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IBM C1000-154 is a certification exam designed for professionals who want to validate their skills as a data scientist using IBM Watson technologies. C1000-154 exam is intended for individuals who have a solid understanding of data science concepts and have experience with IBM Watson Studio and IBM Watson Machine Learning. Successful completion of the IBM C1000-154 exam demonstrates that an individual has the skills and knowledge needed to use IBM Watson to solve complex data science problems.



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IBM C1000-154 exam measures the candidate's ability to create and manage data assets, prepare data for modeling, and build predictive models. Candidates are also tested on their ability to use Watson Studio to develop and train machine learning models, evaluate model performance, and deploy models to production. By passing this certification exam, candidates demonstrate their proficiency in working with IBM Watson Data Scientist v1, which is an essential skill set for data scientists and machine learning engineers.

IBM Watson Data Scientist v1 Sample Questions (Q68-Q73):

NEW QUESTION # 68
The process of aligning on user intents for a solution involves:

* A. Identifying and understanding the needs and goals of end-users
* B. Understanding the business model in depth
* C. Focusing on the data management strategies
* D. Determining the technical feasibility exclusively
Answer: A

NEW QUESTION # 69
When managing data with Cloud Pak for Data Services, what is a common task?

* A. Reading data from and writing data to Watson Studio
* 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. Avoiding the automation of data processing tasks
Answer: A

NEW QUESTION # 70
What is data leakage in the context of model training?

* A. Leakage of sensitive information due to poor data handling practices
* B. Loss of data during the splitting process
* C. A situation where the test data is not available
* D. When data from outside the training dataset is accidentally included in the training process
Answer: D

NEW QUESTION # 71
In the case of imbalanced data, what technique is recommended to ensure that the train and test sets have similar distributions of the target variable?

* A. Stratified split
* B. Using only the majority class for splitting
* C. Random split without considering the target variable
* D. Splitting based on the order of data collection
Answer: A

NEW QUESTION # 72
Understanding how to use libraries in Python within a deployment environment is essential for:

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

NEW QUESTION # 73
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