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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Watson Studio and Watson Knowledge Catalog | 20-25% | - AutoAI and automatic model building - Data asset management - Data governance and cataloging - Project management and collaboration |
| Topic 2: Machine Learning and Model Development | 20-25% | - Feature engineering and selection - Model deployment and monitoring - Supervised and unsupervised learning concepts - Model training, evaluation, and optimization |
| Topic 3: Data Visualization and Storytelling | 15-20% | - Communicating findings to stakeholders - Interactive dashboards and reports - Visualization best practices |
| Topic 4: Data Science and Watson Fundamentals | 20-25% | - IBM Watson ecosystem and components - Data collection, preparation, and exploration - Data science methodology and CRISP-DM framework |
| Topic 5: Watson AI Services and Deployment | 10-15% | - Watson Discovery overview - Deploying models as REST APIs - Watson Assistant integration - Monitoring deployed models |
IBM Watson Data Scientist v1 Sample Questions:
1. In the context of deployment environments, understanding resources is crucial.
What does this typically involve?
A) Focusing exclusively on the cost of storage
B) Choosing the most aesthetically pleasing user interface
C) Determining the computational power and memory requirements for the deployed solution
D) Selecting the programming language with the least number of keywords
2. How does the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology begin its process model?
A) Evaluation
B) Model Building
C) Business Understanding
D) Data Preparation
3. Which metric is commonly used to evaluate the performance of a regression model?
A) Precision
B) Mean Squared Error (MSE)
C) Accuracy
D) Recall
4. Which method is NOT traditionally used for collecting data?
A) Scraping data from a webpage
B) Using Python APIs for external data
C) Using SQL to fetch data from a data warehouse
D) Predicting future trends with machine learning models
5. Choosing the best model often involves trade-offs.
Which scenario represents such a trade-off?
A) Preferring a model with higher accuracy over one that is slightly less accurate but much more interpretable
B) Opting for the most complex model to ensure ease of use
C) Selecting a model based solely on its execution speed, without regard to accuracy
D) Choosing the model with the largest number of features, regardless of performance
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: C | Question # 3 Answer: B | Question # 4 Answer: D | Question # 5 Answer: A |
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