Final Exam: Data Ops

WHAT YOU WILL LEARN

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  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
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  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