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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Watson Studio and Watson Knowledge Catalog | 20-25% | - AutoAI and automatic model building - Data asset management - Project management and collaboration - Data governance and cataloging |
| Data Science and Watson Fundamentals | 20-25% | - Data collection, preparation, and exploration - Data science methodology and CRISP-DM framework - IBM Watson ecosystem and components |
| Data Visualization and Storytelling | 15-20% | - Visualization best practices - Interactive dashboards and reports - Communicating findings to stakeholders |
| Watson AI Services and Deployment | 10-15% | - Deploying models as REST APIs - Monitoring deployed models - Watson Discovery overview - Watson Assistant integration |
| 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 |
IBM Watson Data Scientist v1 Sample Questions:
When deploying models in Watson Machine Learning, what is essential for ensuring the models perform as expected in production?
- A. Deployment without any security measures
- B. Limiting access to the model to a few select users
- C. Using the highest number of resources for every model
- D. Continuous monitoring and evaluation of model performance
Correct Answer: D 🗳️
In the context of avoiding underfitting and overfitting, what role does splitting the data into training, testing, and validation sets play?
- A. It allows for the model to be validated and tested on different subsets of data to check its generalization ability
- B. It ensures that the model is trained on the maximum amount of data possible
- C. It increases the computational complexity without improving model performance
- D. It guarantees that the model will perform with 100% accuracy on unseen data
Correct Answer: A 🗳️
Given the Confusion matrix below, which is the formula for specificity?
- A. TP/(FP + TP)
- B. TP/(FN + TP)
- C. (TP + TN)/(FN + FP + TN + TP)
- D. TN/(TN + FP)
Correct Answer: D 🗳️
In the case of imbalanced data, what technique is recommended to ensure that the train and test sets have similar distributions of the target variable?
- A. Stratified split
- B. Random split without considering the target variable
- C. Splitting based on the order of data collection
- D. Using only the majority class for splitting
Correct Answer: A 🗳️
An essential aspect of the ETL (Extract, Transform, Load) process is:
- A. Transforming data exclusively in cloud environments
- B. Ensuring data quality and consistency throughout the process
- C. Loading data into a single, centralized database for analysis
- D. Extracting the least amount of data for simplicity
Correct Answer: B 🗳️
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