Exam Name: IBM Watson Data Scientist v1
Certification Provider: IBM
Corresponding Certification: IBM Certification
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IBM C1000-154 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Data Preparation and Analysis | - Data cleaning and preprocessing - Feature engineering basics - Exploratory data analysis |
| Topic 2: Data Visualization and Communication | - Visualization techniques - Communicating insights to stakeholders |
| Topic 3: Data Science Fundamentals | - Data science lifecycle - Types of data and data sources |
| Topic 4: Machine Learning Methods | - Model evaluation and validation - Supervised learning - Unsupervised learning |
| Topic 5: IBM Watson Tools and Platform | - Model development and deployment - IBM Watson Studio usage |
IBM Watson Data Scientist v1 Sample Questions:
What does the term "complexity" in model comparison refer to?
- A. The size of the dataset the model can handle
- B. The amount of computational resources required for training and inference
- C. The number of hyperparameters that need to be tuned
- D. The aesthetic appeal of the model's graphical representations
Correct Answer: B 🗳️
A virtual assistant has been developed and deployed based on the Watson Assistant service. The assistant will support customers by answering FAQs (Frequent Answered Questions).
Which metric is a good indicator of the performance of the virtual assistant?
- A. The Root Mean Squared Error (RMSE) of words
- B. The Area Under the Curve (AUC)
- C. Measure escalated calls using A/B testing
- D. The F1 score of predicted intents in the Analytics tab
Correct Answer: C 🗳️
Why is it important to create data splits that are reproducible?
- A. To ensure that each model run can be exactly replicated for verification and comparison
- B. To guarantee that the model will perform with 100% accuracy on unseen data
- C. To allow for larger test sets for more comprehensive testing
- D. To use more data for testing than for training
Correct Answer: A 🗳️
A model's performance is not solely dependent on its accuracy but also on:
- A. The number of features selected
- B. The color of the visualization charts
- C. Metrics like precision, recall, and F1 score
- D. The choice of programming language
Correct Answer: C 🗳️
In IBM Garage Methodology, the 'Minimum Viable Product' (MVP) concept is crucial for:
- A. Waiting for all possible features to be developed before release
- B. Extending the timeline of the project indefinitely
- C. Maximizing the budget before the product launch
- D. Testing hypotheses with the smallest investment of time and resources
Correct Answer: D 🗳️
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