[2026年最新] 高合格率なCT-GenAIテストアンサーかつISQI CT-GenAIテストPDF [Q14-Q32]

Rate this post

[2026年最新] 高合格率なCT-GenAIテストアンサーかつISQI CT-GenAIテストPDF

完璧CT-GenAI問題集試験問題と解答でパス保証されます

質問14、 Which setting can reduce variability by narrowing the sampling distribution during inference?

Increasing temperature

Increasing learning rate

Lowering temperature

Using a larger context window

質問15、 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

質問16、 When an organization uses an AI chatbot for testing, what is the PRIMARY LLMOps concern?

Maximizing scalability by deploying larger cloud-based LLM clusters

Maintaining data privacy and minimizing security risks from external services

Achieving faster responses by reducing model checkpoints and updates

Focusing primarily on user experience improvements and response formatting

質問17、 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.

質問18、 Which statement about fine-tuning for test tasks is INCORRECT?

It adapts a pre-trained model to a domain using task-specific data

It replaces the model’s general knowledge entirely and prevents overfitting

It enhances relevance to organizational terminology and formats

It can be applied to smaller SLMs to improve task performance with lower compute

質問19、 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

質問20、 Which AI approach requires feature engineering and structured data preparation?

Symbolic AI

Generative AI

Classical Machine Learning

Deep Learning

質問21、 Which technique MOST directly reduces hallucinations by grounding the model in project realities?

Provide detailed context

Randomize prompts each run

Rely on generic examples only

Use longer temperature settings

質問22、 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

質問23、 In the context of software testing, which statements (i-v) about foundation, instruction-tuned, and reasoning LLMs are CORRECT?
i. Foundation LLMs are best suited for broad exploratory ideation when test requirements are underspecified.
ii. Instruction-tuned LLMs are strongest at adhering to fixed test case formats (e.g., Gherkin) from clear prompts.
iii. Reasoning LLMs are strongest at multi-step root-cause analysis across logs, defects, and requirements.
iv. Foundation LLMs are optimal for strict policy compliance and template conformance.
v. Instruction-tuned LLMs can follow stepwise reasoning without any additional training or prompting.

i, ii, iii

i, iii, v

i, ii, iii (Duplicate entry in original source)

ii, iii, iv

質問24、 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

質問25、 Which consideration BEST aligns LLM choice with organizational goals in a GenAI testing strategy?

Select models with maximum vendor visibility and strong online presence to ensure reliability

Select open-source models prioritizing creativity over compliance or performance consistency

Select broad-coverage models offering diverse functionalities for various test scenarios

Select LLMs aligned to measurable test outcomes, compatible with current infrastructure

質問26、 What defines a prompt pattern in the context of structured GenAI capability building?

Treating prompts as access credentials or compliance records rather than functional templates

Maintaining static documentation repositories without real-time prompt standardization processes

Applying a reusable and structured template that guides GenAI models toward consistent outputs

Using ad hoc prompts without reference to previously proven structures or examples

質問27、 You must use GenAI to perform test analysis on a payments module with finalized requirements: (1) generate test conditions, (2) prioritize by risk, (3) check coverage gaps. Which sequence best applies prompt chaining?

Generate prioritized conditions in one shot -> verify coverage

Generate conditions -> prioritize by risk -> map to requirements to find gaps

Detect requirement defects -> generate conditions -> prioritize

Prioritize requirements -> generate conditions -> review defects

質問28、 What is a hallucination in LLM outputs?

A transient network failure during inference

A logical mistake in multi-step deduction

Generation of factually incorrect content for the task

A systematic preference learned from data

質問29、 A prompt begins: “You are a senior test manager responsible for risk-based test planning on a payments platform.” Which component is this?

Instruction

Context

Role

Constraints

質問30、 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

CT-GenAI試験問題高合格率なCT-GenAI問題集PDF：https://www.goshiken.com/ISQI/CT-GenAI-mondaishu.html

Tags: CT-GenAIブロンズ教材, CT-GenAI再テスト, CT-GenAI日本語参考, CT-GenAI最新な問題集

[2026年最新] 高合格率なCT-GenAIテストアンサーかつISQI CT-GenAIテストPDF [Q14-Q32]

コメントを残すコメントをキャンセル

関連認証

CT-GenAI 練習テスト

最近の投稿

アーカイブ

カテゴリー

[2026年最新] 高合格率なCT-GenAIテストアンサーかつISQI CT-GenAIテストPDF [Q14-Q32]

コメントを残す コメントをキャンセル

関連認証

CT-GenAI 練習テスト

最近の投稿

アーカイブ

カテゴリー

コメントを残すコメントをキャンセル