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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Watson Studio and Watson Knowledge Catalog | 20-25% | - Data governance and cataloging - AutoAI and automatic model building - Project management and collaboration - Data asset management |
| Topic 2: Machine Learning and Model Development | 20-25% | - Supervised and unsupervised learning concepts - Model training, evaluation, and optimization - Feature engineering and selection - Model deployment and monitoring |
| Topic 3: Data Science and Watson Fundamentals | 20-25% | - Data science methodology and CRISP-DM framework - IBM Watson ecosystem and components - Data collection, preparation, and exploration |
| Topic 4: Data Visualization and Storytelling | 15-20% | - Visualization best practices - Interactive dashboards and reports - Communicating findings to stakeholders |
| Topic 5: Watson AI Services and Deployment | 10-15% | - Deploying models as REST APIs - Monitoring deployed models - Watson Assistant integration - Watson Discovery overview |
IBM Watson Data Scientist v1 Sample Questions:
Which of the following is NOT a direct benefit of connecting to data sources using Cloud Pak for Data?
- A. Enhancing collaboration across data science and IT teams
- B. Streamlining the integration of diverse data sources
- C. Automatically generating insights without data analysis
- D. Facilitating secure and scalable data connectivity
Correct Answer: C 🗳️
Which search algorithm is known for its exhaustive search over a specified parameter space for hyperparameter tuning?
- A. Grid Search
- B. Sequential Search
- C. Binary Search
- D. Random Search
Correct Answer: A 🗳️
In classification models, which of the following metrics is NOT directly derived from the confusion matrix?
- A. Mean Absolute Error (MAE)
- B. Precision
- C. F1-score
- D. Recall
Correct Answer: A 🗳️
An essential aspect of the ETL (Extract, Transform, Load) process is:
- A. Loading data into a single, centralized database for analysis
- B. Extracting the least amount of data for simplicity
- C. Ensuring data quality and consistency throughout the process
- D. Transforming data exclusively in cloud environments
Correct Answer: C 🗳️
Which of the following is a critical first step in understanding a business problem for data science projects?
- A. Choosing the visualization tools
- B. Deploying the model
- C. Defining the project scope
- D. Selecting the machine learning algorithm
Correct Answer: C 🗳️
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