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IBM C1000-154 Exam Syllabus Topics:

IBM Watson Data Scientist v1 Sample Questions:

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

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

2. What is a key advantage of using supervised learning techniques over unsupervised learning techniques?

A) Supervised learning is more effective for discovering hidden patterns in data without prior labeling.
B) Supervised learning can work without any labeled data.
C) Supervised learning algorithms can automatically label data.
D) Supervised learning is typically used for prediction with known outcomes, providing clear metrics for model performance.

3. Which statement best differentiates machine learning from deep learning?

A) Machine learning algorithms perform better on structured data, while deep learning excels with unstructured data like images and text.
B) Deep learning algorithms require less data to learn.
C) Machine learning models are always transparent, whereas deep learning models cannot be interpreted.
D) Deep learning algorithms are a subset of machine learning algorithms that do not require feature engineering.

4. 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) Random split without considering the target variable
B) Splitting based on the order of data collection
C) Using only the majority class for splitting
D) Stratified split

5. Which of the following is a critical first step in understanding a business problem for data science projects?

A) Defining the project scope
B) Deploying the model
C) Choosing the visualization tools
D) Selecting the machine learning algorithm

Solutions: