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IBM C1000-154 Exam Syllabus Topics:

IBM Watson Data Scientist v1 Sample Questions:

1. What is a benefit of creating data pipelines to automate the model lifecycle?

A) It provides a structured approach to processing, validating, and deploying models
B) Encourages a one-size-fits-all approach to model development
C) It necessitates frequent manual updates and checks
D) Reduces the need for understanding the underlying data

2. Which statistical method reduces the number of attributes by lumping highly correlated attributes together?

A) Binning
B) Principal Component Analysis (PCA)
C) Long Short Term Memory Network (LSTM)
D) Synthetic Minority Over-sampling Technique (SMOTE)

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

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

4. When using pandas in a Jupyter notebook for exploratory data analysis, what is a common practice?

A) Utilizing pandas to clean and transform data
B) Avoiding the use of visualizations to understand data
C) Ignoring missing values in the dataset
D) Only analyzing datasets with less than 100 rows

5. Which statement describes bagging?

A) Building models in parallel and aggregating their predictions to select the final prediction.
B) Building models and using their output as features into a final model.
C) Building models sequentially and evaluating the success of earlier models. It combines a set of weak learners into a strong learner.
D) Building models with artificial neural networks based on the sharedweight architecture of the convolution kernels or filters.

Solutions: