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IBM AI Enterprise Workflow Data Science Specialist Exam Certification Details:
| Duration | 90 mins |
| Number of Questions | 62 |
| Schedule Exam | Pearson VUE |
| Exam Name | IBM Certified Specialist - AI Enterprise Workflow V1 |
| Passing Score | 44 / 62 |
| Exam Price | $200 (USD) |
| Exam Code | C1000-059 |
| Books / Training | Coursera - AI Enterprise Workflow Certification Training |
| Sample Questions | IBM AI Enterprise Workflow Data Science Specialist Sample Questions |
Reference: https://www.ibm.com/certify/exam?id=C1000-059
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IBM C1000-059 Exam Syllabus Topics:
| Topic | Details |
|---|---|
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
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