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C1000-059 Exam Topics and Domains
C1000-059 is organized into 8 weighted domains. Expect to work with Cloud Pak for Data, IBM Cloud, Watson OpenScale, Watson Studio, and more.
Scientific, Mathematical, and Technical Essentials for Data Science and AI
Analytics Types
Explain the difference between Descriptive, Prescriptive, Predictive, Diagnostic, and Cognitive Analytics
AI and Data Science Terminology
- Describe and explain the key terms in the field of artificial intelligence
- Distinguish different streams of work within Data Science and AI
Machine Learning Fundamentals
- Describe the key stages of a machine learning pipeline
- Explain the fundamental terms and concepts of design thinking
Tools and Technologies
Distinguish and leverage key Open Source and IBM tools and technologies that can be used by a Data Scientist to implement AI solutions
Mathematical Foundations
- Explain the general properties of common probability distributions
- Explain and calculate different types of matrix operations
Applications of Data Science and AI in Business
Business Use Cases
- Identify use cases where artificial intelligence solutions can address business opportunities
- Translate business opportunities into a machine learning scenario
ML Algorithm Categories
Differentiate the categories of machine learning algorithms and the scenarios where they can be used
Business Communication
- 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
Data Collection
Demonstrate knowledge of data collection practices
Data Types and Characteristics
Explain characteristics of different data types
Data Exploration
- 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
Data Cleaning
Demonstrate expertise cleaning data and addressing data anomalies
Feature Engineering
Show knowledge of feature engineering and dimensionality reduction techniques
Text Data Preparation
Demonstrate mastery preparing and cleaning unstructured text data
Application of Data Science and AI Techniques and Models
Machine Learning Algorithms
- 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
Evaluation Metrics
Identify different evaluation metrics for machine learning algorithms and how to use them in the evaluation of model performance
Model Validation
Demonstrate successful application of model validation and selection methods
Model Optimization
- Show mastery of model results interpretation
- Apply techniques for fine tuning and parameter optimization
Deployment of AI Models
Deployment Platforms
Describe the key considerations when selecting a platform for AI model deployment
Model Management
Demonstrate knowledge of requirements for model monitoring, management and maintenance
IBM Technologies
Identify IBM technology capabilities for building, deploying, and managing AI models
Technology Stack for Data Science and AI
Programming Paradigms
Describe the differences between traditional programming and machine learning
Python for AI
Demonstrate foundational knowledge of using python as a tool for building AI solutions
Cloud Computing
Show knowledge of the benefits of cloud computing for building and deploying AI models
Data Storage
Show knowledge of data storage alternatives
Open Source Technologies
Demonstrate knowledge on open source technologies for deployment of AI solutions
Specialized AI Domains
- Demonstrate basic understanding of natural language processing
- Demonstrate basic understanding of computer vision
- Demonstrate basic understanding of IBM Watson AI services
How do I earn this certification?
Passing C1000-059 earns the IBM Certified Specialist - AI Enterprise Workflow V1 certification. It sits in the Data and AI track.
- C1000-169 - IBM Cloud Pak for Data v4.6 Administrator
- C1000-112 - Fundamentals of Quantum Computation Using Qiskit v0.2X Developer
- C1000-182 - IBM Sterling File Gateway v6.2 Administration Data integration and management skills
- C1000-141 - IBM Maximo Manage v8.0 ImplementationAI application in asset management
- C1000-065 - IBM Cognos Analytics Developer V11.1.x Complementary analytics and visualization skills
Practice with Precision
The PlanetCert Simulator mirrors the real exam environment with authentic questions and timed pressure.
How to study for this exam?
The most effective way to prepare for C1000-059 is by using the PlanetCert Simulator to practice questions and review detailed explanations.
What's changed on this exam?
- Watson Studio Latest Enhanced AutoAI features may appear in future exam updates • Release date: 2024-10-01
- Python Libraries Various Core libraries remain stable but new features continuously added • Release date: Ongoing
Who should take this exam?
- 6+ months of experience working as a Data Scientist
- Experience using IBM methods and open technologies to solve business problems
- Knowledge of machine learning and deep learning fundamentals
- Experience with Python programming
- Understanding of data preparation and feature engineering techniques
- Familiarity with model evaluation and deployment practices