IBM Watson Data Scientist v1 認定 C1000-154 試験問題:

1. The first step in performing exploratory data analysis (EDA) typically involves:

A) Choosing a color palette for data visualization
B) Determining the hypothesis for the analysis
C) Connecting to as many data sources as possible
D) Selecting a random sample of data to analyze

2. Which of the following is a critical first step in understanding a business problem for data science projects?

A) Defining the project scope
B) Selecting the machine learning algorithm
C) Deploying the model
D) Choosing the visualization tools

3. What does the term "complexity" in model comparison refer to?

A) The number of hyperparameters that need to be tuned
B) The aesthetic appeal of the model's graphical representations
C) The size of the dataset the model can handle
D) The amount of computational resources required for training and inference

4. Which of the following best describes when to use deep learning over traditional machine learning algorithms?

A) When computational resources are limited and model interpretability is not a concern.
B) When working with high-dimensional data, such as images or natural language, where feature extraction is complex.
C) For simple tasks that require straightforward predictive modeling.
D) When the dataset is small and easily interpretable.

5. Choosing the best model often involves trade-offs.
Which scenario represents such a trade-off?

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

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