MCQs on Graph Processing with GraphX | Apache Spark MCQs Questions
Apache Spark is a versatile framework for big data processing, and GraphX is its specialized API for graph processing. Apache Spark MCQs questions on GraphX cover essential concepts like graph representation, RDD integration, Pregel API, and graph algorithms. Learn about its applications in building social networks, recommendation systems, and performance tuning techniques for efficient graph computations.
MCQs: Introduction to GraphX
- What is GraphX in Apache Spark used for?
a) Machine learning
b) Graph processing and analytics
c) Real-time data streaming
d) Querying structured data - GraphX provides an abstraction for:
a) Tables
b) Graphs
c) Filesystems
d) Databases - Which type of data is best suited for GraphX?
a) Tabular data
b) Graph-structured data
c) Unstructured text
d) Video files - GraphX is a library built on:
a) Hadoop HDFS
b) Spark Core
c) Spark Streaming
d) Spark SQL - Which two main abstractions does GraphX introduce?
a) Tables and files
b) Graphs and clusters
c) VertexRDD and EdgeRDD
d) Streams and datasets
MCQs: Graph Representation and RDDs in GraphX
- In GraphX, vertices are represented as:
a) Nodes in a graph
b) Edges connecting two points
c) Key-value pairs in a VertexRDD
d) SQL tables - How are edges represented in GraphX?
a) Streams
b) RDDs containing triplets
c) Structured queries
d) Key-value pairs - What does an edge triplet in GraphX contain?
a) Source, destination, and edge property
b) Graph ID and metadata
c) Dataset and transformations
d) Cluster and data partitions - GraphX relies on which Spark abstraction for data representation?
a) DataFrames
b) Resilient Distributed Datasets (RDDs)
c) Streams
d) SQL tables - How can you represent metadata in GraphX?
a) Attach properties to vertices and edges
b) Store them in a separate database
c) Use JSON objects
d) Write them as comments in the code
MCQs: Pregel API and Graph Algorithms
- What is the Pregel API in GraphX used for?
a) Streaming data processing
b) Iterative graph algorithms
c) File storage optimization
d) Data visualization - Which graph algorithm identifies the shortest path between nodes?
a) PageRank
b) Connected Components
c) Shortest Paths
d) Triangle Count - PageRank is commonly used for:
a) Social network visualization
b) Website ranking
c) Data partitioning
d) Machine learning training - Which algorithm is used to find clusters in a graph?
a) PageRank
b) Connected Components
c) Graph Coloring
d) Linear Regression - Triangle counting in GraphX helps in identifying:
a) Graph centrality
b) Node degrees
c) Network clustering
d) Edge weights
MCQs: Building Social Networks and Recommendations
- How is GraphX applied in social network analysis?
a) For video streaming
b) Identifying relationships and connections
c) Storing structured data
d) Writing SQL queries - Which graph algorithm is commonly used in recommendation systems?
a) PageRank
b) Shortest Path
c) Alternating Least Squares (ALS)
d) Connected Components - To analyze influencer impact in a social network, you would use:
a) PageRank
b) Triangle Counting
c) Shortest Path
d) DataFrames - What type of data structure is commonly used in GraphX to store relationships?
a) Adjacency list
b) Flat files
c) XML tables
d) JSON - Recommendation systems with GraphX are based on:
a) Graph algorithms analyzing user-item relationships
b) Batch processing of structured data
c) DataFrame transformations
d) Streaming data ingestion
MCQs: Performance Tuning in Graph Processing
- How can GraphX improve performance during computations?
a) Use of DataFrames
b) Optimizing graph partitioning
c) Increasing edge weight
d) Writing data to disk - Graph partitioning in GraphX helps to:
a) Store graphs in a single node
b) Parallelize computations across clusters
c) Reduce the number of nodes
d) Perform real-time streaming - Which of the following improves GraphX performance?
a) Increasing vertex degrees
b) Caching intermediate RDDs
c) Using flat files for storage
d) Writing custom SQL queries - How does GraphX handle large-scale graph processing?
a) By replicating data across nodes
b) Through distributed computation
c) Using local mode for processing
d) Compressing vertex properties - To debug performance issues in GraphX, you should:
a) Use Spark logs and UI
b) Write additional SQL queries
c) Modify graph algorithms
d) Reduce the number of edges
General Knowledge MCQs on GraphX
- What is the default storage level for RDDs in Spark?
a) MEMORY_ONLY
b) DISK_ONLY
c) MEMORY_AND_DISK
d) MEMORY_ONLY_SER - GraphX uses which type of computation model?
a) SQL-based
b) Directed Acyclic Graph (DAG)
c) MapReduce
d) OLAP - In GraphX, parallel edges are:
a) Allowed by default
b) Not supported
c) Removed during graph construction
d) Stored in a separate RDD - What is the default partitioner used in GraphX?
a) RangePartitioner
b) HashPartitioner
c) EdgePartitioner
d) Custom Partitioner - The Pregel API in GraphX is based on:
a) Message-passing model
b) SQL queries
c) Key-value stores
d) Real-time streaming
Answers Table
| Qno | Answer (Option with Text) |
|---|---|
| 1 | b) Graph processing and analytics |
| 2 | b) Graphs |
| 3 | b) Graph-structured data |
| 4 | b) Spark Core |
| 5 | c) VertexRDD and EdgeRDD |
| 6 | c) Key-value pairs in a VertexRDD |
| 7 | b) RDDs containing triplets |
| 8 | a) Source, destination, and edge property |
| 9 | b) Resilient Distributed Datasets (RDDs) |
| 10 | a) Attach properties to vertices and edges |
| 11 | b) Iterative graph algorithms |
| 12 | c) Shortest Paths |
| 13 | b) Website ranking |
| 14 | b) Connected Components |
| 15 | c) Network clustering |
| 16 | b) Identifying relationships and connections |
| 17 | c) Alternating Least Squares (ALS) |
| 18 | a) PageRank |
| 19 | a) Adjacency list |
| 20 | a) Graph algorithms analyzing user-item relationships |
| 21 | b) Optimizing graph partitioning |
| 22 | b) Parallelize computations across clusters |
| 23 | b) Caching intermediate RDDs |
| 24 | b) Through distributed computation |
| 25 | a) Use Spark logs and UI |
| 26 | c) MEMORY_AND_DISK |
| 27 | b) Directed Acyclic Graph (DAG) |
| 28 | a) Allowed by default |
| 29 | c) EdgePartitioner |
| 30 | a) Message-passing model |