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

070-774 real exams

Exam Code: 070-774

Exam Name: Perform Cloud Data Science with Azure Machine Learning

Updated: Aug 16, 2026

Q & A: 65 Questions and Answers

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About Microsoft 070-774 Exam

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%
  • Develop Machine Learning Models: 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%

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Overview about Overview about MICROSOFT 70-774 Exam

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

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

Microsoft 070-774 Exam Syllabus Topics:

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

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