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IBM Updated C1000-059 Exam Blueprint, Syllabus and Topics

IBM AI Enterprise Workflow V1 Data Science Specialist

Last Update May 9, 2024
Total Questions : 62

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IBM C1000-059 Exam Overview :

Exam Name IBM AI Enterprise Workflow V1 Data Science Specialist
Exam Code C1000-059
Official Information https://www.ibm.com/certify/exam?id=C1000-059
See Expected Questions IBM C1000-059 Expected Questions in Actual Exam
Take Self-Assessment Use IBM C1000-059 Practice Test to Assess your preparation - Save Time and Reduce Chances of Failure

IBM C1000-059 Exam Topics :

Section Weight Objectives
Section 1: Scientific, Mathematical, and technical essentials for Data Science and AI  
  • Explain the difference between Descriptive, Prescriptive, Predictive, Diagnostic, and Cognitive Analytics
  • Describe and explain the key terms in the field of artificial intelligence (Analytics, Data Science, Machine Learning, Deep Learning, Artificial Intelligence etc.)
  • Distinguish different streams of work within Data Science and AI (Data Engineering, Data Science, Data Stewardship, Data Visualization etc.)
  • Describe the key stages of a machine learning pipeline.
  • Explain the fundamental terms and concepts of design thinking
  • Explain the different types of fundamental Data Science
  • Distinguish and leverage key Open Source and IBM tools and technologies that can be used by a Data Scientist to implement AI solutions
  • Explain the general properties of common probability distributions.
  • Explain and calculate different types of matrix operations
Section 2: Applications of Data Science and AI in Business  
  • Identify use cases where artificial intelligence solutions can address business opportunities
  • Translate business opportunities into a machine learning scenario
  • Differentiate the categories of machine learning algorithms and the scenarios where they can be used
  • Show knowledge of how to communicate technical results to business stakeholders
  • Demonstrate knowledge of scenarios for application of machine learning
Section 3: Data understanding techniques in Data Science and AI  
  • Demonstrate knowledge of data collection practices
  • Explain characteristics of different data types
  • Show knowledge of data exploration techniques and data anomaly detection
  • Use data summarization and visualization techniques to find relevant insight
Section 4: Data preparation techniques in Data Science and AI  
  • Demonstrate expertise cleaning data and addressing data anomalies
  • Show knowledge of feature engineering and dimensionality reduction techniques
  • Demonstrate mastery preparing and cleaning unstructured text data
Section 5: Application of Data Science and AI techniques and models  
  • Explain machine learning algorithms and the theoretical basis behind them
  • Demonstrate practical experience building machine learning models and using different machine learning algorithms
Section 6: Evaluation of AI models  
  • Identify different evaluation metrics for machine learning algorithms and how to use them in the evaluation of model performance
  • Demonstrate successful application of model validation and selection methods
  • Show mastery of model results interpretation
  • Apply techniques for fine tuning and parameter optimization
Section 7: Deployment of AI models  
  • Describe the key considerations when selecting a platform for AI model deployment
  • Demonstrate knowledge of requirements for model monitoring, management and maintenance
  • Identify IBM technology capabilities for building, deploying, and managing AI models
Section 8: Technology Stack for Data Science and AI  
  • Describe the differences between traditional programming and machine learning
  • Demonstrate foundational knowledge of using python as a tool for building AI solutions
  • Show knowledge of the benefits of cloud computing for building and deploying AI models
  • Show knowledge of data storage alternatives
  • Demonstrate knowledge on open source technologies for deployment of AI solutions
  • Demonstrate basic understanding of natural language processing
  • Demonstrate basic understanding of computer vision
  • Demonstrate basic understanding of IBM Watson AI services

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