Final Exam: Graph Analytics

Apache Spark    |    Intermediate
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Final Exam: Graph Analytics will test your knowledge and application of the topics presented throughout the Skillsoft Aspire Graph Analytics Journey.


  • Use graph nodes and edges to model entities and relationships
    employ graph nodes and edges to model entities and relationships
    describe the use graph nodes and edges to model entities and relationships in the real world
    recall the attributes of the property graph model used to represent knowledge graphs
    model graphs using an adjacency list and adjacency set and compare the two representations; represent graphs using an adjacency list in python; represent graphs using an adjacency set in python
    represent graphs using an adjacency set in python
    represent graphs using an adjacency matrix in python
    recall how depth-first and breadth-first traversal works; implement breadth-first traversal using a queue data structure; implement depth-first traversal using a stack, as well as using recursion
    compute the shortest path in a weighted graph using greedy traversal and the distance table
    recall the properties of greedy algorithms
    describe the structure and components of a graph recognize the different types of graphs based on the relationships between the nodes
    describe the properties and features of the neo4j graph database
    set up neo4j desktop on your machine; create a database management system from a dump file in neo4j desktop; recognize the features including supporting apps which are available when using neo4j desktop
    use the neo4j browser to run simple queries using the cypher query language
    recognize how data can be grouped in projects, database management systems, and databases
    use the cypher shell to create and manage databases in a dbms; create query parameters and execute cypher queries from the cypher shell
    enable and disable http communication with a neo4j dbms and configure the communication ports
    use the neo4j browser to create a new user and assign a built-in role to it
    recognize how frequently-run queries can be saved and organized from the neo4j browser
    describe the use cases as well as the basic syntax of the cypher query language
    provision nodes with labels as well as properties using the create clause in a cypher query
    define relationships which have their own properties using the cypher language
    remove unwanted nodes and relationships in a neo4j graph
    a variety of match and optional match operations when searching for patterns
    recognize the use cases of the merge clause of a cypher query
    use the cypher query language to look for 2nd degree and higher degree connections between two nodes in a neo4j database
    perform union and intersect operations on data in a neo4j database using the cypher query language
    sort the results of a query execution using the order by clause
    demonstrates searching for specific nodes in a database using the bloom search bar
    configure the appearance of nodes and relationships in a neo4j bloom scene
  • describe the various data views available in the bloom user interface, such as the hierarchical and the presentation views
    use the neo4j bloom interface to analyze the nodes in your graphs, including the connections between them
    recognize the similarities and differences of data modeling approaches for relational, document and graph data
    use labels and properties for neo4j nodes in an optimal manner from the point of view of anticipated queries
    describe how data in a tabular structure containing many-to-one relationships can be modelled as a neo4j graph
    map the tables in a relational database to a graph structure using the neo4j etl tool
    redefine the nodes and relationships in your neo4j database using the apoc library
    migrating to aura with a dump file or using push-to-cloud
    write a python application to modify and read from the contents of an aura database
    connect to an aura database using the cypher shell and run queries against it
    install the graph data science library for a neo4j dbms
    create an in-memory graph using the native projection configuration for nodes and relationships
    load properties from a source database to an in-memory graph
    build a sub-graph containing a subset of elements from an already existing graph
    add properties to an in-memory graph based on the computation of an algorithm
    load properties from the source database of a graph when exporting it to a new database
    export in-memory graphs to a set of csv files containing data for nodes and relationships
    create nodes and relationships from the contents of csv files
    find individual nodes or clusters of nodes in a network which are not connected to one another
    create a graph where each relationship has an attached weight
    perform a breadth-first and depth-first traversal of a graph
    outline apache hadoop and its ecosystem, describe graphframes and their capabilities, and recognize where graphframes fit into the apache hadoop ecosystem
    demonstrate the identification of the most and the least-connected nodes in a graph
    search for patterns of relationships between the nodes in a spark graphframe
    use the breadth-first search and the shortestpaths functions to find the shortest paths between nodes in a graph
    describe the different operations performed by individual neurons in a layer of a neural network
    set up the python libraries required to use the spektral library for building a graph neural network (gnn)
    outline graph convolutional networks (gcns) and recognize the operations performed on input data when using a gcn, including symmetric normalization
    recognize the structure required to feed graph data into a graph convolutional network (gcn) model
    identify various factors which can influence the quality of predictions made by a gcn model


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