Use this quick start guide to collect all the information about IBM Machine Learning Data Scientist (C1000-144) Certification exam. This study guide provides a list of objectives and resources that will help you prepare for items on the C1000-144 IBM Machine Learning Data Scientist v1 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 Machine Learning Data Scientist certification exam.
The IBM Machine Learning Data Scientist 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 Data Scientist - Machine Learning Specialist v1 exam verifies that the candidate possesses the fundamental knowledge and proven skills in the area of IBM Machine Learning Data Scientist.
IBM Machine Learning Data Scientist Exam Summary:
| Exam Name | IBM Certified Data Scientist - Machine Learning Specialist v1 |
| Exam Code | C1000-144 |
| Exam Price | $200 (USD) |
| Duration | 90 mins |
| Number of Questions | 61 |
| Passing Score | 74% |
| Books / Training | IBM Machine Learning Specialist |
| Schedule Exam | Pearson VUE |
| Sample Questions | IBM Machine Learning Data Scientist Sample Questions |
| Practice Exam | IBM C1000-144 Certification Practice Exam |
IBM C1000-144 Exam Syllabus Topics:
| Topic | Details | Weights |
|---|---|---|
| Evaluate business problem including ethical implications |
- Understand business requirements - Understand what data is available - Understand ethical challenges in the business problem - Perform AI design thinking - Assess progress on the AI Ladder |
21% |
| Exploratory Data Analysis including data preparation |
- Identify the methods used to clean, label, and anonymize data - Visualize data - Balance and partition data |
18% |
| Implement the proper model |
- Implement Supervised Learning: Regression - Implement Supervised Learning: Classification - Implement Unsupervised Learning: Clustering - Implement Unsupervised Learning: Dimensional Reduction |
26% |
| Refine and deploy the model |
- Identify operations and transformations taken to select and engineer features - Select the proper tools - Configure the appropriate environment specifications for training the model - Train the model and optimize hyperparameters - Implement the ability for the model to explain itself |
18% |
| Monitor models in production |
- Assess the model - Monitor the model in production - Determine if there is unfair bias in the model |
17% |
To ensure success in IBM Machine Learning Data Scientist certification exam, we recommend authorized training course, practice test and hands-on experience to prepare for IBM Machine Learning Data Scientist v1 (C1000-144) exam.