `*.CSV' | tr `,' `\t' | cat > all.tsv

  D. Find . name `*.CSV' | cat > all.tsv

  E. Cat *.CSV > all.tsv

  Correct Answer: B

• Question 38:

  What are three benefits of running feature selection analysis before filtering a classification model?

  A. Eliminates the need to include a regularization term

  B. Reduces the number of subjective decisions required to construct the model

  C. Guarantee the optimally of the final model

  D. Speeds up the model fitting process

  E. Develops an understanding of the importance of different features

  F. Improves the predictive performance of the model

  Correct Answer: DEF

• Question 39:

  When optimizing a function using stochastic gradient descent, how frequently should you update your estimate of the gradient?

  A. Once after every pass through the data set

  B. Once per observation

  C. For each observation with a probability that you choose ahead of time

  D. After a random number of observations

  E. Once every N observations, where you decide N ahead of time

  Correct Answer: AC

• Question 40:

  In what format are web server log files usually generated and how must you transform them in order to make them usable for analysis in Hadoop?

  A. XML files that you need to convert to JSON

  B. Text files that require parsing into useful fields

  C. CSV files that require parsing into useful fields

  D. HTML files that you need to convert to plain text or CSV

  E. Binary files that may require decompression and conversion using AVRO

  Correct Answer: AB