Dell EMC Data Science Associate Certification Questions and Answers (Dumps and Practice Questions)

Question : You are analyzing a time series and want to determine its stationarity. You also want to determine
the order of autoregressive models.
How are the autocorrelation functions used?


1. PACF as an indication of stationarity, and ACF for the correlation between Xt and Xt-k not
explained by their mutual correlation with X1 through Xk-1.
2. ACF as an indication of stationarity, and PACF to determine the correlation of X1 through Xk-1.
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4. ACF as an indication of stationarity, and PACF for the correlation between Xt and Xt-k not
explained by their mutual correlation with X1 through Xk-1.


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Explanation: Autocorrelation is the linear dependence of a variable with itself at two points in time. For stationary processes, autocorrelation between any two observations only depends on the time lag h between them. Define Cov(yt, yt-h) = Yh. The autocorrelation function tells us the time interval over which a correlation in the
noise exists. If the noise is made entirely of waves, and the waves move through the plasma (or other medium) without decaying as they travel, the autocorrelation
will be large for all time. Why does the autocorrelation Why does the autocorrelation decay in time ? decay in time ? because the data does not go on forever.
Autocorrelation, also known as serial correlation or cross-autocorrelation, is the cross-correlation of a signal with itself at different points in time (that is what the cross stands for). Informally, it is the similarity between observations as a function of the time lag between them. It is a mathematical tool for finding repeating patterns, such as the presence of a periodic signal obscured by noise, or identifying the missing fundamental frequency in a signal implied by its harmonic frequencies. It is often used in signal processing for analyzing functions or series of values, such as time domain signals.






Question : Which word or phrase completes the statement? A spreadsheet is to a data island as a centralized
database for reporting is to a ________?


1. Data Repository
2. Analytic Sandbox
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4. Data Warehouse

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Explanation: Excel Spreadsheets are frequently used in Data Warehousing applications to access and present data from Data Marts. Spreadsheets are powerful, flexible and relatively inexpensive tools that many decision makers are comfortable using.

Before Data Warehousing became popular, decision makers often had difficulty getting access to corporate data. It was necessary to populate spreadsheets from multiple disparate data sources and manually integrate the data. This process was both time consuming and error-prone.

Privacy, data redundancy and currency issues arose when decision makers retained their own personal copies of sensitive corporate data on the personal computers and laptops.






Question : Which R data structure allows elements to have different data types?


1. Matrix
2. Vector
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4. Array

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Explanation: Lists ("generic vectors") are another kind of data storage. Lists have elements, each of which can contain any type of R object, i.e. the elements of a list do not have to be of the same type. List elements are accessed through three different indexing operations. These are explained in detail in Indexing.

Lists are vectors, and the basic vector types are referred to as atomic vectors where it is necessary to exclude lists.

Related Questions

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Question : Select the correct statement which applies to logistic regression


1. Computationally inexpensive, easy to implement, knowledge representation easy to interpret
2. May have low accuracy
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4. Only 1 and 3 are correct
5. All 1,2 and 3 are correct




Question : Suppose that we are interested in the factors that influence whether a political candidate wins an election.
The outcome (response) variable is binary (0/1); win or lose. The predictor variables of interest are the amount of
money spent on the campaign, the amount of time spent campaigning negatively and whether or not the candidate is an incumbent.

Above is an example of



1. Linear Regression
2. Logistic Regression
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4. Maximum likelihood estimation
5. Hierarchical linear models



Question : A researcher is interested in how variables, such as GRE (Graduate Record Exam scores),
GPA (grade point average) and prestige of the undergraduate institution, effect admission into graduate school.
The response variable, admit/don't admit, is a binary variable.

Above is an example of



1. Linear Regression
2. Logistic Regression
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4. Maximum likelihood estimation
5. Hierarchical linear models



Question :

Which of the following is an correct example of the target variable in regression (supervised learning) ?

1. Nominal values like true, false
2. Reptile, fish, mammal, amphibian, plant, fungi
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4. Only 1 and 2
5. All 1,2 and 3




Question : Select the sequence of the developing machine learning applications
A. Analyze the input data
B. Prepare the input data
C. Collect data
D. Train the algorithm
E. Test the algorithm
F. Use It



1. A,B,C,D,E,F
2. C,B,A,D,E,F
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4. C,B,A,D,E,F


Question :

Select the correct statement which applies to K-Nearest Neighbors

1. No Assumption about the data
2. Computationaly expensive
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4. Works with Numeric Values


1. 1,2,3,4
2. 2,3,4
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4. 1,2,4