참고: Fast2test에서 Google Drive로 공유하는 무료 2026 Amazon AIF-C01 시험 문제집이 있습니다: https://drive.google.com/open?id=1g8Ha8leesZJ114q-6K8vQnIz71VydM5r
우리 Fast2test 에는 최신의Amazon AIF-C01학습가이드가 있습니다. Fast2test의 부지런한 IT전문가들이 자기만의 지식과 끊임없는 노력과 경험으로 최고의Amazon AIF-C01합습자료로Amazon AIF-C01인증시험을 응시하실 수 있습니다.Amazon AIF-C01인증시험은 IT업계에서의 비중은 아주 큽니다. 시험신청하시는분들도 많아지고 또 많은 분들이 우리Fast2test의Amazon AIF-C01자료로 시험을 패스했습니다. 이미 패스한 분들의 리뷰로 우리Fast2test의 제품의 중요함과 정확함을 증명하였습니다.
| Section | Objectives |
|---|---|
| Topic 1: Fundamentals of Generative AI | - Large language models (LLMs) concepts - AWS generative AI services overview (e.g., Amazon Bedrock) - Foundation models and prompt engineering basics |
| Topic 2: Fundamentals of Artificial Intelligence and Machine Learning | - Data fundamentals for AI/ML - Common ML workflows and lifecycle - Core AI and ML concepts |
| Topic 3: Responsible AI and Security | - Bias, fairness, and explainability - Security, privacy, and governance in AI systems - AI ethics and responsible use |
| Topic 4: Applications of Foundation Models | - AI-powered assistants and automation - Use cases for generative AI in business - Content generation and summarization |
Fast2test는Amazon AIF-C01시험에 필요한 모든 문제유형을 커버함으로서 Amazon AIF-C01시험을 합격하기 위한 최고의 선택이라 할수 있습니다. Amazon AIF-C01시험 Braindump를 공부하면 학원다니지 않으셔도 자격증을 취득할수 있습니다. Amazon AIF-C01 덤프정보 상세보기는 이 글의 링크를 클릭하시면 Fast2test사이트에 들어오실수 있습니다.
질문 # 72
A company wants to label training datasets by using human feedback to fine-tune a foundation model (FM). The company does not want to develop labeling applications or manage a labeling workforce. Which AWS service or feature meets these requirements?
정답：D
설명：
Amazon SageMaker Ground Truth Plus provides a fully managed data labeling service where AWS manages the workforce, tools, and processes.
Data Wrangler is for data preparation and transformation.
Transcribe is for speech-to-text.
Macie is for sensitive data discovery, not labeling.
Reference:
AWS Documentation - SageMaker Ground Truth Plus
질문 # 73
Which statement presents an advantage of using Retrieval Augmented Generation (RAG) for natural language processing (NLP) tasks?
정답：C
설명：
Comprehensive and Detailed
Retrieval-Augmented Generation (RAG) integrates external knowledge sources (databases, vector stores, document repositories) with LLMs, enabling them to generate contextually accurate and up-to-date responses without retraining.
B is incorrect: RAG does not speed up training; it improves inference results.
C is incorrect: speech recognition is not an RAG use case.
D is incorrect: computer vision augmentation is unrelated to RAG.
Reference:
AWS Documentation - Knowledge Bases for RAG in Amazon Bedrock
질문 # 74
A company is implementing intelligent agents to provide conversational search experiences for its customers.
The company needs a database service that will support storage and queries of embeddings from a generative AI model as vectors in the database.
Which AWS service will meet these requirements?
정답：C
설명：
The requirement is to identify an AWS database service that supports the storage and querying of embeddings (from a generative AI model) as vectors. Embeddings are typically high-dimensional numerical representations of data (e.g., text, images) used in AI applications like conversational search. The database must support vector storage and efficient vector similarity searches. Let's evaluate each option:
A). Amazon Athena: Amazon Athena is a serverless query service for analyzing data in Amazon S3 using SQL. It is designed for ad-hoc querying of structured data but does not natively support vector storage or vector similarity searches, making it unsuitable for this use case.
B). Amazon Aurora PostgreSQL: Amazon Aurora PostgreSQL is a fully managed relational database compatible with PostgreSQL. With the pgvector extension (available in PostgreSQL and supported by Aurora PostgreSQL), it can store and query vector embeddings efficiently. The pgvector extension enables vector similarity searches (e.g., using cosine similarity or Euclidean distance), which is critical for conversational search applications using embeddings from generative AI models.
C). Amazon Redshift: Amazon Redshift is a data warehousing service optimized for analytical queries on large datasets. While it supports machine learning features and can store numerical data, it does not have native support for vector embeddings or vector similarity searches as of May 17, 2025, making it less suitable for this use case.
D). Amazon EMR: Amazon EMR is a managed big data platform for processing large-scale data using frameworks like Apache Hadoop and Spark. It is not a database service and is not designed for storing or querying vector embeddings in the context of a conversational search application.
Exact Extract Reference: According to the AWS documentation, "Amazon Aurora PostgreSQL-Compatible Edition supports the pgvector extension, which enables efficient storage and similarity searches for vector embeddings. This makes it suitable for AI/ML workloads such as natural language processing and recommendation systems that rely on vector data." (Source: AWS Aurora Documentation - Using pgvector with Aurora PostgreSQL, https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide
/PostgreSQLpgvector.html). Additionally, the pgvector extension supports operations like nearest-neighbor searches, which are essential for querying embeddings in a conversational search system.
Amazon Aurora PostgreSQL with the pgvector extension directly meets the requirement for storing and querying embeddings as vectors, making B the correct answer.
References:
AWS Aurora Documentation: Using pgvector with Aurora PostgreSQL (https://docs.aws.amazon.com
/AmazonRDS/latest/AuroraUserGuide/PostgreSQLpgvector.html)
AWS AI Practitioner Study Guide (focus on data engineering for AI, including vector databases) AWS Blog on Vector Search with Aurora (https://aws.amazon.com/blogs/database/using-vector-search-with- amazon-aurora-postgresql/)
질문 # 75
A bank is building a chatbot to answer customer questions about opening a bank account. The chatbot will use public bank documents to generate responses. The company will use Amazon Bedrock and prompt engineering to improve the chatbot's responses.
Which prompt engineering technique meets these requirements?
정답：D
설명：
Directional stimulus prompting guides the foundation model to produce outputs aligned with business context. It's particularly effective for aligning responses with public documents and improving coherence. From Bedrock Prompt Engineering Techniques documentation:
Explanation:
Directional stimulus prompting guides the foundation model to produce outputs aligned with business context. It's particularly effective for aligning responses with public documents and improving coherence. From Bedrock Prompt Engineering Techniques documentation:
"Directional stimulus prompting provides structured prompts to steer the model output towards desired formats or behaviors using specific linguistic cues."
질문 # 76
An AI practitioner must fine-tune an open source large language model (LLM) for text categorization. The dataset is already prepared.
Which solution will meet these requirements with the LEAST operational effort?
정답：C
설명：
The correct answer is B because Amazon SageMaker JumpStart provides pre-built solutions, including training workflows for popular open-source LLMs such as Falcon, LLaMA, and others. It allows practitioners to quickly launch fine-tuning jobs using predefined templates, minimizing operational setup and code complexity.
From AWS documentation:
"Amazon SageMaker JumpStart enables you to fine-tune and deploy foundation models with minimal setup.
It provides easy-to-use interfaces and pre-built configurations for training, which significantly reduces the operational overhead required to train models." Explanation of other options:
A). PartyRock is designed for prototyping generative AI apps but does not support model training or fine- tuning.
C). Writing a custom script for SageMaker training is flexible but involves more operational effort, including handling infrastructure configuration.
D). Training on EC2 via a Jupyter notebook is fully manual and operationally intensive, including dependency setup, data handling, and resource scaling.
Referenced AWS AI/ML Documents and Study Guides:
* Amazon SageMaker JumpStart Developer Guide - Fine-tuning Foundation Models
* AWS Certified Machine Learning Specialty Guide - Model Customization and JumpStart
질문 # 77
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Fast2test Amazon인증AIF-C01시험덤프 구매전 구매사이트에서 무료샘플을 다운받아 PDF버전 덤프내용을 우선 체험해보실수 있습니다. 무료샘플을 보시면Fast2test Amazon인증AIF-C01시험대비자료에 믿음이 갈것입니다.고객님의 이익을 보장해드리기 위하여Fast2test는 시험불합격시 덤프비용전액환불을 무조건 약속합니다. Fast2test의 도움으로 더욱 많은 분들이 멋진 IT전문가로 거듭나기를 바라는바입니다.
AIF-C01시험준비공부: https://kr.fast2test.com/AIF-C01-premium-file.html
참고: Fast2test에서 Google Drive로 공유하는 무료, 최신 AIF-C01 시험 문제집이 있습니다: https://drive.google.com/open?id=1g8Ha8leesZJ114q-6K8vQnIz71VydM5r