[2025] C1000-154 Exam Dumps, Test Engine Practice Test Questions
Pass C1000-154 exam [Jan 03, 2025] Updated 82 Questions
NEW QUESTION # 23
Which of the following best describes when to use deep learning over traditional machine learning algorithms?
- A. When working with high-dimensional data, such as images or natural language, where feature extraction is complex.
- B. When computational resources are limited and model interpretability is not a concern.
- C. For simple tasks that require straightforward predictive modeling.
- D. When the dataset is small and easily interpretable.
Answer: A
NEW QUESTION # 24
What is the primary purpose of hyperparameter tuning in machine learning models?
- A. To reduce the training time of the model to an absolute minimum
- B. To increase the number of features in the dataset automatically
- C. To adjust the model's complexity to improve its performance on unseen data
- D. To ensure the model uses all available computational resources
Answer: C
NEW QUESTION # 25
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 consistent random seed when splitting the data
- C. By using a different random seed each time the data is split
- D. By partitioning the data manually
Answer: B
NEW QUESTION # 26
When anticipating additional data sources that might be relevant, what is a crucial factor to consider?
- A. The graphical interface of the data source
- B. The data source's popularity on social media
- C. The color scheme of the data visualization
- D. The relevance of the data source to the business problem
Answer: D
NEW QUESTION # 27
When selecting a small number of algorithms based on model requirements, what factor should you primarily consider?
- A. The popularity of the algorithm in recent academic papers.
- B. The algorithm that requires the least amount of data preprocessing.
- C. Choosing algorithms that are only based on supervised learning.
- D. Compatibility of the algorithm with the data characteristics and the predictive task.
Answer: D
NEW QUESTION # 28
Which of the following is true about the AUC measure in the context of classification models?
- A. It measures the model's accuracy using a single threshold.
- B. It is less useful when the classes are highly imbalanced.
- C. It indicates the number of false positives.
- D. It represents the degree of separability between classes.
Answer: D
NEW QUESTION # 29
Assessing the feasibility of a solution(s) often requires evaluating:
- A. Technical feasibility, cost, and time constraints
- B. Market competition only
- C. Preferred communication channels of the project manager
- D. The color scheme of the user interface
Answer: A
NEW QUESTION # 30
Profiling and visualizing data using Watson tools primarily helps in:
- A. Creating aesthetically pleasing presentations without regard to data relevance
- B. Increasing the quantity of data for analysis
- C. Identifying patterns, outliers, and insights in the data
- D. Simplifying the data collection process without analyzing quality
Answer: C
NEW QUESTION # 31
Selecting the right model for a data science project depends on:
- A. The size of the dataset only
- B. The type of data and the problem to be solved
- C. The project's budget only
- D. The preference of the data scientist
Answer: B
NEW QUESTION # 32
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. Choosing the visualization tools
- D. Deploying the model
Answer: A
NEW QUESTION # 33
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 allows for the model to be validated and tested on different subsets of data to check its generalization ability
- C. It increases the computational complexity without improving model performance
- D. It guarantees that the model will perform with 100% accuracy on unseen data
Answer: B
NEW QUESTION # 34
Which metric is commonly used to evaluate the performance of a regression model?
- A. Accuracy
- B. Precision
- C. Mean Squared Error (MSE)
- D. Recall
Answer: C
NEW QUESTION # 35
Which of the following is true regarding cross-validation?
- A. It decreases the variability of the model performance estimation.
- B. It helps in identifying the model's performance variability across different data splits.
- C. It involves training the model on the entire dataset at once.
- D. It should be avoided as it leads to overfitting.
Answer: A,B
NEW QUESTION # 36
What is a key advantage of using supervised learning techniques over unsupervised learning techniques?
- A. Supervised learning algorithms can automatically label data.
- B. Supervised learning is typically used for prediction with known outcomes, providing clear metrics for model performance.
- C. Supervised learning is more effective for discovering hidden patterns in data without prior labeling.
- D. Supervised learning can work without any labeled data.
Answer: B
NEW QUESTION # 37
When determining upskill requirements for a team working on a solution, it is important to consider:
- A. The existing skill level of the team and the skills required for the project
- B. The budget allocation for the project
- C. The preferred programming languages of the team
- D. The geographical location of the team members
Answer: A
NEW QUESTION # 38
In the deployment phase, why is it important to know the different data sources available in Cloud Pak for Data?
- A. Because only one type of data source can be used in any deployment
- B. To effectively integrate and manage data from various sources for analysis and model training
- C. To ensure that all data sources are manually processed
- D. To limit the deployment to only use local file storage
Answer: B
NEW QUESTION # 39
What is the primary purpose of partitioning data into training and test sets?
- A. To ensure that the model gets exposed to all possible data scenarios during training
- B. To increase the computational efficiency of model training
- C. To evaluate the model's performance on unseen data
- D. To maximize the accuracy of the model by using all data for training
Answer: C
NEW QUESTION # 40
When comparing models to choose the best one, which factor is least likely to be considered?
- A. The complexity of the model
- B. The color scheme of the model's output visualizations
- C. The performance of the model on validation data
- D. The explainability of the model's predictions
Answer: B
NEW QUESTION # 41
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