Try Microsoft : 070-774 valid & accurate questions and answers

Updated: Sep 27, 2026

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Analyzing Big Data with Microsoft R 70-773 Dumps Provided Study Notes

Exams-boost expert team recommend you to prepare some notes on these topics along with it don't forget to practice Perform Cloud Data Science with Azure Machine Learning 70-774 Dumps which been written by our expert team, Both these will help you a lot to clear this exam with good marks.

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

Microsoft 070-774 Exam Overview:

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

Difficulty in writing MICROSOFT 70-774 Exam

It is one of the toughest exam and it requires you a lot of hands-on experience. It is advisable to have prior knowledge of Data analytics on Microsoft. The more experience you have, the more it is beneficial for you, Major challenging things which you can find in exam is about different scenario based questions, you must have strong understanding of Big Data, Machine Learning, Data Science, Data Analytics.

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

For more info visit:

MICROSOFT 70-774 Exam Reference, Microsoft Documents

Microsoft 070-774 Exam Syllabus Topics:

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

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