그리고 Itcertkr AIF-C01 시험 문제집의 전체 버전을 클라우드 저장소에서 다운로드할 수 있습니다: https://drive.google.com/open?id=1i6SW4ujU5ZtGL8tWuqgb2RMlwBIE7d97
최근들어 Amazon AIF-C01시험이 큰 인기몰이를 하고 있는 가장 핫한 IT인증시험입니다. Amazon AIF-C01덤프는Amazon AIF-C01시험 최근문제를 해석한 기출문제 모음집으로서 시험패스가 한결 쉬워지도록 도와드리는 최고의 자료입니다. Amazon AIF-C01인증시험을 패스하여 자격증을 취득하면 보다 쉽고 빠르게 승진할수 있고 연봉인상에도 많은 도움을 얻을수 있습니다.
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Itcertkr 의 Amazon인증 AIF-C01시험에 도전장을 던지셨나요? 현황에 만족하지 않고 열심히 하는 모습에 박수를 보내드립니다. Amazon인증 AIF-C01시험을 학원등록하지 않고 많은 공부자료 필요없이Itcertkr 에서 제공해드리는 Amazon인증 AIF-C01덤프만으로도 가능합니다. 수많은 분들이 검증한Amazon인증 AIF-C01덤프는 시장에서 가장 최신버전입니다.가격도 친근하구요.
질문 # 21
An education company is building a chatbot whose target audience is teenagers. The company is training a custom large language model (LLM). The company wants the chatbot to speak in the target audience's language style by using creative spelling and shortened words.
Which metric will assess the LLM's performance?
정답：B
설명：
The verified answer is D. Bilingual Evaluation Understudy (BLEU) score. The company wants to evaluate whether the chatbot's generated language matches a target communication style that includes creative spelling and shortened words. AWS AI Practitioner guidance lists BLEU, ROUGE, and BERTScore as relevant metrics for assessing foundation model performance. AWS Prescriptive Guidance describes BLEU as a metric-based evaluation method that measures n-gram overlap and focuses on precision. Because the requirement is about matching a particular style of generated language against reference examples, BLEU is the best fit among the given options.
F1 score is incorrect because it is primarily used for classification-style problems where precision and recall are combined into one score. It is not the correct metric for evaluating generated text style.
BERTScore is incorrect in this specific question because BERTScore focuses on semantic similarity using contextual embeddings. That can be useful when paraphrases should be accepted, but the question emphasizes surface language style: creative spelling, shortened words, and target-audience wording. Those are closer to token and phrase overlap against reference-style responses.
ROUGE is incorrect because ROUGE is commonly used for summarization and measures overlap with reference text, especially recall-oriented overlap. The scenario is not primarily asking whether the chatbot preserved all important content from a source document. It is asking whether the generated response matches a language style.
BLEU is therefore the best answer because it can compare the generated chatbot output to reference examples written in the desired teenage language style and measure how closely the model's wording overlaps with those references.
질문 # 22
An AI practitioner is using Amazon Bedrock Prompt Management to create a reusable prompt. The prompt must be able to interact with external services by calling an external API. Which solution will meet this requirement?
정답：D
설명：
The correct answer is B because Amazon Bedrock Prompt Management supports tool use via tools configuration, which enables a prompt to define tools that can invoke external APIs or services.
According to the AWS documentation:
"You can use the tools configuration in Amazon Bedrock to specify external APIs that a foundation model can call during inference. This enables the model to interact with external services, such as invoking functions, retrieving real-time data, or executing workflows." The tools configuration allows a prompt to describe which external functions (APIs) are available, their parameters, and how they should be invoked, similar to OpenAI's function calling or tool use pattern.
질문 # 23
A company uses Amazon SageMaker AI to generate article summaries in multiple languages. The company needs a metric to evaluate the quality of the summary translations in multiple languages. Which evaluation metric will meet these requirements?
정답：B
설명：
BLEU (Bilingual Evaluation Understudy) is the standard metric for evaluating machine translation quality across multiple languages.
ROUGE is for summarization quality (not translation).
AUC is for classification model performance.
Precision is a general metric but not specific for evaluating translations.
Reference:
AWS Documentation - Evaluation Metrics for NLP
질문 # 24
Sated and order the steps from the following bat to correctly describe the ML Lifecycle for a new custom modal Select each step one time. (Select and order FOUR.)
* Define the business objective.
* Deploy the modal.
* Develop and tram the model.
* Process the data.
정답：
설명：
Step 1: Define the business objective.
Step 2: Process the data.
Step 3: Develop and train the model.
Step 4: Deploy the model.
The correct order represents the machine learning lifecycle as defined by AWS in the Amazon SageMaker documentation and AWS Certified Machine Learning Specialty Study Guide. The lifecycle describes the sequence of tasks required to build, train, and deploy a custom ML model effectively.
From AWS documentation:
" The machine learning process begins with defining the business problem, followed by collecting and processing data, developing and training models, and finally deploying them into production for inference. " Step 1 - Define the business objective:
This step involves clearly identifying the business problem to be solved and determining the measurable outcomes expected from the ML model. This ensures alignment between business goals and ML outputs.
Step 2 - Process the data:
Data is collected, cleaned, transformed, and prepared for training. This includes handling missing values, normalizing data, and performing feature engineering - a crucial phase that influences model performance.
Step 3 - Develop and train the model:
The model is built and trained on the processed data using algorithms appropriate to the problem (e.g., regression, classification, clustering). Hyperparameters are tuned to optimize model accuracy.
Step 4 - Deploy the model:
Once validated, the model is deployed to a production environment (e.g., Amazon SageMaker endpoint) to make predictions on new data. Continuous monitoring and retraining ensure the model remains effective.
Referenced AWS AI/ML Documents and Study Guides:
Amazon SageMaker Developer Guide - Machine Learning Lifecycle
AWS Certified Machine Learning Specialty Study Guide - Model Development Lifecycle AWS ML Best Practices Whitepaper - End-to-End ML Workflow
질문 # 25
A company is building a generative AI (GenAI) application. The company wants to implement mechanisms to monitor and direct AI system behavior.
Which responsible AI dimension is the company applying?
정답：D
설명：
The verified answer is C. Controllability. AWS defines responsible AI through several dimensions, including fairness, explainability, privacy and security, safety, controllability, veracity and robustness, governance, and transparency. The exact wording in the question is the clue: the company wants mechanisms to monitor and direct AI system behavior. AWS describes controllability as having mechanisms to monitor and steer AI system behavior. That maps directly to the requirement.
Fairness is incorrect because fairness focuses on considering impacts on different groups of stakeholders. It is about avoiding unjust outcomes, biased treatment, or disproportionate harm across populations. The question does not describe demographic representation, biased outputs, or unequal model behavior.
Explainability is incorrect because explainability focuses on understanding and evaluating system outputs. It helps stakeholders understand why the model generated a result or how model behavior can be interpreted.
The question is not asking about understanding a decision; it is asking about controlling and directing system behavior.
Safety is incorrect because safety focuses on reducing harmful system output and misuse. Safety is related, but the wording "monitor and direct AI system behavior" is specifically the AWS definition of controllability, not safety. Safety might include preventing toxic, harmful, or dangerous outputs, but controllability is the broader dimension about steering the system according to intended behavior.
Therefore, the responsible AI dimension being applied is controllability, because the company wants active mechanisms to guide, monitor, and steer how the generative AI application behaves in operation.
질문 # 26
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Itcertkr 의 Amazon인증 AIF-C01시험에 도전장을 던지셨나요? 현황에 만족하지 않고 열심히 하는 모습에 박수를 보내드립니다. Amazon인증 AIF-C01시험을 학원등록하지 않고 많은 공부자료 필요없이Itcertkr 에서 제공해드리는 Amazon인증 AIF-C01덤프만으로도 가능합니다. 수많은 분들이 검증한Amazon인증 AIF-C01덤프는 시장에서 가장 최신버전입니다.가격도 친근하구요.
AIF-C01최고품질 덤프샘플문제 다운: https://www.itcertkr.com/AIF-C01_exam.html
그 외, Itcertkr AIF-C01 시험 문제집 일부가 지금은 무료입니다: https://drive.google.com/open?id=1i6SW4ujU5ZtGL8tWuqgb2RMlwBIE7d97