CT-GenAI考證 - CT-GenAI考題套裝

CT-GenAI認證考試是現今很受歡迎的考試。還沒有取得這個考試的認證資的你，肯定也想參加這個考試了吧。確實，這是一個困難的考試，但是這也並不是說不能 取得高分輕鬆通過考試。那麼，還不知道通過考試的捷徑的你，想知道技巧嗎？我現在告訴你，那就是KaoGuTi的CT-GenAI考古題。

ISQI CT-GenAI Exam Syllabus Topics:

SectionWeightObjectives
Fundamentals of Generative AI20%- AI Terminology
  • 1. Transformer architecture
  • 2. Large Language Models (LLMs)
  • 3. Tokens and prompts
  • 4. Machine Learning (ML)
  • 5. Deep Learning
  • 6. Artificial Intelligence (AI)
  • 7. Generative AI (GenAI)
- Generative AI Concepts
  • 1. Training data and context windows
  • 2. Alignment and guardrails
  • 3. Hallucinations
  • 4. Emergent capabilities and limitations
  • 5. Model types (Base, Instruction-tuned, RAG)
  • 6. AI model behavior
- AI Development Lifecycle
  • 1. Model training and fine-tuning
  • 2. Evaluation
  • 3. Deployment and monitoring
  • 4. Data collection, preparation, and curation
Risks and Testing Challenges for Generative AI30%- Quality Risks Specific to Generative AI
  • 1. Inconsistent responses across runs
  • 2. Input sensitivity (prompt brittleness)
  • 3. Dependency on external components
  • 4. Offensive, harmful, or biased content
  • 5. Inappropriate output for the context
  • 6. Incorrect or fabricated outputs (hallucinations)
- Testing Challenges for Generative AI
  • 1. Non-deterministic output behavior
  • 2. Coverage challenges
  • 3. Complexity of the AI component
  • 4. Ethical testing concerns
  • 5. Test oracle problem
  • 6. Regulatory and compliance considerations
  • 7. Subjectivity of quality assessment
Testing Activities for Generative AI30%- Prompt-Based Testing
  • 1. Prompt engineering basics
  • 2. Test data creation with GenAI
  • 3. Test case design using prompts
- Traceability and Documentation
  • 1. Test coverage of AI model components
  • 2. Documentation requirements for AI testing
- Requirements-Based Testing
  • 1. Functional requirements for AI-based systems
  • 2. Non-functional requirements for AI-based systems
  • 3. AI-related quality requirements
- Model and Output Evaluation
  • 1. Output correctness and quality assessment
  • 2. Automated evaluation methods
  • 3. Human evaluation methods
  • 4. Checkpoint testing
  • 5. Metamorphic testing
Tools for Testing Generative AI20%- Testing Tools Overview
  • 1. Selecting appropriate tools for specific testing needs
  • 2. Categories of GenAI testing tools
- Using Tools for Common Testing Activities
  • 1. Simulation and monitoring tools
  • 2. Security testing tools
  • 3. Model evaluation tools
  • 4. Prompt testing tools

>> CT-GenAI考證 <<

CT-GenAI考題套裝，CT-GenAI考試資訊

現在有許多IT培訓機構都能為你提供ISQI CT-GenAI 認證考試相關的培訓資料，但通常考生通過這些網站得不到詳細的資料。因為他們提供的關於ISQI CT-GenAI 認證考試資料都比較寬泛，不具有針對性，所以吸引不了考生的注意力。

最新的 AI Testing CT-GenAI 免費考試真題 (Q39-Q44):

問題 #39
Which AI approach requires feature engineering and structured data preparation?

答案：B

解題說明：
Classical Machine Learning(which includes algorithms like Random Forests, Support Vector Machines, and Linear Regression) is characterized by its reliance onFeature Engineering. This is the process where human experts manually select, extract, and transform raw data into a set of "features" or variables that the algorithm can process. For instance, in a classical ML model predicting software defects, a tester might have to manually define features like "lines of code changed" or "number of previous bugs." In contrast,Deep Learningand its subset,Generative AI(Options B and D), utilize "Representation Learning." This means the multi-layered neural networks automatically identify and extract the relevant features from raw, often unstructured data (like text or images) without explicit human instruction.Symbolic AI(Option A) is based on hard-coded logical rules rather than data-driven learning. Understanding this distinction is fundamental for testers, as it determines the level of data preparation required: Classical ML requires high human effort in data structuring, while GenAI requires high effort in prompt engineering and grounding.


問題 #40
What is a primary compliance concern related to Shadow AI in organizational test environments?

答案：C

解題說明：
Shadow AIrefers to the use of artificial intelligence tools and services within an organization without explicit approval or oversight from the IT or Security departments. In a software testing environment, this often occurs when testers use public, consumer-grade LLMs to analyze proprietary code or sensitive requirement documents to speed up their work. The primary compliance concern is theviolation of established data handling and regulatory compliance standards(such as GDPR, HIPAA, or SOC2). When sensitive test data is fed into a "shadow" AI tool, that data may be stored on external servers or used to train future iterations of the model, leading to massive data leaks and legal exposure. This bypasses the organization's security controls, such as data masking and role-based access. Unlike "authorized" AI which undergoes a rigorous vendor risk assessment, Shadow AI creates an invisible attack surface. For a test organization, mitigating this risk involves providing approved, secure AI alternatives and implementing strict policies and monitoring to ensure that internal intellectual property is never processed by unvetted external services.


問題 #41
Consider applying the meta-prompting technique to generate automated test scripts for API testing. You need to test a REST API endpoint that processes user registration with validation rules. Which one of the following prompts is BEST suited to this task?

答案：A

解題說明：
Option A is the superior choice because it strictly adheres to thestructured prompting patternrecommended in the CT-GenAI syllabus. This pattern divides the prompt into six distinct components:Role, Context, Instruction, Input Data, Constraints, and Output Format.By specifying theRole(Senior Test Automation Engineer), the model accesses relevant technical knowledge. TheInstructionis specific about using pytest and the requests library, and it explicitly lists both positive and negative scenarios. Most importantly, the Constraintssection provides the necessary "guardrails" for the code structure, such as the use of fixtures and clear assertions. Options B, C, and D are increasingly vague and fail to provide the model with the necessary technical boundaries to produce "production-ready" testware. Structured prompting reduces the "probabilistic drift" of the model, ensuring the output is not just functional code, but a script that follows industry-standard testing patterns (like modularity and clean naming conventions), making it directly usable within a CI/CD pipeline.


問題 #42
Which option BEST differentiates the three prompting techniques?

答案：C

解題說明：
Differentiating between prompting techniques is essential for a tester to select the right tool for the task.Few- shot promptingis characterized by providing the model with a few examples of inputs and desired outputs, allowing it to learn the pattern and format.Prompt Chaininginvolves breaking a complex task into a sequence of smaller, interconnected prompts, where the output of one step becomes the input for the next (e.g., first extract requirements, then generate test cases from those requirements).Meta-promptingis a more advanced technique where the user asks the LLM to help design, write, or refine the prompt itself-essentially using the AI as a "prompt engineer" to optimize the instructions. Option D correctly identifies these core characteristics.
Options A, B, and C contain fundamental mischaracterizations: for instance, Few-shotrequiresexamples (contradicting A), and Chaining is theoppositeof a single prompt (contradicting A). Mastering these distinctions allows testers to move from simple "chatting" to sophisticated AI orchestration that can handle complex, multi-stage testing workflows with high reliability.


問題 #43
Which concept refers to breaking text into smaller units for processing by LLMs?

答案：C

解題說明：
Tokenizationis the foundational process by which an LLM breaks down raw text into smaller, manageable units called "tokens." These tokens can represent individual words, parts of words (sub-words), or even punctuation marks. This is a critical step because LLMs do not "read" words like humans do; they process numerical representations of these tokens. The way text is tokenized directly impacts the model's efficiency and its ability to understand complex technical terminology used in software testing. For example, a rare technical term might be broken into several sub-word tokens. This process is closely linked to theContext Window(Option C), which is the maximum number of tokens a model can "remember" or process at one time. WhileEmbeddings(Option B) are the numerical vectors that represent the meaning of these tokens, and theTransformer(Option A) is the underlying architecture that processes them, tokenization is the specific mechanism for initial text decomposition. Understanding tokenization is vital for testers when managing long requirement documents to ensure they do not exceed the model's limits.


問題 #44
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CT-GenAI考題套裝: https://www.kaoguti.com/CT-GenAI_exam-pdf.html