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Listening Quiz: Phỏng Vấn Kỹ Thuật Cho Vị Trí Nhà Khoa Học Dữ Liệu Cấp Cao – Medium Level
Th11 17, 2025
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bởi
Anna Phạm
trong
Tiếng Anh dành cho Nhà khoa học dữ liệu (English for Data Scientist)
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1.
Which machine learning models has Linda worked with?
Time series and reinforcement learning models
Regression, classification, and clustering models
Only random forests and gradient boosting models
Decision trees, neural networks, and support vector machines
Explanation:
Linda says she has worked with regression, classification, and clustering models.
2.
Why does Linda prefer random forests?
They handle big data well
They are easier to explain than all other models
They always require less preprocessing
They are best for missing data
Explanation:
Linda states that she prefers random forests because they handle big data well.
3.
How does Linda deal with missing data in a dataset?
She replaces missing data only with random values
She always deletes the entire dataset
She ignores missing data and continues
She uses imputation methods or removes rows if necessary
Explanation:
Linda explains that she uses imputation methods or removes rows if necessary.
4.
What did Linda do to improve a model’s accuracy?
She reduced the number of features and lowered the learning rate
She changed the model to a clustering algorithm
She tuned hyperparameters and added new features to the data
She trained the model without any validation
Explanation:
Linda says she improved accuracy by tuning hyperparameters and adding new features.
5.
How does Linda stay updated with new data science techniques?
She asks Daniel for updates every week
She follows social media posts but never studies papers
She only relies on workplace training
She reads research papers and attends online courses regularly
Explanation:
Linda says she stays updated by reading research papers and attending online courses regularly.
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