Free E20-065 Exam Braindumps
Advanced Analytics Specialist Exam for Data Scientists

Updated On: 23-Jan-2021
QUESTION: 1
What is an important simu-lation design consideration?

A. Ensure model Inputs align with reality
B. Use different seed values to regenerate results
C. For rare event models, minimize number of trials
D. A complex model is better than a simple model

Answer(s): A
QUESTION: 2
How is the relative value of a node visualized in a sunburst?

A. Color
B. Area
C. Gradient
D. Position

Answer(s): A
QUESTION: 3
What describes how nodes in a social network are similar to each other in characteristics?

A. Community clustering
B. Modularity
C. Homophily
D. Strongly tied network

Answer(s): C
QUESTION: 4
What is a characteristic of stemming?

A. Reduces words of variant forms to their base forms based on a set of heuristics
B. Can be performed by calling the stemming!) function on a lemma in NLTK
C. Can be performed by calling the stemming() function on a synset in NLTK
D. Reduces words of variant forms to their base forms based on a dictionary

Answer(s): A
QUESTION: 5
Given an input vector of features, a Random Forests model performs a classification task and
ends in a tie. How does the model handle this outcome?

A. The model wil be rebuilt
B. A winner is chosen at random
C. The tree that caused the tie is discarded

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Overview of the Advanced Analytics Specialist Exam for Data Scientists Exam

The E20-065 Advanced Analytics Specialist certification mandates deep proficiency in implementing complex analytical workflows for data scientists tasked with extracting actionable intelligence from vast datasets. Candidates must demonstrate advanced technical competency in R, SAS, and SQL, alongside the application of sophisticated machine learning algorithms including k-means clustering, logistic regression, and naive Bayes classification. The curriculum necessitates proficiency in data visualization techniques, time series analysis, and optimization methods within the Hadoop ecosystem. Practitioners must synthesize unstructured data using MapReduce and Pig while executing predictive modeling through the MADlib library to solve high-dimensional statistical problems across scalable enterprise storage architectures.



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