IBM C1000-059 real exam prep : IBM AI Enterprise Workflow V1 Data Science Specialist

  • Exam Code: C1000-059
  • Exam Name: IBM AI Enterprise Workflow V1 Data Science Specialist
  • Updated: Aug 26, 2026
  • Q&As: 64 Questions and Answers

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C1000-059 exam dumps

IBM AI Enterprise Workflow Data Science Specialist Exam Certification Details:

Exam Price$200 (USD)
Duration90 mins
Schedule ExamPearson VUE
Exam NameIBM Certified Specialist - AI Enterprise Workflow V1
Exam CodeC1000-059
Sample QuestionsIBM AI Enterprise Workflow Data Science Specialist Sample Questions
Books / TrainingCoursera - AI Enterprise Workflow Certification Training
Number of Questions62
Passing Score44 / 62

Reference: https://www.ibm.com/certify/exam?id=C1000-059

IBM C1000-059 Exam Syllabus Topics:

TopicDetails
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
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
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
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
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
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
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
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

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