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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions:

1. The model flags anomalies in logs and also proposes partitions for input validation tests. Which metrics BEST evaluate these two outcomes together?

A) Diversity for anomaly identification and precision for partitions
B) Precision for anomaly identification and recall for coverage of valid/invalid partitions
C) Accuracy for anomaly detection and Precision for coverage of valid/invalid partitions
D) Time efficiency for anomaly detection and accuracy for coverage of valid/invalid partitions

2. Which option BEST differentiates the three prompting techniques?

A) Chaining = give examples; Few-shot = break tasks; Meta = manual edits only
B) Few-shot = no examples; Chaining = single prompt; Meta = disable iteration
C) Few-shot = examples; Chaining = multi-step prompts; Meta = model helps draft/refine prompts
D) Meta = step decomposition; Chaining = zero-shot only; Few-shot = manual optimization

3. Which factor MOST influences the overall energy consumption of a Generative AI model used in software testing tasks?

A) The location of the data center determines model bias and accuracy levels
B) The number of tokens processed directly determines the carbon intensity of each query
C) The duration of user sessions primarily affects latency but not power efficiency
D) The type of cloud platform affects processing speed but not total energy draw

4. Which concept refers to breaking text into smaller units for processing by LLMs?

A) Tokenization
B) Transformer
C) Context Window
D) Embeddings

5. Which setting can reduce variability by narrowing the sampling distribution during inference?

A) Increasing temperature
B) Lowering temperature
C) Increasing learning rate
D) Using a larger context window

Solutions: