ISQI Certified CT-GenAI Dumps Questions Valid CT-GenAI Materials [Q18-Q32]

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ISQI Certified CT-GenAI Dumps Questions Valid CT-GenAI Materials

Current CT-GenAI Exam Dumps [2026] Complete ISQI Exam Smoothly

ISQI CT-GenAI Exam Syllabus Topics:

Section	Weight	Objectives
Tools for Testing Generative AI	20%	– Using Tools for Common Testing Activities 1. Simulation and monitoring tools 2. Model evaluation tools 3. Security testing tools 4. Prompt testing tools – Testing Tools Overview 1. Categories of GenAI testing tools 2. Selecting appropriate tools for specific testing needs
Fundamentals of Generative AI	20%	– Generative AI Concepts 1. Training data and context windows 2. Alignment and guardrails 3. AI model behavior 4. Model types (Base, Instruction-tuned, RAG) 5. Hallucinations 6. Emergent capabilities and limitations – AI Terminology 1. Artificial Intelligence (AI) 2. Machine Learning (ML) 3. Generative AI (GenAI) 4. Tokens and prompts 5. Deep Learning 6. Large Language Models (LLMs) 7. Transformer architecture – AI Development Lifecycle 1. Data collection, preparation, and curation 2. Evaluation 3. Deployment and monitoring 4. Model training and fine-tuning
Testing Activities for Generative AI	30%	– Prompt-Based Testing 1. Test case design using prompts 2. Prompt engineering basics 3. Test data creation with GenAI – Requirements-Based Testing 1. Non-functional requirements for AI-based systems 2. AI-related quality requirements 3. Functional requirements for AI-based systems – Model and Output Evaluation 1. Output correctness and quality assessment 2. Checkpoint testing 3. Automated evaluation methods 4. Metamorphic testing 5. Human evaluation methods – Traceability and Documentation 1. Test coverage of AI model components 2. Documentation requirements for AI testing
Risks and Testing Challenges for Generative AI	30%	– Quality Risks Specific to Generative AI 1. Inappropriate output for the context 2. Inconsistent responses across runs 3. Offensive, harmful, or biased content 4. Input sensitivity (prompt brittleness) 5. Dependency on external components 6. Incorrect or fabricated outputs (hallucinations) – Testing Challenges for Generative AI 1. Subjectivity of quality assessment 2. Test oracle problem 3. Ethical testing concerns 4. Regulatory and compliance considerations 5. Non-deterministic output behavior 6. Coverage challenges 7. Complexity of the AI component

NO.18 Which concept refers to breaking text into smaller units for processing by LLMs?

Transformer

Embeddings

Context Window

Tokenization

NO.19 Which setting can reduce variability by narrowing the sampling distribution during inference?

Increasing temperature

Increasing learning rate

Lowering temperature

Using a larger context window

NO.20 A tester uploads crafted images that steer the LLM into validating non-existent acceptance criteria. Which attack vector is this?

Data poisoning

Data exfiltration

Request manipulation

Malicious code generation

NO.21 A team notices vague, inconsistent LLM outputs for the same story for two different prompts. Which technique BEST helps choose the stronger wording among two prompt versions using predefined metrics?

A/B testing of prompts

Iterative prompt modification

Output analysis

Integrating user feedback

NO.22 Which statement BEST describes vision-language models (VLMs)?

VLMs are a subset of multimodal LLMs integrating visual and textual information.

VLMs are unrelated to multimodal LLMs and focus only on UI automation.

VLMs are a superset of multimodal LLMs.

VLMs process audio and video but not images.

NO.23 The model flags anomalies in logs and also proposes partitions for input validation tests. Which metrics BEST evaluate these two outcomes together?

Precision for anomaly identification and recall for coverage of valid/invalid partitions

Time efficiency for anomaly detection and accuracy for coverage of valid/invalid partitions

Diversity for anomaly identification and precision for partitions

Accuracy for anomaly detection and Precision for coverage of valid/invalid partitions

NO.24 Which statement about data privacy risks in GenAI-assisted testing is INCORRECT?

Some GenAI tools may store/process data without explicit consent

GenAI outputs can accidentally reveal sensitive information present in inputs

Strict GDPR compliance eliminates all privacy risk

Using GenAI without regulatory compliance can lead to legal exposure

NO.25 Which statement BEST differentiates an LLM-powered test infrastructure from a traditional chatbot system used in testing?

It dynamically generates test insights using contextual information

It produces scripted conversational responses similar to traditional bots

It focuses primarily on visual dashboards and user navigation features

It provides fixed responses from predefined rule sets and scripts

NO.26 You are using an LLM to assist in analyzing test execution trends to predict potential risks. Which of the following improvements would BEST enhance the LLM’s ability to predict risks and provide actionable alerts?

Emphasize constraints that focus on deviations that could impact release timelines or quality gates.

Expand the output format to include risk predictions with severity levels, recommended actions, and a timeline for team intervention based on trend analysis.

Specify that the role is a test analyst with expertise in predictive analytics and risk management.

Add an instruction to calculate statistical variance and highlight tests that deviate by more than 20% from baseline metrics.

NO.27 Which competency MOST helps testers steer LLMs to produce useful, on-policy testware?

Mastering prompt engineering

Configuring network routers

Writing low-level device drivers

Designing custom CPU instructions

NO.28 Your team needs to generate 500 API test cases for a REST API with 50 endpoints. You have documented 10 exemplar test cases that follow your organization’s standard format. You want the LLM to generate test cases following the pattern demonstrated in your examples. Which of the following prompting techniques is BEST suited to achieve your goal in this scenario?

Prompt chaining

Few-shot prompting

Meta prompting

Zero-shot prompting

NO.29 You are tasked with applying structured prompting to perform impact analysis on recent code changes. Which of the following improvements would BEST align the prompt with structured prompt engineering best practices for comprehensive impact analysis?

Include references to version control systems like Git in the constraints.

Specify that the role is a test architect specializing in CI/CD pipelines.

Add a step to review the change log for syntax errors before analysis.

Include mapping code changes to affected modules, identifying test cases, prioritizing by risk level and change complexity

NO.30 Which factor MOST influences the overall energy consumption of a Generative AI model used in software testing tasks?

The number of tokens processed directly determines the carbon intensity of each query

The location of the data center determines model bias and accuracy levels

The duration of user sessions primarily affects latency but not power efficiency

The type of cloud platform affects processing speed but not total energy draw

NO.31 What are the three key phases in adopting GenAI in a test organization?

Discovery; initiation and usage definition; utilization and iteration

Prototype; pilot; decommission

Training; certification; outsourcing

Planning; execution; sign-off

NO.32 What does an embedding represent in an LLM?

Tokens grouped into context windows

Numerical vectors capturing semantic relationships

Logical rules for reasoning

A set of test cases for validation

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ISQI Certified CT-GenAI Dumps Questions Valid CT-GenAI Materials [Q18-Q32]

ISQI CT-GenAI Exam Syllabus Topics:

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