IBM C1000-177 Exam Details & Actual Exam Questions

  • Exam Code/Number: C1000-177
  • Exam Name/Title: Foundations of Data Science using IBM watsonx
  • Certification Provider: IBM
  • Corresponding Certification: IBM Certification
  • Certification Level: Associate

IBM Foundations of Data Science using IBM watsonx Exam Questions

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IBM C1000-177 Exam Overview:

Certification Vendor:IBM
Exam Name:Foundations of Data Science using IBM watsonx
Exam Number:C1000-177
Exam Format:Multiple choice
Passing Score:Approximately 70% (43 out of 61 correct)
Available Languages:English
Exam Duration:90 minutes
Related Certifications:IBM Certified watsonx Data Scientist - Associate
Real Exam Qty:61
Exam Price:USD 200
Exam Way:Online/onsite proctored via Pearson VUE
Official Syllabus URL:https://www.ibm.com/training/certification/ibm-certified-watsonx-data-scientist-associate-C9006400

IBM C1000-177 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Pre-Processing and Feature Engineering33%- Mitigate imbalanced data
- Normalize data
- Transform features
- Integrate data from different sources and formats
- Handle data anomalies and missing values
- Identify the best categorical data encoding techniques
- Select relevant features
Topic 2: Development Tools and Techniques13%- Assess which modeling and statistical techniques are best suited
- Select the appropriate environment and libraries
Topic 3: Evaluate the Business Problem16%- Translate business objectives into Data Science/ML/AI solutions
- Identify appropriate tools for analysis
- Formulate the hypothesis to be tested
Topic 4: Perform Exploratory Data Analysis21%- Assess data characteristics to guide future processing
- Visualize data to identify patterns/trends
- Deselect features that have minimal predictive value
- Conduct statistical analysis of data
- Visually examine the data for data understanding
Topic 5: Model Selection, Training, Evaluation, and Presentation17%- Split the data to support model evaluation
- Identify adequate machine learning model
- Choose appropriate model metrics to assess model performance


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