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IBM Watson Data Scientist v1 Sample Questions (Q51-Q56):
NEW QUESTION # 51
When managing data with Cloud Pak for Data Services, what is a common task?
- A. Avoiding the automation of data processing tasks
- B. Ignoring data security and compliance requirements
- C. Using the same data management approach for all types of data, regardless of size or format
- D. Reading data from and writing data to Watson Studio
Answer: D
NEW QUESTION # 52
When would you use AutoAI to select algorithms for your model?
- A. When you have a deep understanding of all available algorithms and want to manually tune hyperparameters.
- B. When the model requirements are extremely specific and no standard algorithm fits.
- C. Only when working with small datasets due to processing limitations.
- D. When you want to automatically explore multiple algorithms and hyperparameters to find the best model.
Answer: D
NEW QUESTION # 53
Which statement describes bagging?
- A. Building models with artificial neural networks based on the sharedweight architecture of the convolution kernels or filters.
- B. Building models sequentially and evaluating the success of earlier models. It combines a set of weak learners into a strong learner.
- C. Building models and using their output as features into a final model.
- D. Building models in parallel and aggregating their predictions to select the final prediction.
Answer: D
NEW QUESTION # 54
An essential aspect of the ETL (Extract, Transform, Load) process is:
- A. Extracting the least amount of data for simplicity
- B. Transforming data exclusively in cloud environments
- C. Loading data into a single, centralized database for analysis
- D. Ensuring data quality and consistency throughout the process
Answer: D
NEW QUESTION # 55
What is the primary purpose of hyperparameter tuning in machine learning models?
- A. To ensure the model uses all available computational resources
- B. To reduce the training time of the model to an absolute minimum
- C. To increase the number of features in the dataset automatically
- D. To adjust the model's complexity to improve its performance on unseen data
Answer: D
NEW QUESTION # 56
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