Microsoft 070-774 Q&A - in .pdf

  • 070-774 pdf
  • Exam Code: 070-774
  • Exam Name: Perform Cloud Data Science with Azure Machine Learning
  • Updated: Aug 08, 2026
  • Q & A: 65 Questions and Answers
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  • Exam Code: 070-774
  • Exam Name: Perform Cloud Data Science with Azure Machine Learning
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  • Updated: Aug 08, 2026
  • Q & A: 65 Questions and Answers
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  • Value Pack Total: $99.98  $69.99
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Microsoft 070-774 Q&A - Testing Engine

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  • Exam Code: 070-774
  • Exam Name: Perform Cloud Data Science with Azure Machine Learning
  • Updated: Aug 08, 2026
  • Q & A: 65 Questions and Answers
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Analyzing Big Data with Microsoft R 70-773 Dumps Provided Study Notes

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

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070-774 Practice Dumps

Overview about Overview about MICROSOFT 70-774 Exam

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

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

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Microsoft 070-774 Exam Syllabus Topics:

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