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Guarantee your C1000-059 exam success with examkiller's study guide. The C1000-059 practice test questions are developed by experiences IBM Certification Professionals who working in todays prospering companies and IBM exam data center.

Exam Number: C1000-059

Exam Title: IBM AI Enterprise Workflow V1 Data Science Specialist

Format: Single and Multiple Choice

Duration: 90 Minutes

Number of Questions: 62

Number of questions to pass: 44

Passing Score: 71%

Origin Provider: ExamKiller

Total Questions: 62 QAs

Type: Real Exam Questions

Guarantee: 100% Pass Guarantee

Demo: Click Here for Check Demo

IBM C1000-059 Exam Objectives

• Section 1: Scientific, Mathematical, and technical essentials for Data Science and AI
  1. Explain the difference between Descriptive, Prescriptive, Predictive, Diagnostic, and Cognitive Analytics
  2. Describe and explain the key terms in the field of artificial intelligence (Analytics, Data Science, Machine Learning, Deep Learning, Artificial Intelligence etc.)
  3. Distinguish different streams of work within Data Science and AI (Data Engineering, Data Science, Data Stewardship, Data Visualization etc.)
  4. Describe the key stages of a machine learning pipeline.
  5. Explain the fundamental terms and concepts of design thinking
  6. Explain the different types of fundamental Data Science
  7. Distinguish and leverage key Open Source and IBM tools and technologies that can be used by a Data Scientist to implement AI solutions
  8. Explain the general properties of common probability distributions.
  9. Explain and calculate different types of matrix operations
• Section 2: Applications of Data Science and AI in Business
  1. Identify use cases where artificial intelligence solutions can address business opportunities
  2. Translate business opportunities into a machine learning scenario
  3. Differentiate the categories of machine learning algorithms and the scenarios where they can be used
  4. Show knowledge of how to communicate technical results to business stakeholders
  5. Demonstrate knowledge of scenarios for application of machine learning
• Section 3: Data understanding techniques in Data Science and AI
  1. Demonstrate knowledge of data collection practices
  2. Explain characteristics of different data types
  3. Show knowledge of data exploration techniques and data anomaly detection
  4. Use data summarization and visualization techniques to find relevant insight
• Section 4: Data preparation techniques in Data Science and AI
  1. Demonstrate expertise cleaning data and addressing data anomalies
  2. Show knowledge of feature engineering and dimensionality reduction techniques
  3. Demonstrate mastery preparing and cleaning unstructured text data
• Section 5: Application of Data Science and AI techniques and models
  1. Explain machine learning algorithms and the theoretical basis behind them
  2. Demonstrate practical experience building machine learning models and using different machine learning algorithms
• Section 6: Evaluation of AI models
  1. Identify different evaluation metrics for machine learning algorithms and how to use them in the evaluation of model performance
  2. Demonstrate successful application of model validation and selection methods
  3. Show mastery of model results interpretation
  4. Apply techniques for fine tuning and parameter optimization
• Section 7: Deployment of AI models
  1. Describe the key considerations when selecting a platform for AI model deployment
  2. Demonstrate knowledge of requirements for model monitoring, management and maintenance
  3. Identify IBM technology capabilities for building, deploying, and managing AI models
• Section 8: Technology Stack for Data Science and AI
  1. Describe the differences between traditional programming and machine learning
  2. Demonstrate foundational knowledge of using python as a tool for building AI solutions
  3. Show knowledge of the benefits of cloud computing for building and deploying AI models
  4. Show knowledge of data storage alternatives
  5. Demonstrate knowledge on open source technologies for deployment of AI solutions
  6. Demonstrate basic understanding of natural language processing
  7. Demonstrate basic understanding of computer vision
  8. Demonstrate basic understanding of IBM Watson AI services

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