Use this quick start guide to collect all the information about IBM AI Enterprise Workflow Data Science Specialist (C1000-059) Certification exam. This study guide provides a list of objectives and resources that will help you prepare for items on the C1000-059 IBM AI Enterprise Workflow V1 Data Science Specialist exam. The Sample Questions will help you identify the type and difficulty level of the questions and the Practice Exams will make you familiar with the format and environment of an exam. You should refer this guide carefully before attempting your actual IBM AI Enterprise Workflow Data Science Specialist certification exam.
The IBM AI Enterprise Workflow Data Science Specialist certification is mainly targeted to those candidates who want to build their career in IBM Data and AI - Data and AI domain. The IBM Certified Specialist - AI Enterprise Workflow V1 exam verifies that the candidate possesses the fundamental knowledge and proven skills in the area of IBM AI Enterprise Workflow Data Science Specialist.
IBM AI Enterprise Workflow Data Science Specialist Exam Summary:
| Exam Name | IBM Certified Specialist - AI Enterprise Workflow V1 |
| Exam Code | C1000-059 |
| Exam Price | $200 (USD) |
| Duration | 90 mins |
| Number of Questions | 62 |
| Passing Score | 71% |
| Books / Training | IBM AI Enterprise Specialist |
| Schedule Exam | Pearson VUE |
| Sample Questions | IBM AI Enterprise Workflow Data Science Specialist Sample Questions |
| Practice Exam | IBM C1000-059 Certification Practice Exam |
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 |
To ensure success in IBM AI Enterprise Workflow Data Science Specialist certification exam, we recommend authorized training course, practice test and hands-on experience to prepare for IBM AI Enterprise Workflow V1 Data Science Specialist (C1000-059) exam.