Ibm AI Enterprise Workflow V1 Data Science Specialist

C1000-059

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Multiple choiceFill in the blankDiagramsCase studies

What's Included

167
Practice Questions
1
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Aug 11, 2025
Updated

Complete Exam Package

167 C1000-059 practice questions with detailed explanations

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Exam Details

Duration90 min
Passing Score71%
LevelSpecialist
TestingPearson VUE
Valid ForDoes not expire
Exam Cost$200

What topics are on the C1000-059 exam?

1

Scientific, Mathematical, and Technical Essentials for Data Science and AI 15%

1.1
Analytics Types
1 subtopics
1.1.1Analytics Classifications
Learning Objectives
  • Explain the difference between Descriptive, Prescriptive, Predictive, Diagnostic, and Cognitive Analytics
1.2
AI and Data Science Terminology
2 subtopics
1.2.1Key Terms and Concepts
1.2.2Data Science Streams
Learning Objectives
  • Describe and explain the key terms in the field of artificial intelligence
  • Distinguish different streams of work within Data Science and AI
1.3
Machine Learning Fundamentals
2 subtopics
1.3.1ML Pipeline Stages
1.3.2Design Thinking
Learning Objectives
  • Describe the key stages of a machine learning pipeline
  • Explain the fundamental terms and concepts of design thinking
1.4
Tools and Technologies
1 subtopics
1.4.1Open Source and IBM Tools
Learning Objectives
  • Distinguish and leverage key Open Source and IBM tools and technologies that can be used by a Data Scientist to implement AI solutions
1.5
Mathematical Foundations
2 subtopics
1.5.1Probability Distributions
1.5.2Matrix Operations
Learning Objectives
  • Explain the general properties of common probability distributions
  • Explain and calculate different types of matrix operations
Domain Hands-on Skills
Statistical analysisMathematical computationTool selection
Common Mistakes to Avoid
  • Confusing ML with AI
  • Misunderstanding analytics types
  • Incorrect matrix calculations
2

Applications of Data Science and AI in Business 12%

3

Data Understanding Techniques in Data Science and AI 13%

4

Data Preparation Techniques in Data Science and AI 15%

5

Application of Data Science and AI Techniques and Models 15%

6

Evaluation of AI Models 12%

7

Deployment of AI Models 10%

8

Technology Stack for Data Science and AI 18%

How do I earn the Ibm AI Enterprise Workflow V1 Data Science Specialist certification?

Official Pathway Guidance

Track: Data and AI

Prerequisites

  • NoneNo formal prerequisitesOptional

Next Steps

  • C1000-169IBM Cloud Pak for Data v4.6 Administrator
  • C1000-112Fundamentals of Quantum Computation Using Qiskit v0.2X Developer

Alternative Paths

Certification Maintenance

  • Recertification Options:
    No recertification required

How do I study for the C1000-059 Exam?

Practice the Ibm AI Enterprise Workflow V1 Data Science Specialist with our Exam Simulator

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Official Resources

AI Enterprise Workflow: End-to-End Creation and Deployment of Machine Learning Modelstraining_courseIBM AI Enterprise Specialist Learning Pathlearning_pathIBM Certified Specialist - AI Enterprise Workflow V1certification_page

Learn more about this exam

EDUSUM Sample Questionssample_questions

Study Tips

  • Focus on hands-on practice with Python and machine learning libraries
  • Understand the complete ML pipeline from data collection to deployment
  • Master evaluation metrics for both classification and regression
  • Practice with IBM Watson Studio and Cloud Pak for Data
  • Review dimensionality reduction techniques (PCA, t-SNE, autoencoders)
  • Study the differences between various analytics types (descriptive, predictive, prescriptive, etc.)
  • Understand IBM's AI services and their use cases

What's changed on this exam?

Status: ACTIVE

Technology Coverage

Watson StudioLatest

Enhanced AutoAI features may appear in future exam updates

Released: 2024-10-01
Python LibrariesVarious

Core libraries remain stable but new features continuously added

Released: Ongoing

Industry Trends

Who should take this exam?

Recommended Experience

  • 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

Experience Level: Intermediate

How do I register & what's the exam fee?

Exam Cost$200 USD
Testing CentersPearson VUE
Online ProctoringAvailable

How long is the certification valid?

Valid ForDoes not expire
Recertification
  • No recertification required - credential does not expire

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