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IBM C1000-059 Exam Topics, Blueprint and Syllabus

IBM AI Enterprise Workflow V1 Data Science Specialist

Last Update December 19, 2024
Total Questions : 62

Our IBM Data and AI: Data and AI C1000-059 exam questions and answers cover all the topics of the latest IBM AI Enterprise Workflow V1 Data Science Specialist exam, See the topics listed below. We also provide IBM C1000-059 exam dumps with accurate exam content to help you prepare for the exam quickly and easily. Additionally, we offer a range of IBM C1000-059 resources to help you understand the topics covered in the exam, such as IBM Data and AI: Data and AI video tutorials, C1000-059 study guides, and C1000-059 practice exams. With these resources, you can develop a better understanding of the topics covered in the exam and be better prepared for success.

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IBM C1000-059 Exam Overview :

Exam Name IBM AI Enterprise Workflow V1 Data Science Specialist
Exam Code C1000-059
Actual Exam Duration The duration of the IBM C1000-059 exam is 90 minutes.
What exam is all about IBM C1000-059 is an IBM Cloud Pak for Data V2.5 Administrator certification exam. It is designed to test the knowledge and skills of a candidate in the areas of installation, configuration, and administration of IBM Cloud Pak for Data V2.5. The exam covers topics such as installation and configuration of IBM Cloud Pak for Data, managing and monitoring the platform, and troubleshooting and resolving issues.
Passing Score required The passing score required in the IBM C1000-059 exam is 44.
Competency Level required The IBM C1000-059 exam is an intermediate-level certification exam. It requires a basic understanding of IBM Cloud Pak for Data and the ability to apply the knowledge to solve problems.
Questions Format The IBM C1000-059 exam consists of multiple-choice questions.
Delivery of Exam IBM C1000-059 exam is delivered through the Pearson VUE testing platform.
Language offered The IBM C1000-059 exam is offered in English.
Cost of exam The cost of the IBM C1000-059 exam is $200 USD.
Target Audience The IBM C1000-059 certification exam is designed for IT professionals who have experience in developing, deploying, and managing applications on IBM Cloud. This includes professionals who have experience in developing, deploying, and managing applications on IBM Cloud, such as cloud architects, cloud developers, cloud administrators, and cloud operations professionals.
Average Salary in Market The average salary for someone with IBM C1000-059 certification is difficult to estimate as it depends on a variety of factors such as experience, location, and job role. However, according to PayScale, the average salary for someone with IBM C1000-059 certification is $90,000 per year.
Testing Provider IBM does not provide C1000-059 exams for testing. The C1000-059 exam is an IBM certification exam that is used to assess the knowledge and skills of professionals in the IBM Cloud: Digital Business Automation field. Candidates must register and pay for the exam in order to take it.
Recommended Experience The recommended experience for the IBM C1000-059 exam is at least two years of experience in developing, deploying, and managing applications on IBM Cloud. Candidates should also have a good understanding of IBM Cloud services, such as IBM Cloud Foundry, IBM Cloud Kubernetes Service, IBM Cloud Functions, and IBM Cloud Object Storage. Additionally, candidates should have a good understanding of IBM Cloud security and compliance.
Prerequisite The IBM C1000-059 exam requires that you have a basic understanding of IBM Cloud Pak for Data, IBM Watson Studio, IBM Watson Machine Learning, IBM Watson Knowledge Catalog, IBM Watson OpenScale, and IBM Watson Natural Language Understanding. Additionally, you should have a working knowledge of data science and machine learning concepts, such as data wrangling, data visualization, predictive analytics, and model deployment.
Retirement (If Applicable) The IBM C1000-059 exam does not have an expected retirement date.
Certification Track (RoadMap): The IBM C1000-059 certification track/roadmap is a comprehensive guide to help you prepare for the IBM C1000-059 exam. It includes a detailed overview of the exam objectives, recommended study materials, and tips for success. The roadmap also provides a timeline for completing the exam, including the recommended study time and the estimated time to complete the exam. Additionally, the roadmap provides links to additional resources to help you prepare for the exam.
Official Information https://www.ibm.com/certify/exam?id=C1000-059
See Expected Questions IBM C1000-059 Expected Questions in Actual Exam
Take Self-Assessment Use IBM C1000-059 Practice Test to Assess your preparation - Save Time and Reduce Chances of Failure

IBM C1000-059 Exam Topics :

Section Weight Objectives
Section 1: Scientific, Mathematical, and technical essentials for Data Science and AI  
  • 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
Section 2: Applications of Data Science and AI in Business  
  • 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
Section 3: Data understanding techniques in Data Science and AI  
  • 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
Section 4: Data preparation techniques in Data Science and AI  
  • 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
Section 5: Application of Data Science and AI techniques and models  
  • Explain machine learning algorithms and the theoretical basis behind them
  • Demonstrate practical experience building machine learning models and using different machine learning algorithms
Section 6: Evaluation of AI models  
  • 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
Section 7: Deployment of AI models  
  • 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
Section 8: Technology Stack for Data Science and AI  
  • 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