Big Data Analytics Quiz 1
Big Data Analytics Quiz
Test your knowledge in the exciting world of Big Data Analytics! This quiz will challenge your understanding of real-time data processing, frameworks, and the core dimensions of Big Data.
- 10 engaging questions
- Multiple choice and checkbox formats
- Score points and enhance your skills
RTBDA stands for
Real Time Big Data Analytics
Real Time Big Data Analysis
Right Time Big Data Analytics
Real Time Big Data Algorithms
Real Technology Big Data Analysis
There are really only two paradigms for data processing:
Velocity
Parallel
Varied
Stream
Persisted
Batch
Volume
Typically when we’re talking about real-time or near real-time systems, what we mean is architectures that allow you to respond to data as you receive it without necessarily persisting it to a database first.
True
False
What description best describes this diagram?
Split Match Framework
Very Big Split Data Match Framework
Traditional Parallel Distributed Computing Framework
Big Data Map Reduce Framework
What description best describes this diagram?
Traditional Very Big Data Framework
Big Data Hadoop Processing Framework
Traditional Parallel Distributed Computing Framework
Input Output Framework
The Map Reduce framework allows us to perform parallel computations without bothering with issues like Reliability, Fault Tolerance, etc.
True
False
Real-time Big Data is about these things:
Detecting fraud while someone is swiping a credit card
Making available instant access (real-time) to everything there is to know about your online presence, to anyone who wants to know
A process for storing petabytes or exabytes of data in a data warehouse
Covertly tracking user behavior and movements on the internet to enable identity theft
Triggering an offer while a shopper is standing on a checkout line
Placing an ad on a website while someone is reading a specific article
Make better decisions and take meaningful actions at the right time
Adding or removing hardware on the fly as server utilization increases or decreases
Select the five phases of "real-time":
Data Distillation
Persistence & Real-time Access
Model Refresh
Real-time Scoring
Validation & Deployment
Instant Pulling of Data
Truth in Time Calculations
Model Development
Instant Pushing of Data
The traditional non-"Big Data" way of handling parallel and distributed processing was to:
Merge the data into larger parts or blocks and store them in different machines.
Split the data into smaller parts or blocks and store them in different machines
Merge the data into larger parts or blocks and store them in the same machine.
Combine the data into smaller parts or blocks and store them in different machines.
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