A team has limited GPU resources and needs to train a model within a few hours. Which consideration is most relevant to algorithm selection?

The color of the data points in plots

Computational cost and training time

A data science team is developing a model to predict equipment failure in a manufacturing plant. They have a moderate-sized dataset with high variance, meaning different training subsets produce very different models. They need to reduce variance without losing too much interpretability. Which approach is most appropriate?

Use a single deep decision tree

Use a support vector machine with a linear kernel

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Domain 3Objective 6

Factors Involved in ML Algorithm Selection CT-AI Practice Questions (Page 3)

Part of the Domain 3: Machine Learning (ML) - Overview domain, which makes up ~15% of our current practice bank. ISTQB does not publish an official question count, but from its 60-minute exam (~25–40 total, ~4–6 in this domain), expect 1–1 from this objective — we provide 29 practice questions to prepare you well beyond it. (estimate)

29questions here

6free pages

8concepts

Questions 11–15

11foundation · easy
A very complex model performs perfectly on training data but poorly on new test data. What is this phenomenon called?
Select an answer first
12foundation · easy
A team has limited GPU resources and needs to train a model within a few hours. Which consideration is most relevant to algorithm selection?
Select an answer first
13foundation · easy
In a medical screening test for a rare disease, the cost of missing a positive case (false negative) is very high. Which performance metric should be prioritized when selecting the model?
Select an answer first
14foundation · easy
A spam filter should not block important emails (false positives are very costly). Which metric should guide algorithm selection?
Select an answer first
15application · medium
A data science team is developing a model to predict equipment failure in a manufacturing plant. They have a moderate-sized dataset with high variance, meaning different training subsets produce very different models. They need to reduce variance without losing too much interpretability. Which approach is most appropriate?
Select an answer first

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Factors Involved in ML Algorithm Selection CT-AI Practice Questions (Page 3)

Questions 11–15

A very complex model performs perfectly on training data but poorly on new test data. What is this phenomenon called?

A team has limited GPU resources and needs to train a model within a few hours. Which consideration is most relevant to algorithm selection?

In a medical screening test for a rare disease, the cost of missing a positive case (false negative) is very high. Which performance metric should be prioritized when selecting the model?

A spam filter should not block important emails (false positives are very costly). Which metric should guide algorithm selection?

Keep studying — Domain 3: Machine Learning (ML) - Overview