Microsoft 070-774 - Perform Cloud Data Science with Azure Machine Learning

Microsoft 070-774 Actual PDF
  • Exam Code: 070-774
  • Exam Name: Perform Cloud Data Science with Azure Machine Learning
  • Updated: Jul 26, 2026
  • Q & A: 65 Questions and Answers
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Perform Cloud Data Science with Azure Machine learning 70-774 Dumps will include below mentioned topics with Exam focused percentage

  • Use Other Services for Machine Learning: 20-25%
  • Prepare Data for Analysis in Azure Machine Learning and Export from Azure Machine Learning: 20-25%
  • Operationalize and Manage Azure Machine Learning Services: 20-25%
  • Develop Machine Learning Models: 20-25%

Reference: https://www.microsoft.com/en-us/learning/exam-70-774.aspx

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TestPDF expert team recommend you to prepare some notes on these topics along with it don't forget to practice Perform Cloud Data Science with Azure Machine Learning 70-774 Dumps which been written by our expert team, Both these will help you a lot to clear this exam with good marks.

  • SQL/T-SQL
  • Data Science
  • Python Languages.
  • Azure Machine Learning.
  • Algorithms for grouping unlabeled Data.
  • Cognitive Services/API
  • Microsoft R Servers.
  • HDinsights
  • Machine Learning models
  • Machine Learning Projects and workspace

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Overview about Overview about MICROSOFT 70-774 Exam

  • Registration Fee: 165 USD
  • Number of Questions: 40-60
  • Length of Examination: 120 minutes
  • Format: Multiple choice, multiple answer
  • Passing Score: 70-80%

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Microsoft 070-774 Exam Syllabus Topics:

SectionObjectives
Deploy and manage models- Model deployment in Azure
  • 1. Batch inference pipelines
    • 2. Deploy to Azure web services
      - Operationalization
      • 1. Update and retrain models
        • 2. Monitor deployed models
          Develop machine learning models- Select and implement algorithms
          • 1. Supervised learning models
            • 2. Unsupervised learning models
              - Training models in Azure Machine Learning
              • 1. Use Azure ML Studio experiments
                • 2. Train models using Python or R scripts
                  Evaluate and optimize models- Model evaluation techniques
                  • 1. ROC and AUC analysis
                    • 2. Accuracy, precision, recall, F1 score
                      - Model tuning and improvement
                      • 1. Hyperparameter tuning
                        • 2. Feature selection and engineering
                          Prepare and process data for Azure Machine Learning- Data ingestion and storage
                          • 1. Connect to Azure storage services
                            • 2. Import data into Azure Machine Learning datasets
                              - Data preparation and transformation
                              • 1. Transform features for modeling
                                • 2. Clean missing or inconsistent data

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