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IBM C1000-154 Exam Syllabus Topics:

IBM Watson Data Scientist v1 Sample Questions:

1. Which statement describes bagging?

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

2. Which feature is NOT available when managing models with Watson Machine Learning?

A) Real-time performance monitoring
B) Version control of deployed models
C) Rollback capabilities for model versions
D) Automatic conversion of all models to deep learning models

3. What is a key disadvantage of using Grid Search for hyperparameter tuning?

A) It can be computationally expensive and time-consuming due to its exhaustive nature
B) It is too quick and may miss out on evaluating some hyperparameters
C) It is unable to handle discrete parameters
D) It requires no prior knowledge of the hyperparameters

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

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

5. Which Python library is commonly used for data manipulation and analysis, and is available in Cloud Pak for Data?

A) PyTorch
B) Keras
C) Pandas
D) TensorFlow

Solutions: