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AIF-C01日本語受験教科書 & AIF-C01日本語サンプル

あなたはまだ試験について心配していますか？心配しないで！ AIF-C01試験トレントは、作業または学習プロセス中にこの障害を克服するのに役立ちます。 AIF-C01テスト準備の指示の下で、非常に短時間でタスクを完了し、間違いなく試験に合格してAIF-C01証明書を取得できます。サービスをさまざまな個人に合わせて調整し、わずか20〜30時間の練習とトレーニングの後、目的の試験に参加できるようにします。さらに、理論と内容に関してAIF-C01クイズトレントを毎日更新する専門家がいます。

Amazon AIF-C01 認定試験の出題範囲：

トピック
出題範囲

トピック 1

AI ソリューションのセキュリティ、コンプライアンス、ガバナンス: このドメインでは、AI ソリューションの管理に不可欠なセキュリティ対策、コンプライアンス要件、ガバナンスプラクティスについて説明します。AI システムの保護、規制コンプライアンスの確保、効果的なガバナンスフレームワークの実装を担当するセキュリティ専門家、コンプライアンス担当者、IT マネージャーを対象としています。

トピック 2

責任ある AI のためのガイドライン: このドメインでは、公平性と透明性の確保など、AI ソリューションを責任を持って導入するための倫理的な考慮事項とベストプラクティスに焦点を当てています。これは、AI システムの開発と導入に携わり、倫理基準を遵守する必要があるデータサイエンティストやコンプライアンス担当者などの AI 実践者を対象としています。

トピック 3

AI と ML の基礎: このドメインでは、コアアルゴリズムと原則を含む、人工知能 (AI) と機械学習 (ML) の基本概念について説明します。初心者のデータサイエンティストや IT プロフェッショナルなど、AI と ML を初めて使用する個人を対象としています。

トピック 4

基礎モデルのアプリケーション: このドメインでは、大規模言語モデルなどの基礎モデルが実際のアプリケーションでどのように使用されるかを調べます。このドメインは、AI テクノロジーを使用して複雑な問題を解決するソリューションアーキテクトやデータエンジニアなど、これらのモデルの実際の実装を理解する必要がある人向けに設計されています。

トピック 5

生成 AI の基礎: このドメインでは、テキストや画像の生成など、学習したパターンから新しいコンテンツを作成する手法に焦点を当て、生成 AI の基礎を探ります。AI の開発者や研究者など、生成モデルの理解に関心のある専門家を対象としています。

>> AIF-C01日本語受験教科書 <<

AIF-C01日本語サンプル、AIF-C01テキスト

AIF-C01学習教材があれば、あなたは自分の夢を叶えます。AIF-C01学習教材はすごく人気があります。全世界のお客様からいい評価をもらいました。なんといっても、自分はAIF-C01学習教材を利用したら、その資料のよさを感じることができます。大切なのは、AIF-C01学習教材の合格率が高いので、多くのお客様はAIF-C01認定試験資格証明書を取得したということです。

Amazon AWS Certified AI Practitioner 認定 AIF-C01 試験問題 (Q21-Q26):

質問 # 21
A company wants to fine-tune an ML model that is hosted on Amazon Bedrock. The company wants to use its own sensitive data that is stored in private databases in a VPC. The data needs to stay within the company's private network.
Which solution will meet these requirements?

A. Use AWS Key Management Service (AWS KMS) keys to encrypt the data.
B. Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) resource policy.
C. Use AWS PrivateLink to connect the VPC and Amazon Bedrock.
D. Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) service role.

正解：C

解説：
The company wants to fine-tune an ML model on Amazon Bedrock using sensitive data stored in private databases within a VPC, ensuring the data remains within its private network. AWS PrivateLink provides a secure, private connection between a VPC and AWS services like Amazon Bedrock, allowing data to stay within the company's network without traversing the public internet. This meets the requirement for maintaining data privacy during fine-tuning.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"AWS PrivateLink enables you to securely connect your VPC to Amazon Bedrock without exposing data to the public internet. This is particularly useful for fine-tuning models with sensitive data, as it ensures that data remains within your private network." (Source: AWS Bedrock User Guide, Security and Networking) Detailed Option A: Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) service role.While IAM service roles control access to Amazon Bedrock, they do not address the requirement of keeping data within the private network during data transfer. This option is insufficient.
Option B: Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) resource policy.IAM resource policies define permissions for Bedrock resources but do not ensure that data stays within the private network. This option is incorrect.
Option C: Use AWS PrivateLink to connect the VPC and Amazon Bedrock.This is the correct answer. AWS PrivateLink creates a secure, private connection between the VPC and Amazon Bedrock, ensuring that sensitive data does not leave the private network during fine-tuning, as required.
Option D: Use AWS Key Management Service (AWS KMS) keys to encrypt the data.While AWS KMS can encrypt data, encryption alone does not guarantee that data remains within the private network during transfer. This option does not fully meet the requirement.
Reference:
AWS Bedrock User Guide: Security and Networking (https://docs.aws.amazon.com/bedrock/latest/userguide/security.html) AWS Documentation: AWS PrivateLink (https://aws.amazon.com/privatelink/) AWS AI Practitioner Learning Path: Module on Security and Networking for AI/ML Services

質問 # 22
Which functionality does Amazon SageMaker Clarify provide?

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

正解：D

解説：
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 Reference:
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.

質問 # 23
A loan company is building a generative AI-based solution to offer new applicants discounts based on specific business criteria. The company wants to build and use an AI model responsibly to minimize bias that could negatively affect some customers.
Which actions should the company take to meet these requirements? (Select TWO.)

A. Detect imbalances or disparities in the data.
B. Ensure that the model runs frequently.
C. Evaluate the model's behavior so that the company can provide transparency to stakeholders.
D. Use the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) technique to ensure that the model is 100% accurate.
E. Ensure that the model's inference time is within the accepted limits.

正解：A、C

解説：
To build an AI model responsibly and minimize bias, it is essential to ensure fairness and transparency throughout the model development and deployment process. This involves detecting and mitigating data imbalances and thoroughly evaluating the model's behavior to understand its impact on different groups.
Option A (Correct): "Detect imbalances or disparities in the data": This is correct because identifying and addressing data imbalances or disparities is a critical step in reducing bias. AWS provides tools like Amazon SageMaker Clarify to detect bias during data preprocessing and model training.
Option C (Correct): "Evaluate the model's behavior so that the company can provide transparency to stakeholders": This is correct because evaluating the model's behavior for fairness and accuracy is key to ensuring that stakeholders understand how the model makes decisions. Transparency is a crucial aspect of responsible AI.
Option B: "Ensure that the model runs frequently" is incorrect because the frequency of model runs does not address bias.
Option D: "Use the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) technique to ensure that the model is 100% accurate" is incorrect because ROUGE is a metric for evaluating the quality of text summarization models, not for minimizing bias.
Option E: "Ensure that the model's inference time is within the accepted limits" is incorrect as it relates to performance, not bias reduction.
AWS AI Practitioner References:
Amazon SageMaker Clarify: AWS offers tools such as SageMaker Clarify for detecting bias in datasets and models, and for understanding model behavior to ensure fairness and transparency.
Responsible AI Practices: AWS promotes responsible AI by advocating for fairness, transparency, and inclusivity in model development and deployment.

質問 # 24
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?

A. Amazon EMR
B. Amazon Aurora PostgreSQL
C. Amazon Redshift
D. Amazon Athena

正解：B

解説：
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.
:
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/)

質問 # 25
Which option is a benefit of ongoing pre-training when fine-tuning a foundation model (FM)?

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

正解：D

解説：
Ongoing pre-training when fine-tuning a foundation model (FM) improves model performance over time by continuously learning from new data.
* Ongoing Pre-Training:
* Involves continuously training a model with new data to adapt to changing patterns, enhance generalization, and improve performance on specific tasks.
* Helps the model stay updated with the latest data trends and minimize drift over time.
* Why Option B is Correct:
* Performance Enhancement: Continuously updating the model with new data improves its accuracy and relevance.
* Adaptability: Ensures the model adapts to new data distributions or domain-specific nuances.
* Why Other Options are Incorrect:
* A. Decrease model complexity: Ongoing pre-training typically enhances complexity by learning new patterns, not reducing it.
* C. Decreases training time requirement: Ongoing pre-training may increase the time needed for training.
* D. Optimizes inference time: Does not directly affect inference time; rather, it affects model performance.

質問 # 26
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AIF-C01試験問題は、学習結果を検出するためのさまざまな自己学習および自己評価機能を備えたソフトウェアを提供します。統計レポート機能は、学生が弱点を見つけて対処するのに役立つように提供されています。当社のソフトウェアには、時間制限やシミュレートされたテスト機能など、多くの新しい機能も搭載されています。速度を調整してアラートを維持できるAIF-C01テストガイドでシミュレーションテストタイマーを設定したら、知識を習得するために心を傾けることができます。この関数がAIF-C01試験の合格に役立つことは間違いありません。

AIF-C01日本語サンプル: https://www.japancert.com/AIF-C01.html