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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Visualization and Storytelling | 15-20% | - Communicating findings to stakeholders - Interactive dashboards and reports - Visualization best practices |
| Data Science and Watson Fundamentals | 20-25% | - Data science methodology and CRISP-DM framework - Data collection, preparation, and exploration - IBM Watson ecosystem and components |
| Machine Learning and Model Development | 20-25% | - Model training, evaluation, and optimization - Feature engineering and selection - Model deployment and monitoring - Supervised and unsupervised learning concepts |
| Watson AI Services and Deployment | 10-15% | - Watson Assistant integration - Deploying models as REST APIs - Monitoring deployed models - Watson Discovery overview |
| Watson Studio and Watson Knowledge Catalog | 20-25% | - Data asset management - Data governance and cataloging - AutoAI and automatic model building - Project management and collaboration |
IBM Watson Data Scientist v1 Sample Questions:
Which of the following best describes when to use deep learning over traditional machine learning algorithms?
- A. When working with high-dimensional data, such as images or natural language, where feature extraction is complex.
- B. When the dataset is small and easily interpretable.
- C. For simple tasks that require straightforward predictive modeling.
- D. When computational resources are limited and model interpretability is not a concern.
Correct Answer: A 🗳️
Which hyperparameter is NOT commonly adjusted in a deep learning model?
- A. The color of the model's output
- B. Activation function
- C. Learning rate
- D. Number of layers
Correct Answer: A 🗳️
Which statement describes bagging?
- A. Building models in parallel and aggregating their predictions to select the final prediction.
- B. Building models with artificial neural networks based on the sharedweight architecture of the convolution kernels or filters.
- C. Building models and using their output as features into a final model.
- D. Building models sequentially and evaluating the success of earlier models. It combines a set of weak learners into a strong learner.
Correct Answer: A 🗳️
What is a key advantage of using supervised learning techniques over unsupervised learning techniques?
- A. Supervised learning is typically used for prediction with known outcomes, providing clear metrics for model performance.
- B. Supervised learning can work without any labeled data.
- C. Supervised learning algorithms can automatically label data.
- D. Supervised learning is more effective for discovering hidden patterns in data without prior labeling.
Correct Answer: A 🗳️
When managing data with Cloud Pak for Data Services, what is a common task?
- A. Avoiding the automation of data processing tasks
- B. Reading data from and writing data to Watson Studio
- C. Using the same data management approach for all types of data, regardless of size or format
- D. Ignoring data security and compliance requirements
Correct Answer: B 🗳️
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