Passing exam with 070-774 latest training materials

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Updated: Sep 30, 2026

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Microsoft 070-774 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Perform Cloud Data Science with Azure Machine Learning
Exam Number:70-774
Related Certifications:70-773 Analyzing Big Data with Microsoft R
Azure Data Scientist Associate
Exam Price:165 USD
Exam Duration:120 minutes
Certificate Validity Period:Retired effective June 30, 2019
Exam Format:Drag and drop, Multiple choice, Scenario-based
Passing Score:700 out of 1000
Available Languages:English
Real Exam Qty:40-60
Recommended Training:Exam Ref 70-774 Book
Microsoft Azure ML Documentation
Exam Registration:Pearson VUE Registration
Microsoft Learning Page
Sample Questions:Microsoft 070-774 Sample Questions
Exam Way:Proctored onsite at Pearson VUE centers or online proctored
Pre Condition:No mandatory prerequisites; recommended knowledge of data science, Azure basics, and machine learning concepts
Official Syllabus URL:https://www.microsoft.com/en-us/learning/exam-70-774.aspx

For more info visit:

MICROSOFT 70-774 Exam Reference, Microsoft Documents

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

Benefits of having MICROSOFT 70-774 Certifications

MICROSOFT 70-774 Exam certification will not only help you learn some new skills, it can position you for a higher paying job or help you transform your current role into Data Scientist Professional. Get prepared to take this exam and become one of the very first Microsoft Azure Machine Learning Data Scientist Expert in the world by using our relevance quality of MICROSOFT 70-774 Exam study material and so we bring best-in-industry MICROSOFT 70-774 Exam online course and practice exams for you to help in your exam preparation.

Perform Cloud Data Science with Azure Machine learning 70-774 Dumps will include below mentioned topics with Exam focused percentage

  • Develop Machine Learning Models: 20-25%
  • 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%

Microsoft 070-774 Exam Syllabus Topics:

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

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