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Updated: Sep 25, 2026

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The Microsoft 070-774 exam evaluates candidates' knowledge about Azure Machine Learning services that enable data scientists and machine learning engineers to build and deploy machine learning solutions. It covers the different operational steps required for the preparation, analysis, algorithm development, and model consumption stages of a machine learning solution. 070-774 exam will test your proficiency in building effective machine learning models using various Azure services like Azure Data Factory, Azure Stream Analytics, Cognitive Services, and more.

Microsoft 070-774 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Perform Cloud Data Science with Azure Machine Learning
Exam Number:70-774
Passing Score:700 out of 1000
Available Languages:English
Related Certifications:Azure Data Scientist Associate
70-773 Analyzing Big Data with Microsoft R
Exam Price:165 USD
Exam Format:Drag and drop, Scenario-based, Multiple choice
Exam Duration:120 minutes
Real Exam Qty:40-60
Certificate Validity Period:Retired effective June 30, 2019
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

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.

Microsoft 070-774 is a certification exam designed by Microsoft to test a candidate's proficiency in performing cloud-based data science tasks using Azure Machine Learning. 070-774 exam is designed to verify a candidate's ability to carry out various tasks related to developing and deploying data models, preparing data for processing, and building machine learning solutions.

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

Microsoft 070-774 Exam Syllabus Topics:

SectionWeightObjectives
Use Other Services for Machine Learning10%- Integrate with HDInsight
- Leverage Cortana Intelligence Gallery
- Use SQL Server R Services
- Work with Microsoft Cognitive Toolkit
Operationalize and Manage Azure Machine Learning Services25%- Consume models and APIs
  • 1. Call deployed web services
    • 2. Use Cognitive Services APIs
      - 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
              Prepare Data for Analysis in Azure Machine Learning30%- Perform feature engineering
              • 1. Create derived features
                • 2. Normalize and scale features
                  - 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. Connect to Azure storage and databases
                              • 3. Import from files and external sources
                                Develop Machine Learning Models35%- Train and tune models
                                • 1. Split training and test sets
                                  • 2. Configure parameters
                                    - Validate and evaluate models
                                    • 1. Perform cross-validation
                                      • 2. Use accuracy, precision, recall, AUC
                                        - Select appropriate algorithms
                                        • 1. Unsupervised learning methods
                                          • 2. Supervised learning methods

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