Exploring Spark’s GraphX Cognitive Class Exam Quiz Answers

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Enroll Here: Exploring Spark’s GraphX Cognitive Class Exam Quiz Answers

Exploring Spark’s GraphX Cognitive Class Certification Answers

Module 1 – Introduction to Graph Parallel Quiz Answers – Cognitive Class

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 – Cognitive Class

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 – Cognitive Class

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 – Cognitive Class

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 – Cognitive Class

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

Introduction to Exploring Spark’s GraphX

“Exploring Spark’s GraphX” introduces you to GraphX, a component of Apache Spark designed for processing and analyzing graph data at scale. GraphX provides an API for expressing graph computation that seamlessly integrates with Spark’s RDD (Resilient Distributed Dataset) abstraction, enabling efficient distributed processing.

Here’s a brief overview to get you started:

1. Understanding Graphs: In GraphX, graphs are mathematical structures composed of vertices (nodes) and edges (links). These graphs are used to model relationships or networks between entities in various domains, such as social networks, transportation systems, or biological networks.
2. Key Components:
   • Vertices: Represent individual entities in the graph.
   • Edges: Represent relationships between pairs of vertices.
   • Graph: A collection of vertices and edges.
3. RDD Integration: GraphX leverages Spark’s RDDs to represent graphs efficiently. RDDs provide fault tolerance and parallelism necessary for distributed graph processing.
4. Graph Operations:
   • Vertex and Edge Operations: GraphX provides APIs to manipulate vertices and edges, such as filtering, mapping, joining, and aggregating.
   • Graph Algorithms: GraphX offers a wide range of built-in graph algorithms, including PageRank, Connected Components, and Triangle Counting, enabling various graph analytics tasks.
   • Graph Construction: GraphX facilitates the creation of graphs from RDDs of vertices and edges or by transforming existing graphs.
5. Parallel Computation: GraphX optimizes graph computation by exploiting parallelism across vertices and edges, distributing tasks across the cluster for efficient processing.
6. Integration with Spark Ecosystem: GraphX seamlessly integrates with other components of the Spark ecosystem, such as Spark SQL for data querying, MLlib for machine learning, and Spark Streaming for real-time processing, enabling end-to-end data pipelines.
7. Use Cases:
   • Social Network Analysis: Analyzing social connections, influence propagation, and community detection.
   • Recommendation Systems: Building personalized recommendation systems based on graph-based collaborative filtering.
   • Network Analysis: Studying communication networks, transportation networks, or biological networks for insights and optimizations.
8. Scalability and Performance: GraphX is designed for scalability, allowing you to process large-scale graphs efficiently across distributed clusters, making it suitable for handling massive graph datasets.

By exploring Spark’s GraphX, you can unlock powerful capabilities for analyzing and extracting insights from complex graph data structures efficiently and at scale.