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Microsoft Certified Azure Data Scientist Associate



Azure Data Scientists apply Azure's machine learning techniques to train, evaluate, and deploy models that solve business problems.

Who should take this exam?

Candidates for this exam apply scientific rigor and data exploration techniques to gain actionable insights and communicate results to stakeholders. Candidates use machine learning techniques to train, evaluate, and deploy models to build AI solutions that satisfy business objectives. Candidates use applications that involve natural language processing, speech, computer vision, and predictive analytics.

Candidates serve as part of a multi-disciplinary team that incorporates ethical, privacy, and governance considerations into the solution. Candidates typically have background in mathematics, statistics, and computer science.

Benefits of training with us

  • Intuitive and rewarding online training resources

  • 24/7 access to our unique course materials

  • Custom built practical tasks and challenges
  • Official mock examinations to fully prepare you for any final exams
  • Career advice and CV support once training has been completed
  • Full printable course materials, allowing you take your learning offline at your own convenience

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Define and prepare the development environment
Select development environment
  • assess the deployment environment constraints

  • analyze and recommend tools that meet system requirements

  • select the development environment
Set up development environment
  • create an Azure data science environment

  • configure data science work environments
Quantify the business problem
  • define technical success metrics

  • quantify risks

Prepare data for modeling
Transform data into usable datasets
  • develop data structures

  • design a data sampling strategy

  • design the data preparation flow
Perform Exploratory Data Analysis (EDA)
  • review visual analytics data to discover patterns and determine next steps

  • identify anomalies, outliers, and other data inconsistencies

  • create descriptive statistics for a dataset
Cleanse and transform data
  • resolve anomalies, outliers, and other data inconsistencies

  • standardize data formats

  • set the granularity for data

Perform feature engineering
Perform feature extraction
  • perform feature extraction algorithms on numerical data

  • perform feature extraction algorithms on non-numerical data

  • scale features
Perform feature selection
  • define the optimality criteria

  • apply feature selection algorithms

Develop models
Select an algorithmic approach
  • determine appropriate performance metrics

  • implement appropriate algorithms

  • consider data preparation steps that are specific to the selected algorithms
Split datasets
  • determine ideal split based on the nature of the data

  • determine number of splits

  • determine relative size of splits

  • ensure splits are balanced
Identify data imbalances
  • resample a dataset to impose balance

  • adjust performance metric to resolve imbalances

  • implement penalization
Train the model
  • select early stopping criteria

  • tune hyper-parameters
Evaluate model performance
  • score models against evaluation metrics

  • implement cross-validation

  • identify and address overfitting

  • identify root cause of performance results
Configure and Manage Active Directory
Configure service authentication
  • Create and configure Service Accounts; create and configure Group Managed Service Accounts; configure Kerberos delegation; manage Service Principal Names (SPNs); configure virtual accounts
Configure Domain Controllers
  • Transfer and seize operations master roles; install and configure a read-only domain controller (RODC); configure Domain Controller cloning
Maintain Active Directory
  • Back up Active Directory and SYSVOL; manage Active Directory offline; optimize an Active Directory database; clean up metadata; configure Active Directory snapshots; perform object- and container-level recovery; perform Active Directory restore; configure and restore objects by using the Active Directory Recycle Bin
Configure account policies
  • Configure domain and local user password policy settings; configure and apply Password Settings Objects (PSOs); delegate password settings management; configure account lockout policy settings; configure Kerberos policy settings
Configure and Manage Group Policy
Configure Group Policy processing
  • Configure processing order and precedence; configure blocking of inheritance; configure enforced policies; configure security filtering and WMI filtering; configure loopback processing; configure and manage slow-link processing and Group Policy caching; configure client-side extension (CSE) behavior; force Group Policy Update
Configure Group Policy settings
  • Configure settings including software installation, folder redirection, scripts, and administrative template settings; import security templates; import custom administrative template file; configure property filters for administrative templates
Manage Group Policy objects (GPOs)
  • Back up, import, copy, and restore GPOs; create and configure Migration Table; reset default GPOs; delegate Group Policy management
Configure Group Policy Preferences (GPP)
  • Configure GPP settings including printers, network drive mappings, power options, custom registry settings, Control Panel settings, Internet Explorer settings, file and folder deployment, and shortcut deployment; configure item-level targeting

  • Official Microsoft DP-100 Exam

  • Online or classroom training

  • Interactive mock exams

  • Live Lab (access to LIVE software)

  • Free Phone Support

  • Online & Email Support

  • World wide Recognised Qualification

  • Unlimited access for 12 months
  • Data Scientist

  • Jr. Data Scientist

  • Google Cloud Data Architect

  • Data Scientist Research Programmer

  • Data Engineer

  • Analytics, Data Scientist

  • + More

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