ISTQB CT-GenAI - Certified Tester Testing with Generative AI Exam
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Total 39 questions
Question #1 (Topic: Exam A)
Which statement ACCURATELY describes Classical Machine Learning within the context of AI?
A. It primarily relies on rule-based systems to model human decision processes.
B. It utilizes neural networks with multiple hidden layers to automatically discover patterns in raw data.
C. It processes vast textual datasets to generate new, human-like content by mimicking learned patterns.
D. It employs a data-driven methodology that involves explicit data preparation and feature engineering.
Answer: D
Question #2 (Topic: Exam A)
Which of the following statements BEST describes the purpose of embeddings in Large Language Models (LLMs) when processing and generating content?
A. They define the maximum length of text an LLM can process at one time to maintain coherence.
B. They break down input text into smaller units, such as words or sub-words, for individual processing.
C. They are numerical representations of tokens that capture their semantic, syntactic, and contextual relationships.
D. They control the randomness or creativity of an LLM's outputs during text generation.
Answer: C
Question #3 (Topic: Exam A)
In the context of Large Language Models (LLMs), which of the following statements (i-v) regarding 'tokenization' and 'context window' are CORRECT?
i) Tokenization is the process of breaking down textual input into smaller units called tokens.
ii) The context window dictates the maximum number of tokens that an LLM can consider at any given time to maintain coherence.
iii) Increasing the size of an LLM's context window generally leads to a reduction in computational complexity and processing time.
iv) Tokenization primarily involves converting tokens into high-dimensional vectors to capture their semantic relationships.
v) A larger context window allows an LLM to maintain coherence over longer passages, such as when analyzing large test logs.
i) Tokenization is the process of breaking down textual input into smaller units called tokens.
ii) The context window dictates the maximum number of tokens that an LLM can consider at any given time to maintain coherence.
iii) Increasing the size of an LLM's context window generally leads to a reduction in computational complexity and processing time.
iv) Tokenization primarily involves converting tokens into high-dimensional vectors to capture their semantic relationships.
v) A larger context window allows an LLM to maintain coherence over longer passages, such as when analyzing large test logs.
A. i, ii, and iii
B. i, ii, and v
C. ii, iv, and v
D. iii, iv, and v
Answer: B
Question #4 (Topic: Exam A)
In the context of LLMs, which statement BEST characterizes an instruction-tuned Large Language Model?
A. It is a general-purpose model primarily used for broad tasks without specific adaptation.
B. It is specifically designed to perform complex multi-step logical inference and problem-solving.
C. It is a foundation model that has been further refined to better follow human instructions and user intent.
D. It can create new content like images or code by learning patterns from diverse training data.
Answer: C
Question #5 (Topic: Exam A)
A test team is analyzing screenshots of a mobile application's GUI alongside textual defect reports to identify visual inconsistencies that are not captured by these reports.
Which type of Generative AI model is MOST suited to assist with this task?
Which type of Generative AI model is MOST suited to assist with this task?
A. Symbolic AI, due to its rule-based system for mimicking human decision-making.
B. Classical Machine Learning, as it excels at data-driven defect categorization.
C. Multimodal LLMs, specifically those augmented with vision-language models.
D. Reasoning LLMs, given their emphasis on logical inference for complex problem-solving.
Answer: C
