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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 2: Watson AI Services and Deployment | 10-15% | - Deploying models as REST APIs - Monitoring deployed models - Watson Assistant integration - Watson Discovery overview |
| Topic 3: 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 4: Watson Studio and Watson Knowledge Catalog | 20-25% | - Data governance and cataloging - AutoAI and automatic model building - Data asset management - Project management and collaboration |
| Topic 5: Data Visualization and Storytelling | 15-20% | - Interactive dashboards and reports - Visualization best practices - Communicating findings to stakeholders |
IBM Watson Data Scientist v1 Sample Questions:
1. Which metric would be most appropriate for evaluating a model in a highly imbalanced classification problem?
A) Accuracy
B) Recall
C) Precision
D) F1-score
2. Which statement describes bagging?
A) Building models and using their output as features into a final model.
B) Building models sequentially and evaluating the success of earlier models. It combines a set of weak learners into a strong learner.
C) Building models with artificial neural networks based on the sharedweight architecture of the convolution kernels or filters.
D) Building models in parallel and aggregating their predictions to select the final prediction.
3. In the context of IBM Garage Methodology, which of the following best describes the "Enterprise Design Thinking" stage?
A) It focuses on maintaining and operating solutions at scale.
B) It involves the rapid building of prototypes to validate ideas.
C) It emphasizes understanding user outcomes and business needs.
D) It is primarily concerned with the technical deployment of solutions.
4. The ROC curve is a graphical representation that shows the performance of a classification model at all classification thresholds.
What does ROC stand for?
A) Regression Operation Characteristic
B) Random Output Curve
C) Recall Operation Curve
D) Receiver Operating Characteristic
5. Which of the following is NOT a direct benefit of connecting to data sources using Cloud Pak for Data?
A) Streamlining the integration of diverse data sources
B) Facilitating secure and scalable data connectivity
C) Automatically generating insights without data analysis
D) Enhancing collaboration across data science and IT teams
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: D | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: C |
