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시험대비AIF-C01유효한최신덤프자료덤프데모문제다운받기

ITDumpsKR의 Amazon AIF-C01덤프로Amazon AIF-C01시험준비를 하면 시험패스는 간단한 일이라는걸 알게 될것입니다. Amazon AIF-C01덤프는 최근Amazon AIF-C01시험의 기출문제모음으로 되어있기에 적중율이 높습니다.시험에서 떨어지면 덤프비용 전액 환불해드리기에 우려없이 덤프를 주문하셔도 됩니다.

Amazon AIF-C01 시험요강:

주제
소개

주제 1

Fundamentals of Generative AI: This domain explores the basics of generative AI, focusing on techniques for creating new content from learned patterns, including text and image generation. It targets professionals interested in understanding generative models, such as developers and researchers in AI.

주제 2

Fundamentals of AI and ML: This domain covers the fundamental concepts of artificial intelligence (AI) and machine learning (ML), including core algorithms and principles. It is aimed at individuals new to AI and ML, such as entry-level data scientists and IT professionals.

주제 3

Security, Compliance, and Governance for AI Solutions: This domain covers the security measures, compliance requirements, and governance practices essential for managing AI solutions. It targets security professionals, compliance officers, and IT managers responsible for safeguarding AI systems, ensuring regulatory compliance, and implementing effective governance frameworks.

주제 4

Applications of Foundation Models: This domain examines how foundation models, like large language models, are used in practical applications. It is designed for those who need to understand the real-world implementation of these models, including solution architects and data engineers who work with AI technologies to solve complex problems.

주제 5

Guidelines for Responsible AI: This domain highlights the ethical considerations and best practices for deploying AI solutions responsibly, including ensuring fairness and transparency. It is aimed at AI practitioners, including data scientists and compliance officers, who are involved in the development and deployment of AI systems and need to adhere to ethical standards.

>> AIF-C01유효한 최신덤프자료 <<

AIF-C01인기자격증 시험대비 공부자료, AIF-C01완벽한 덤프자료

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최신 AWS Certified AI AIF-C01 무료샘플문제 (Q43-Q48):

질문 # 43
Which option is a benefit of ongoing pre-training when fine-tuning a foundation model (FM)?

A. Improves model performance over time
B. Optimizes model inference time
C. Helps decrease the model's complexity
D. Decreases the training time requirement

정답：A

질문 # 44
A company has built a solution by using generative AI. The solution uses large language models (LLMs) to translate training manuals from English into other languages. The company wants to evaluate the accuracy of the solution by examining the text generated for the manuals.
Which model evaluation strategy meets these requirements?

A. F1 score
B. Recall-Oriented Understudy for Gisting Evaluation (ROUGE)
C. Root mean squared error (RMSE)
D. Bilingual Evaluation Understudy (BLEU)

정답：D

설명：
BLEU (Bilingual Evaluation Understudy) is a metric used to evaluate the accuracy of machine-generated translations by comparing them against reference translations. It is commonly used for translation tasks to measure how close the generated output is to professional human translations.
Option A (Correct): "Bilingual Evaluation Understudy (BLEU)": This is the correct answer because BLEU is specifically designed to evaluate the quality of translations, making it suitable for the company's use case.
Option B: "Root mean squared error (RMSE)" is incorrect because RMSE is used for regression tasks to measure prediction errors, not translation quality.
Option C: "Recall-Oriented Understudy for Gisting Evaluation (ROUGE)" is incorrect as it is used to evaluate text summarization, not translation.
Option D: "F1 score" is incorrect because it is typically used for classification tasks, not for evaluating translation accuracy.
AWS AI Practitioner Reference:
Model Evaluation Metrics on AWS: AWS supports various metrics like BLEU for specific use cases, such as evaluating machine translation models.

질문 # 45
Which functionality does Amazon SageMaker Clarify provide?

A. Integrates a Retrieval Augmented Generation (RAG) workflow
B. Monitors the quality of ML models in production
C. Documents critical details about ML models
D. Identifies potential bias during data preparation

정답：D

설명：
Exploratory data analysis (EDA) involves understanding the data by visualizing it, calculating statistics, and creating correlation matrices. This stage helps identify patterns, relationships, and anomalies in the data, which can guide further steps in the ML pipeline.
* Option C (Correct): "Exploratory data analysis": This is the correct answer as the tasks described (correlation matrix, calculating statistics, visualizing data) are all part of the EDA process.
* Option A: "Data pre-processing" is incorrect because it involves cleaning and transforming data, not initial analysis.
* Option B: "Feature engineering" is incorrect because it involves creating new features from raw data, not analyzing the data's existing structure.
* Option D: "Hyperparameter tuning" is incorrect because it refers to optimizing model parameters, not analyzing the data.
AWS AI Practitioner References:
* Stages of the Machine Learning Pipeline: AWS outlines EDA as the initial phase of understanding and exploring data before moving to more specific preprocessing, feature engineering, and model training stages.

질문 # 46
Which functionality does Amazon SageMaker Clarify provide?

A. Integrates a Retrieval Augmented Generation (RAG) workflow
B. Monitors the quality of ML models in production
C. Documents critical details about ML models
D. Identifies potential bias during data preparation

정답：D

질문 # 47
A company wants to use generative AI to increase developer productivity and software development. The company wants to use Amazon Q Developer.
What can Amazon Q Developer do to help the company meet these requirements?

A. Create software snippets, reference tracking, and open-source license tracking.
B. Enable voice commands for coding and providing natural language search.
C. Convert audio files to text documents by using ML models.
D. Run an application without provisioning or managing servers.

정답：A

설명：
Amazon Q Developer is a tool designed to assist developers in increasing productivity by generating code snippets, managing reference tracking, and handling open-source license tracking. These features help developers by automating parts of the software development process.
* Option A (Correct): "Create software snippets, reference tracking, and open-source license tracking": This is the correct answer because these are key features that help developers streamline and automate tasks, thus improving productivity.
* Option B: "Run an application without provisioning or managing servers" is incorrect as it refers to AWS Lambda or AWS Fargate, not Amazon Q Developer.
* Option C: "Enable voice commands for coding and providing natural language search" is incorrect because this is not a function of Amazon Q Developer.
* Option D: "Convert audio files to text documents by using ML models" is incorrect as this refers to Amazon Transcribe, not Amazon Q Developer.
AWS AI Practitioner References:
* Amazon Q Developer Features: AWS documentation outlines how Amazon Q Developer supports developers by offering features that reduce manual effort and improve efficiency.

질문 # 48
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