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

070-774

Exam Code: 070-774

Exam Name: Perform Cloud Data Science with Azure Machine Learning

Updated: Aug 17, 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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  • Microsoft R Servers.
  • Cognitive Services/API
  • Algorithms for grouping unlabeled Data.
  • SQL/T-SQL
  • Python Languages.
  • Data Science
  • Machine Learning models
  • HDinsights
  • Azure Machine Learning.
  • Machine Learning Projects and workspace

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

Overview about Overview about MICROSOFT 70-774 Exam

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

How to book MICROSOFT 70-774 Certifications Exam?

  • Select Date and Center of examination and confirm with payment value of 165$
  • Visit to Microsoft Exam Registration
  • Signup/Login to MICROSOFT account
  • Search for MICROSOFT 70-774 Certifications Exam

Microsoft 070-774 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Use Other Services for Machine Learning10%- Use SQL Server R Services
- Integrate with HDInsight
- Work with Microsoft Cognitive Toolkit
- Leverage Cortana Intelligence Gallery
Topic 2: Prepare Data for Analysis in Azure Machine Learning30%- Explore and summarize data
  • 1. Visualize data distributions
    • 2. Generate statistical summaries
      - Perform feature engineering
      • 1. Create derived features
        • 2. Normalize and scale features
          - Cleanse and transform data
          • 1. Handle missing values and outliers
            • 2. Remove duplicates and inconsistent entries
              - Import and export data
              • 1. Connect to Azure storage and databases
                • 2. Import from files and external sources
                  • 3. Export results and datasets
                    Topic 3: Operationalize and Manage Azure Machine Learning Services25%- Manage workspaces and experiments
                    • 1. Share and collaborate
                      • 2. Create and organize projects
                        - Deploy models as web services
                        • 1. Publish to Azure ML Studio
                          • 2. Configure endpoints and access
                            - Consume models and APIs
                            • 1. Call deployed web services
                              • 2. Use Cognitive Services APIs
                                Topic 4: Develop Machine Learning Models35%- Train and tune models
                                • 1. Configure parameters
                                  • 2. Split training and test sets
                                    - Select appropriate algorithms
                                    • 1. Unsupervised learning methods
                                      • 2. Supervised learning methods
                                        - Validate and evaluate models
                                        • 1. Use accuracy, precision, recall, AUC
                                          • 2. Perform cross-validation
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