A1000-144 Exam Dumps
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| Certification Provider: | IBM |
|---|---|
| Exam Code / Number: | A1000-144 |
| Exam Name: | Assessment: IBM Machine Learning Data Scientist v1 |
| Exam Questions: | 0 |
| Corresponding Certification: | IBM Certification |
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IBM A1000-144 certification exam is intended for data scientists who have experience in machine learning and data analysis. A1000-144 exam tests the candidate's ability to develop and deploy machine learning models using various tools and techniques. It covers topics such as data preparation, feature engineering, model selection, and evaluation, as well as deep learning and neural networks.
IBM A1000-144 certification exam is an online proctored exam that consists of 60 multiple-choice questions. A1000-144 exam has a time limit of 90 minutes, and the passing score is 68%. A1000-144 exam is available in English, Japanese, and Chinese, and it can be taken from anywhere in the world.
IBM A1000-144 certification exam is a challenging exam that requires extensive preparation and study. Candidates must have a solid understanding of machine learning and data science concepts and be familiar with the tools and technologies used in the field. Obtaining this certification demonstrates the candidate's commitment to professional development and their ability to work effectively in a data-driven environment.
IBM A1000-144 (Assessment: IBM Machine Learning Data Scientist v1) Certification Exam is designed for professionals who want to validate their skills and knowledge in the field of machine learning. It is an industry-recognized certification that demonstrates the ability to build and deploy machine learning models using IBM Watson Studio and IBM Cloud Pak for Data. A1000-144 exam covers a range of topics, including data pre-processing, model selection, evaluation, and deployment.
IBM A1000-144 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Refine and Deploy the Model | 18% | - Implement model explainability - Train the model and optimize hyperparameters - Identify operations and transformations used for feature selection and engineering - Configure environment specifications for model training - Select appropriate tools |
| Implement the Proper Model | 26% | - Implement unsupervised learning using clustering - Implement supervised learning using regression - Implement unsupervised learning using dimensional reduction - Implement supervised learning using classification |
| Exploratory Data Analysis Including Data Preparation | 18% | - Balance and partition data - Visualize data - Identify methods used to clean, label, and anonymize data |
| Evaluate Business Problem Including Ethical Implications | 21% | - Assess progress on the AI Ladder - Understand ethical challenges in the business problem - Perform AI design thinking - Understand what data is available - Understand business requirements |
| Monitor Models in Production | 17% | - Determine whether unfair bias exists in the model - Monitor the model in production - Assess the model |