IBM C1000-154 問題集

質問 1：
A model's performance is not solely dependent on its accuracy but also on:
A. The number of features selected
B. The choice of programming language
C. Metrics like precision, recall, and F1 score
D. The color of the visualization charts
正解：C

質問 2：
In model lifecycle management, what is a key consideration when deploying models with Watson Machine Learning?
A. Ensuring there is no logging or monitoring of model performance
B. Avoiding the use of APIs for integration with applications
C. The ability to update or retire models based on performance metrics
D. Deploying all models simultaneously regardless of use case
正解：C

質問 3：
In the context of avoiding underfitting and overfitting, what role does splitting the data into training, testing, and validation sets play?
A. It ensures that the model is trained on the maximum amount of data possible
B. It guarantees that the model will perform with 100% accuracy on unseen data
C. It allows for the model to be validated and tested on different subsets of data to check its generalization ability
D. It increases the computational complexity without improving model performance
正解：C

質問 4：
Which feature is NOT available when managing models with Watson Machine Learning?
A. Version control of deployed models
B. Rollback capabilities for model versions
C. Automatic conversion of all models to deep learning models
D. Real-time performance monitoring
正解：C

質問 5：
When would you use AutoAI to select algorithms for your model?
A. When you want to automatically explore multiple algorithms and hyperparameters to find the best model.
B. Only when working with small datasets due to processing limitations.
C. When you have a deep understanding of all available algorithms and want to manually tune hyperparameters.
D. When the model requirements are extremely specific and no standard algorithm fits.
正解：A

質問 6：
An essential aspect of the ETL (Extract, Transform, Load) process is:
A. Ensuring data quality and consistency throughout the process
B. Transforming data exclusively in cloud environments
C. Loading data into a single, centralized database for analysis
D. Extracting the least amount of data for simplicity
正解：A

質問 7：
Automating data processing and model deployment with jobs in Watson Studio helps to:
A. Reduce the scalability of deployed solutions
B. Limit the ability to update models based on new data
C. Increase the need for manual intervention in the model lifecycle
D. Enhance the reproducibility and efficiency of model deployments
正解：D

質問 8：
Which statement best differentiates machine learning from deep learning?
A. Machine learning models are always transparent, whereas deep learning models cannot be interpreted.
B. Deep learning algorithms are a subset of machine learning algorithms that do not require feature engineering.
C. Deep learning algorithms require less data to learn.
D. Machine learning algorithms perform better on structured data, while deep learning excels with unstructured data like images and text.
正解：D