A healthcare startup is developing an AI system to detect skin cancer from dermoscopic images. They have a small internal dataset and want to use a benchmark to validate their model. Which approach is most appropriate?

Use a general medical imaging benchmark like CheXpert.

Use ImageNet and fine-tune on the internal dataset.

Use the ISIC benchmark, which is specifically for skin lesion analysis.

Use only the internal dataset for evaluation.

A team is evaluating a language model on the GLUE benchmark. They want to report a single number that summarizes performance across all tasks. Which metric is commonly used for this purpose?

The average of task-specific metrics, such as accuracy and F1

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Domain 5Objective 4

Benchmark Suites for ML CT-AI Practice Questions (Page 5)

Part of the Domain 5: ML Functional Performance Metrics domain, which makes up ~5% of our current practice bank. ISTQB does not publish an official question count, but from its 60-minute exam (~25–40 total, ~1–2 in this domain), expect 1–1 from this objective — we provide 23 practice questions to prepare you well beyond it. (estimate)

23questions here

5free pages

5concepts

Questions 21–23

21expert · hard
A healthcare startup is developing an AI system to detect skin cancer from dermoscopic images. They have a small internal dataset and want to use a benchmark to validate their model. Which approach is most appropriate?
Select an answer first
22application · easy
A team is developing a model to classify images of everyday objects into 1000 categories. Which benchmark dataset is most appropriate for this task?
Select an answer first
23application · medium
A team is evaluating a language model on the GLUE benchmark. They want to report a single number that summarizes performance across all tasks. Which metric is commonly used for this purpose?
Select an answer first

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Benchmark Suites for ML CT-AI Practice Questions (Page 5)

Questions 21–23

A healthcare startup is developing an AI system to detect skin cancer from dermoscopic images. They have a small internal dataset and want to use a benchmark to validate their model. Which approach is most appropriate?

A team is developing a model to classify images of everyday objects into 1000 categories. Which benchmark dataset is most appropriate for this task?

A team is evaluating a language model on the GLUE benchmark. They want to report a single number that summarizes performance across all tasks. Which metric is commonly used for this purpose?

Keep studying — Domain 5: ML Functional Performance Metrics