Quiz IBM - C1000-154 - IBM Watson Data Scientist v1–The Best Training Tools

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IBM Watson Data Scientist certification exam is divided into two parts: the first part focuses on the IBM Watson Studio and the second part focuses on the IBM Watson Knowledge Catalog. C1000-154 exam consists of multiple-choice questions, and the time limit for completing the exam is 90 minutes. The passing score for the exam is 70%, and the exam can be taken in-person or online. C1000-154 Exam content is regularly updated to ensure that it reflects the latest trends and developments in the field of data science.

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IBM Watson Data Scientist v1 Sample Questions (Q38-Q43):

NEW QUESTION # 38
Which method is NOT traditionally used for collecting data?

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

Answer: A

NEW QUESTION # 39
An essential aspect of the ETL (Extract, Transform, Load) process is:

• A. Loading data into a single, centralized database for analysis
• B. Extracting the least amount of data for simplicity
• C. Ensuring data quality and consistency throughout the process
• D. Transforming data exclusively in cloud environments

Answer: C

NEW QUESTION # 40
How can data splits be made reproducible in a machine learning experiment?

• A. By splitting the data in a sequential manner without randomization
• B. By using a different random seed each time the data is split
• C. By using a consistent random seed when splitting the data
• D. By partitioning the data manually

Answer: C

NEW QUESTION # 41
Which hyperparameter is NOT commonly adjusted in a deep learning model?

• A. Number of layers
• B. Activation function
• C. Learning rate
• D. The color of the model's output

Answer: D

NEW QUESTION # 42
Choosing the best model often involves trade-offs.
Which scenario represents such a trade-off?

• A. Preferring a model with higher accuracy over one that is slightly less accurate but much more interpretable
• B. Choosing the model with the largest number of features, regardless of performance
• C. Selecting a model based solely on its execution speed, without regard to accuracy
• D. Opting for the most complex model to ensure ease of use

Answer: A

NEW QUESTION # 43
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