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IBM C1000-154 Exam Syllabus Topics:

IBM Watson Data Scientist v1 Sample Questions:

1. Which type of machine learning algorithm would be most appropriate for predicting house prices based on various features like location, size, and number of bedrooms?

A) Clustering
B) Dimensionality Reduction
C) Classification
D) Regression

2. In IBM Garage Methodology, the 'Minimum Viable Product' (MVP) concept is crucial for:

A) Extending the timeline of the project indefinitely
B) Testing hypotheses with the smallest investment of time and resources
C) Waiting for all possible features to be developed before release
D) Maximizing the budget before the product launch

3. Which metric would be most appropriate for evaluating a model in a highly imbalanced classification problem?

A) Accuracy
B) F1-score
C) Recall
D) Precision

4. What is the key difference between batch processing and streaming in data processing?

A) Batch processing processes data in large blocks at a time, whereas streaming processes data in real- time as it arrives
B) Batch processing involves real-time data processing, whereas streaming does not process data
C) Streaming is suitable for large, historical datasets, whereas batch processing is for real-time data analysis
D) Batch processing processes data in large blocks at a time, whereas streaming processes data in real- time as it arrives

5. Understanding how to use libraries in Python within a deployment environment is essential for:

A) Increasing the complexity and maintenance cost of the deployed solution
B) Deploying models that are incompatible with the deployment environment
C) Ensuring that all models are developed without any external libraries
D) Leveraging specific functionalities for data analysis, manipulation, and model building

Solutions: