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

070-774
  • Exam Code: 070-774
  • Exam Name: Perform Cloud Data Science with Azure Machine Learning
  • Updated: Aug 10, 2026
  • Q & A: 65 Questions and Answers
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Overview about Overview about MICROSOFT 70-774 Exam

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

Perform Cloud Data Science with Azure Machine Learning 70-774 Exam

Perform Cloud Data Science with Azure Machine Learning 70-774 Exam is centered around Azure Machine Learning, Bot Framework, and Cognitive Services. The exam is designed to target candidates who are Data Scientists or Analysts using the Microsoft Azure cloud services to build and deploy intelligent solutions. This exam will test your understanding and familiarity with common data science processes such as filtering and transforming data sets, model estimation, and model evaluation. Machine Learning is one of the hottest topics and technologies in the industry right now.

Machine Learning is being used to combine with Big Data to automate the processes of gaining insights and predicting future behavior of systems, devices, and people. In an effort to expand out the breadth of certifications and exams offered around Microsoft Azure and the cloud, Microsoft has added the Perform Cloud Data Science with Azure Machine Learning Microsoft 70-774 Exam. This exam only available for the candidates till 30th June 2019 after that this may get replace with some other exam so you can plan get it completed before it get expire.

We think our Perform Cloud Data Science with Azure Machine Learning 70-774 Exam Practice Test Paper and Dumps will provide you 100% confidence to make you appear for Microsoft 70-774 Exam

This is the list of the contents in our Perform Cloud Data Science with Azure Machine Learning 70-774

  • Explore and summarize data
  • Cleanse data for Azure Machine Learning
  • Validate models
  • Azure Machine Learning models
  • Import and Export Data to and from ML.
  • Perform data sciences at scale by using HD Insights

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

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

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

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Microsoft 070-774 Exam Syllabus Topics:

SectionWeightObjectives
Use Other Services for Machine Learning10%- Use SQL Server R Services
- Leverage Cortana Intelligence Gallery
- Work with Microsoft Cognitive Toolkit
- Integrate with HDInsight
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. Use Cognitive Services APIs
            • 2. Call deployed web services
              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
                      - 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. Handle missing values and outliers
                              • 2. Remove duplicates and inconsistent entries
                                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. Supervised learning methods
                                          • 2. Unsupervised learning methods

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