Microsoft 070-774 dumps - in .pdf

070-774 pdf
  • Exam Code: 070-774
  • Exam Name: Perform Cloud Data Science with Azure Machine Learning
  • Updated: Aug 16, 2026
  • Q & A: 65 Questions and Answers
  • PDF Price: $49.99
  • Free Demo

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  • Exam Code: 070-774
  • Exam Name: Perform Cloud Data Science with Azure Machine Learning
  • Updated: Aug 16, 2026
  • Q & A: 65 Questions and Answers
  • PDF Version + PC Test Engine + Online Test Engine
  • Value Pack Total: $99.98  $69.99
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Microsoft 070-774 dumps - Testing Engine

070-774 Testing Engine
  • Exam Code: 070-774
  • Exam Name: Perform Cloud Data Science with Azure Machine Learning
  • Updated: Aug 16, 2026
  • Q & A: 65 Questions and Answers
  • Software Price: $49.99
  • Testing Engine

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Overview about Overview about MICROSOFT 70-774 Exam

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

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

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

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Microsoft 070-774 Exam Syllabus Topics:

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

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