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

070-774 real exams

Exam Code: 070-774

Exam Name: Perform Cloud Data Science with Azure Machine Learning

Updated: Oct 02, 2026

Q & A: 65 Questions and Answers

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MICROSOFT 70-774 Exam Reference, Microsoft Documents

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

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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

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

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

  • Prepare Data for Analysis in Azure Machine Learning and Export from Azure Machine Learning: 20-25%
  • Use Other Services for Machine Learning: 20-25%
  • Operationalize and Manage Azure Machine Learning Services: 20-25%
  • Develop Machine Learning Models: 20-25%

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

Microsoft 070-774 Exam Syllabus Topics:

SectionObjectives
Topic 1: Prepare and process data for Azure Machine Learning- Data preparation and transformation
  • 1. Clean missing or inconsistent data
    • 2. Transform features for modeling
      - Data ingestion and storage
      • 1. Import data into Azure Machine Learning datasets
        • 2. Connect to Azure storage services
          Topic 2: Evaluate and optimize models- Model tuning and improvement
          • 1. Feature selection and engineering
            • 2. Hyperparameter tuning
              - Model evaluation techniques
              • 1. ROC and AUC analysis
                • 2. Accuracy, precision, recall, F1 score
                  Topic 3: Deploy and manage models- Operationalization
                  • 1. Monitor deployed models
                    • 2. Update and retrain models
                      - Model deployment in Azure
                      • 1. Deploy to Azure web services
                        • 2. Batch inference pipelines
                          Topic 4: Develop machine learning models- Training models in Azure Machine Learning
                          • 1. Train models using Python or R scripts
                            • 2. Use Azure ML Studio experiments
                              - Select and implement algorithms
                              • 1. Unsupervised learning models
                                • 2. Supervised learning models

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