IBM C1000-144 IBM Machine Learning Data Scientist v1 Online Training
IBM C1000-144 Online Training
The questions for C1000-144 were last updated at Feb 20,2025.
- Exam Code: C1000-144
- Exam Name: IBM Machine Learning Data Scientist v1
- Certification Provider: IBM
- Latest update: Feb 20,2025
Which of the following is a common use case for recommendation engines?
- A . Predicting property prices
- B . Detecting fraudulent credit card transactions
- C . Suggesting products to customers based on past purchases
- D . Categorizing news articles into topics
You need to compare sales performance across different regions.
Which type of chart would most effectively serve this purpose?
- A . Histogram
- B . Box plot
- C . Bar chart
- D . Heatmap
If the goal is to explore the central tendency and variability of a dataset, which types of plots would be most informative?
- A . Bar chart and line plot
- B . Histogram and box plot
- C . Scatterplot and heatmap
- D . Pie chart and line plot
Which approach is best for refining an AI solution based on feasibility assessment?
- A . Increasing the complexity of the solution
- B . Reducing scope to match available resources and capabilities
- C . Outsourcing the entire project
- D . Ignoring feasibility concerns to speed up deployment
In SQL, how would you extract the ‘name’ and ‘age’ columns from a table named ‘customers’?
- A . SELECT name, age FROM customers;
- B . EXTRACT name, age FROM customers;
- C . GET name, age IN customers;
- D . PULL name, age OUT OF customers;
Which algorithm is most appropriate for non-linear classification problems?
- A . Linear regression
- B . Logistic regression
- C . Support Vector Machine with non-linear kernels
- D . K-means clustering
Which approach would not be suitable for assessing model fairness?
- A . Analyzing confusion matrices for different subgroups
- B . Using the same performance metric for all models
- C . Conducting audits on model decisions
- D . Implementing external fairness monitoring tools
Which techniques ensure a model can explain its decisions and predictions?
- A . Implementing deep learning models exclusively
- B . Using highly non-linear models without any simplification
- C . Integrating explanation frameworks like LIME or SHAP
- D . Minimizing the use of regularization techniques
Converting a neural network into the newest version of TensorFlow or another deep-learning package is what type of performance drift or software decay?
- A . Data changes
- B . Concept drift
- C . Software changes
- D . Sampling bias and selection bias changes
How can ensemble modeling improve machine learning performance?
- A . By simplifying the models to reduce computation time
- B . By combining multiple models to reduce variance and bias
- C . By using a single, highly accurate model
- D . By focusing exclusively on increasing model accuracy
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