Exploring Spark’s GraphX Cognitive Class Exam Answers

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Introduction to Exploring Spark’s GraphX

Exploring Spark’s GraphX is an exciting journey into the world of graph processing within Apache Spark. GraphX is a powerful library that allows you to perform parallel computation on large-scale graphs directly within the Spark framework. Whether you’re analyzing social networks, financial transactions, or any other interconnected data, GraphX provides efficient tools for graph representation and computation.

Key Concepts in GraphX:

1. Graph Representation:
   • GraphX represents graphs as collections of vertices (nodes) and edges (connections between nodes). These can be any type of objects, allowing flexibility in data representation.
2. RDD-Based Graphs:
   • Under the hood, GraphX uses RDDs (Resilient Distributed Datasets), the fundamental data structure in Spark, to store and process graphs. This allows for distributed and fault-tolerant graph processing.
3. Vertex and Edge RDDs:
   • Vertex RDDs store information about each vertex in the graph.
   • Edge RDDs store information about each edge connecting vertices.
4. Graph Operators:
   • GraphX provides a rich set of operators for manipulating graphs, such as mapVertices, mapEdges, subgraph, and more. These operators allow you to transform graph structure and properties efficiently.
5. Graph Algorithms:
   • GraphX includes a variety of built-in graph algorithms, including PageRank, Connected Components, Triangle Counting, and more. These algorithms are optimized for distributed execution on large-scale datasets.
6. Integration with Spark’s Ecosystem:
   • GraphX seamlessly integrates with other Spark components, such as Spark SQL for data manipulation and MLlib for machine learning tasks. This integration enables comprehensive data analysis pipelines.

Getting Started:

To begin exploring GraphX, you typically follow these steps:

1. Data Loading:
   • Load your graph data into Spark, either from files (like CSV, JSON, etc.) or from other data sources compatible with Spark.
2. Graph Construction:
   • Construct a GraphX graph using the loaded vertex and edge RDDs.
3. Graph Processing:
   • Apply graph operators and algorithms to analyze and manipulate the graph according to your specific use case.
4. Analysis and Visualization:
   • Analyze the results of graph processing and visualize the graph structure or algorithm outputs as needed.

Conclusion:

Exploring Spark’s GraphX opens up a wide range of possibilities for scalable and efficient graph processing. Whether you’re a data scientist, analyst, or engineer, GraphX provides the tools to handle large-scale graph data and extract meaningful insights. It’s a valuable addition to Spark’s ecosystem, leveraging its strengths in distributed computing and data processing.

Exploring Spark’s GraphX Cognitive Class Certification Answers

Module 1 – Introduction to Graph Parallel Quiz Answers

Question 1: GraphX extends RDDs, which allows users to use GraphX as a collection, but not as a graph!

• True
• False

Question 2: Which of the following statements is true?

• Graph-Parallel is usually handled by Hadoop and Spark.
• Graph-Parallel focuses on distributing data across different nodes and systems.
• Data-Parallel is usually handled by Pregel, GraphLab and Giraph.
• Data-Parallel focuses on efficiently executing graph algorithms.
• None of the above

Question 3: GraphX unifies Data-Parallelism and Graph-Parallelism in one library.

• True
• False

Module 2 – Visualizing GraphX and Exploring Graph Operators Quiz Answers

Question 1: The “degree” operator returns a VertexRDD[Int] containing the number of outgoing edges of each vertex.

• True
• False

Question 2: Which of the following is not an attribute of a Triplet class?

• attr
• id
• srcAttr
• srcId
• None of the above

Question 3: Other libraries such as Gephi or GraphLab can help GraphX with visualization.

• True
• False

Module 3 – Modifying GraphX Quiz Answers

Question 1: We must run the “partitionBy” function before running the “groupEdges” operator.

• True
• False

Question 2: Which of following is among the PartitionStrategies provided by GraphX?

• EdgePartition2D
• RandomVertexCut
• EdgePartition1D
• CanonicalRandomVertexCut
• All of the above

Question 3: To improve efficiency, GraphX reuses portions of the graph which are unaffected by a modifier.

• True
• False

Module 4 – Neighborhood Aggregation Caching Quiz Answers

Question 1: AggregateMessages is the only neighborhood aggregation function provided by GraphX.

• True
• False

Question 2: Which of the following is not an attribute of TripletFields?

• TripletFields.None
• TripletFields.DstOnly
• TripletFields.EdgeOnly
• TripletFields.All
• None of the Above

Question 3: The ClassTag is optional for aggregateMessages if the message is a String.

• True
• False

Exploring Spark’s GraphX Final Exam Answers

Question 1: To instantiate a Graph, you need at LEAST 2 RDDs.

• True
• False

Question 2: pageRank is a graph algorithm that ranks the edges of the graph by correlating their relation with vertices, in terms of both quality and quantity.

• True
• False

Question 3: The numEdges operator returns an EdgesRDD[Long].

• True
• False

Question 4: Which of the following ClassTypes are returned from mapTriplets, assuming Graph[VD, ED] is the original?

• Graph[VD, ED]
• Graph[VD2, ED]
• Graph[VD, ED2]
• Graph[VD2, ED2]
• None of the Above

Question 5: The reverse operator returns a graph in which the direction of all edges are reversed.

• True
• False

Question 6: Which of the following ClassTypes are returned from mapTriplets, assuming Graph[VD, ED] is the original?

• Graph[VD, ED]
• Graph[VD2, ED]
• Graph[VD, ED2]
• Graph[VD2, ED2]
• None of the Above

Question 7: Caching graphs that are only used infrequently can slow computations.

• True
• False

Question 8: Which of the following is required to define aggregateMessages?

• sendMsg
• mergeMsg
• tripletFields
• sendMsg and mergeMsg
• All of the Above

Question 9: Triplets are a required parameter when instantiating a Graph.

• True
• False

Question 10: When defining the merge parameter for groupEdges (Int), which of the following is a valid definition for merge = (Edge1, Edge2)?

• Edge1
• Edge1 * Edge2
• Edge1 – Edge2 / Edge1
• Edge1 + Edge2
• All of the Above

Question 11: In a tuple, the first parameter returned by the “degrees” operator is the degree info, and the second parameter is the vertexid.

• True
• False

Question 12: Data-Parallel is usually handled by Pregel, GraphLab, and Giraph.

• True
• False

Question 13: Which of the following is true about GraphX?

• GraphX does not have built-in visualization functions.
• GraphX is a Graph-Processing library built into Apache Spark.
• GraphX extends the RDD class which allows us to use GraphX as a graph or a collection.
• GraphX is mainly a graph processing library.
• All of the above

Question 14: By using the mapTriplets function, we are only able to modify the edge attribute.

• True
• False

Question 15: Which of the following is true about the EdgeContext class?

• It has access to vertex attributes, but not to edge attributes.
• It has access to edge attributes, but not to vertex attributes.
• It has sendToDst, sendToSrc, and sendToAll functions.
• It is the same as the EdgeTriplet Class.
• None of the above