Free Master of Data science Big Data Questions and Answers — Questions and Answers

Question 1: Which of the following terms best describes the illustration below?

Dark Data
Big Data (Correct answer)
Large Data
None of the above

Correct answer: Big Data

The term 'Big Data' refers to extremely large, complex, and diverse datasets that traditional data processing applications struggle to handle. It is characterized by the '3 Vs': Volume (immense amount of data), Velocity (speed of data generation and processing), and Variety (diverse types of data). An illustration depicting these characteristics would best be described as Big Data.

Question 2: Identify the accurate statement.

Data Cleaning focuses on prediction, based on known properties learned from the training data
Representing data in a form which both mere mortals can understand and get valuable insights is as much a science as much as it is art
Machine learning focuses on prediction, based on known properties learned from the training data
None of the above (Correct answer)

Correct answer: None of the above

Let's evaluate the statements: A) Data Cleaning focuses on identifying and correcting errors, not prediction. B) Representing data for insights is indeed both a science and an art (data visualization/communication). C) Machine learning focuses heavily on prediction based on learned properties from training data, especially in supervised learning. Given that B and C are largely accurate statements in general data science contexts, and A is clearly inaccurate, the instruction to identify the 'accurate statement' and the provided answer 'None of the above' suggests a very specific or nuanced interpretation where B and C are deemed insufficiently precise or universally true. However, without further context, C is generally considered an accurate description of a core ML focus.

Question 3: Which of the following big data traits is comparatively more important to data science?

Volume
Variety (Correct answer)
Velocity
None of the above

Correct answer: Variety

While Volume, Velocity, and Veracity are all crucial traits of Big Data, Variety is often considered particularly important for data science. Data scientists frequently work with diverse data types—structured, semi-structured, and unstructured—from various sources. The ability to integrate, process, and derive insights from this heterogeneous data is a core challenge and strength of data science.

Question 4: Which of the following analytic skills does an information management organization offer?

Information Integration
Content Management
Stream Computing
All of the above (Correct answer)

Correct answer: All of the above

An information management organization, especially in the context of Big Data and data science, offers a range of analytic skills and capabilities. These include Information Integration (combining data from various sources), Content Management (organizing and managing diverse content), and Stream Computing (processing data in real-time as it arrives). All these are essential for effectively leveraging data.

Question 5: Identify the incorrect statement.

Big Data is just about lots of data (Correct answer)
The big volume indeed represents Big Data
The data growth and social media explosion have changed how we look at the data
All of the above

Correct answer: Big Data is just about lots of data

The statement 'Big Data is just about lots of data' is incorrect because Big Data is defined by more than just its immense volume. It also encompasses velocity (the speed at which data is generated and processed), variety (the diverse types of data), and often veracity (the quality and trustworthiness of the data). Reducing Big Data to merely its size misses these other critical dimensions.

Question 6: After gathering the data, which of the following steps does the data scientist perform?

Data Replication
Data Integration
Data Cleansing (Correct answer)
All of the above

Correct answer: Data Cleansing

After gathering data, the immediate and crucial next step for a data scientist is typically data cleansing (also known as data cleaning or data scrubbing). This involves identifying and correcting errors, inconsistencies, missing values, and duplicates to ensure the data is accurate and reliable for subsequent analysis and modeling. High-quality data is foundational for meaningful insights.

Question 7: Which of the following focuses on the data's (previously unidentified) qualities being discovered?

Machine Learning
Data wrangling
Big Data
Data mining (Correct answer)

Correct answer: Data mining

Data mining is the process of discovering patterns, insights, and knowledge from large datasets, often previously unidentified. It involves using various techniques from statistics, machine learning, and database systems to extract valuable information. The core objective is to uncover hidden relationships and predictive models within the data.

Question 8: Data that list all findings in a category are referred to as _________ data.

raw
frequency
summarized (Correct answer)
None of the above

Correct answer: summarized

Summarized data presents aggregated information, often in tables or charts, that condenses raw findings into meaningful categories. This process involves calculating totals, averages, or counts to provide an concise overview rather than individual data points, making it easier to understand trends and patterns.

Question 9: Which of the subsequent is an illustration of raw data?

a real-time GPS-encoded navigation file
initial time-series file of temperature values
original swath files generated from a sonar system
All of the above (Correct answer)

Correct answer: All of the above

Raw data refers to unprocessed, original data collected directly from its source. Real-time GPS files, initial time-series temperature values, and original sonar swath files are all examples of data in its most basic, unanalyzed form before any cleaning, transformation, or aggregation.

Question 10: Identify the accurate statement.

Secondary data is original source of data
Questions are obtained after data processing steps
Primary data is original source of data (Correct answer)
None of the above

Correct answer: Primary data is original source of data

Primary data is information collected directly by the researcher or organization for a specific purpose, making it the original source. In contrast, secondary data is information that has already been collected by someone else and is available from existing sources.

Question 11: Which of the following information is entered into a formula to provide findings that are widely accepted?

Processed (Correct answer)
Synchronized
Raw
Data booting

Correct answer: Processed

Processed data, also known as clean or transformed data, is information that has been organized, structured, and validated after collection. This preparation makes it suitable for analysis, ensuring accuracy and consistency when entered into formulas to derive reliable and widely accepted findings.

Question 12: Which of the following languages should the question mark in the illustration below replace?

COBOL
Java (Correct answer)
PHP
NLP

Correct answer: Java

Without the specific illustration, it's impossible to provide context. However, Java is a widely used, high-level, class-based, object-oriented programming language known for its 'write once, run anywhere' capability. It is extensively used in enterprise-level applications, Android development, and big data technologies, making it a common language in data science contexts.

Question 13: The problems with big data veracity go beyond volume, diversity, and velocity.

True (Correct answer)
FALSE

Correct answer: True

The '3 V's' (Volume, Velocity, Variety) are commonly associated with Big Data, but 'Veracity' is a fourth crucial dimension. Veracity refers to the trustworthiness, accuracy, and quality of the data, addressing issues like bias, noise, and abnormalities that can significantly impact analysis and decision-making.

Question 14: Which of the subsequent processes involves organizing datasets to make analysis easier?

Data booting
Data mining
Data tidying (Correct answer)
All of the above

Correct answer: Data tidying

Data tidying is the process of structuring datasets to make them easy to analyze. It involves ensuring that each variable forms a column, each observation forms a row, and each type of observational unit forms a table, which simplifies data manipulation and statistical modeling.

Question 15: Which of the following aspects of untidy data is most frequently problematic?

A single observational unit is stored in multiple tables
Variables are stored in both rows and columns
Column headers are values
All of the above (Correct answer)

Correct answer: All of the above

Untidy data often presents several issues that hinder analysis. These include having a single observational unit spread across multiple tables, variables being stored in both rows and columns, and column headers containing values rather than variable names, all of which complicate data processing and interpretation.

Question 16: Identify the accurate statement.

Nearly 20% of data analysis is spent on data dredging
Nearly 80% of data analysis is spent on the cleaning and preparing data (Correct answer)
Nearly 80% of data analysis is spent on wrangling data
None of the above

Correct answer: Nearly 80% of data analysis is spent on the cleaning and preparing data

Data scientists often report that a significant portion of their time, typically around 80%, is dedicated to data cleaning and preparation tasks. This involves identifying and correcting errors, handling missing values, transforming data formats, and structuring it for analysis, highlighting its critical role in ensuring data quality and reliable insights.

Free Master of Data science Big Data Questions and Answers

Which of the following terms best describes the illustration below?

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