Final Exam: Data Ops

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Final Exam: Data Ops will test your knowledge and application of the topics presented throughout the Data Ops track of the Skillsoft Aspire Data Analyst to Data Scientist Journey.


  • Identify the benefits of rolling out a successful data compliance program
    describe the common compliance standards that a data scientist needs to be familiar with including gdpr, hippa, pci dss, soc 2
    create dashboards using elk
    specify the different types of dashboards and with their associated features and benefits
    identify the common compliance standards that a data scientist needs to be familiar with including gdpr, hippa, pci dss, soc 3
    implement effective security controls to protect data
    describe the different types of data that are used in analysis and types of visualizations that can be created from the data
    demonstrate the essential approaches of using iot device simulator
    use microsoft system center configuration manager to view managed device security compliance
    recognize how to turn big data to smart data and how to use data volumes
    understand key security risks associated with distributed processing frameworks
    demonstrate detecting anomalies using boxplot and scatter plot
    connect a web application to aws iot using mqtt over websockets
    understand how to deploy a vpn using azure to secure data in motion
    prominent anomaly detection techniques
    recognize the features of change streams in mongodb
    enable microsoft bitlocker to protect data at rest
    list the steps in involved in processing streaming data, the transformation of streams, and the materialization of the results of the transformation
    demonstrate how to detect anomalies using r, rcp, and the devtools package
    use the aws console to load datasets to amazon s3 and then load that data into a table provisioned on a redshift cluster
    create tables, load data, and run queries
    use sql server to rollback databases to a specific point in time
    configure a streaming data source using netcat and write an application to process the stream
    recall methods of encrypting sensitive data
    identify the types of data that need to be governed
    identify the steps involved in transforming big data to smart data using k-nn
    discuss the five main requirements for data governance
    work with spark sql in order to process streaming data using sql queries
    generate streams of weather data using the mqtt messaging protocol
    specify how to design a data governance process
  • recognize the key aspects of working with structured streaming in spark
    mitigate data breach events by identifying weaknesses
    describe why we need data governance
    different uses for data science analytic tools
    identify the role iam plays in a data governance framework
    create charts and dashboards using qlikview
    contextual data and collective anomaly detection using scikit-learn
    describe different uses for data science visualization tools
    implement multi-document transaction management using replica set in mongodb
    understand how data streams are secured
    describe how the use of a message transport decouples a streaming application from the sources of streaming data
    install the aws command line interface and use it to create and delete redshift clusters
    identify how data access can be monitored through siem and reports
    understand key security concerns related to nosql databases
    identify the approaches and the steps involved in setting up aws iot greengrass
    demonstrate the mathematical approaches of detecting anomalies
    use the redshift query editor to create tables, load data, and run queries
    describe the various smart data solution implementation frameworks
    create an iam role on aws that includes the necessary permissions to interact with the redshift and s3 services
    list essential sql server change data capture features
    describe the cloud architectures of iot from the perspective of microsoft azure, aws, and gcp
    list sql server rollback mechanisms
    configure file system object auditing using group policy
    recognize how to implement clustering on smart data
    recognize the differences between batch and streaming data and the types of streaming data sources
    run queries on data in a redshift cluster and use the query evaluation feature to analyze the query execution metrics
    identify the essential components that are involved in building a productive dashboard
    describe what devops is and some of the common functionalities
    use the quicksight dashboard to generate a time series plot to visualize sales at a retailer over time
    recognize the critical benefits provided by leaderboards and scorecards


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