IBM Certified Specialist – AI Enterprise Workflow V1

十月 18, 2022 by
Filed under: killtest 

IBM Certified Specialist – AI Enterprise Workflow V1
數據科學家專家擅長使用 IBM 方法和技術,在設計思維視角和方法論中,通過機器學習解決方案解決業務問題。這包括將機器學習解決方案與企業要求和業務優先順序聯繫起來的能力,以及在實施企業 AI 工作流時應用理解。

考試 C1000-059:IBM AI 企業工作流 V1 數據科學專家
試題數量: 62 通
題數: 44
允許時間: 90 分鐘
狀態: 直播
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
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
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
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
Explain machine learning algorithms and the theoretical basis behind them
Demonstrate practical experience building machine learning models and using different machine learning algorithms
第6章 AI模型的評估
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
第7章 AI模型的部署
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
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
每次考試價格:200 美元


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