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IBM C1000-154 Exam Syllabus Topics:

IBM Watson Data Scientist v1 Sample Questions:

1. 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) Continuous monitoring and evaluation of model performance
D) Using the highest number of resources for every model

2. Why is it important to create data splits that are reproducible?

A) To use more data for testing than for training
B) To allow for larger test sets for more comprehensive testing
C) To guarantee that the model will perform with 100% accuracy on unseen data
D) To ensure that each model run can be exactly replicated for verification and comparison

3. When selecting a small number of algorithms based on model requirements, what factor should you primarily consider?

A) Compatibility of the algorithm with the data characteristics and the predictive task.
B) The popularity of the algorithm in recent academic papers.
C) Choosing algorithms that are only based on supervised learning.
D) The algorithm that requires the least amount of data preprocessing.

4. Which method is NOT traditionally used for collecting data?

A) Using SQL to fetch data from a data warehouse
B) Predicting future trends with machine learning models
C) Using Python APIs for external data
D) Scraping data from a webpage

5. Which of the following best describes when to use deep learning over traditional machine learning algorithms?

A) When the dataset is small and easily interpretable.
B) When working with high-dimensional data, such as images or natural language, where feature extraction is complex.
C) For simple tasks that require straightforward predictive modeling.
D) When computational resources are limited and model interpretability is not a concern.

Solutions: