Full Name: IBM Certified Specialist - AI Enterprise Workflow V1
Exam Code: C1000-059
IBM AI Enterprise Workflow Data Science Specialist Exam Summary:
Exam Name
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IBM Certified Specialist - AI Enterprise Workflow V1
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Exam Code
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C1000-059
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Exam Price
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$200 (USD)
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Duration
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90 mins
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Number of Questions
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62
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Passing Score
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44 / 62
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Training
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Sample Questions
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Practice Exam
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IBM C1000-059 Exam Syllabus Topics:
| Topic | Details |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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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