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IBM C1000-154 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: IBM Watson Tools and Platform | - Model development and deployment - IBM Watson Studio usage |
| Topic 2: Data Visualization and Communication | - Communicating insights to stakeholders - Visualization techniques |
| Topic 3: Data Preparation and Analysis | - Data cleaning and preprocessing - Exploratory data analysis - Feature engineering basics |
| Topic 4: Data Science Fundamentals | - Types of data and data sources - Data science lifecycle |
| Topic 5: Machine Learning Methods | - Model evaluation and validation - Unsupervised learning - Supervised learning |
IBM Watson Data Scientist v1 Sample Questions:
In classification models, which of the following metrics is NOT directly derived from the confusion matrix?
- A. F1-score
- B. Mean Absolute Error (MAE)
- C. Precision
- D. Recall
Correct Answer: B 🗳️
Which metric would be most appropriate for evaluating a model in a highly imbalanced classification problem?
- A. F1-score
- B. Precision
- C. Accuracy
- D. Recall
Correct Answer: A 🗳️
Which statement describes bagging?
- A. Building models sequentially and evaluating the success of earlier models. It combines a set of weak learners into a strong learner.
- 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 in parallel and aggregating their predictions to select the final prediction.
Correct Answer: D 🗳️
In the context of assessing data quality in Watson Knowledge Catalog (WKC) and Cloud Pak for Data (CPD), what is a primary focus?
- A. Analyzing completeness, consistency, and accuracy of data
- B. Focusing on the data's color scheme
- C. Enhancing the graphical user interface
- D. Increasing the volume of data collected
Correct Answer: A 🗳️
An essential aspect of the ETL (Extract, Transform, Load) process is:
- A. Extracting the least amount of data for simplicity
- B. Transforming data exclusively in cloud environments
- C. Loading data into a single, centralized database for analysis
- D. Ensuring data quality and consistency throughout the process
Correct Answer: D 🗳️
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