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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Machine Learning and Model Development | 20-25% | - Model training, evaluation, and optimization - Supervised and unsupervised learning concepts - Model deployment and monitoring - Feature engineering and selection |
| Topic 2: 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 3: Watson AI Services and Deployment | 10-15% | - Watson Assistant integration - Monitoring deployed models - Deploying models as REST APIs - Watson Discovery overview |
| Topic 4: Data Visualization and Storytelling | 15-20% | - Interactive dashboards and reports - Visualization best practices - Communicating findings to stakeholders |
| Topic 5: Data Science and Watson Fundamentals | 20-25% | - Data science methodology and CRISP-DM framework - Data collection, preparation, and exploration - IBM Watson ecosystem and components |
IBM Watson Data Scientist v1 Sample Questions:
Question 1
What is a critical consideration when selecting the right model class for a given problem?
A. The nature of the problem (e.g., classification, regression) and the characteristics of the data.
B. The model's ability to produce results quickly, regardless of accuracy.
C. The availability of high-performance computing resources.
D. The theoretical complexity of the model, with more complex models always being preferred.
Question 2
What is data leakage in the context of model training?
A. Loss of data during the splitting process
B. A situation where the test data is not available
C. Leakage of sensitive information due to poor data handling practices
D. When data from outside the training dataset is accidentally included in the training process
Question 3
Cloud Pak for Data's integration with Spark allows users to:
A. Perform complex computations on small datasets only
B. Use Spark exclusively for data visualization purposes
C. Avoid using any form of data processing or analysis
D. Leverage distributed computing for processing large datasets efficiently
Question 4
Given the Confusion matrix below, which is the formula for specificity?
A. (TP + TN)/(FN + FP + TN + TP)
B. TN/(TN + FP)
C. TP/(FP + TP)
D. TP/(FN + TP)
Question 5
In the deployment phase, why is it important to know the different data sources available in Cloud Pak for Data?
A. To effectively integrate and manage data from various sources for analysis and model training
B. To ensure that all data sources are manually processed
C. To limit the deployment to only use local file storage
D. Because only one type of data source can be used in any deployment
Solutions:
| Question 1 Answer: A | Question 2 Answer: D | Question 3 Answer: D | Question 4 Answer: B | Question 5 Answer: A |
