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[General] AIF-C01 Trainingsmaterialien: AWS Certified AI Practitioner & AIF-C01 Lernmi

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【General】 AIF-C01 Trainingsmaterialien: AWS Certified AI Practitioner & AIF-C01 Lernmi

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Fast2test hat eine starke Gruppe, die aus IT-Eliten besteht. Sie verfolgen ständig die neuesten Informationen über die Schulungsunterlagen der Amazon AIF-C01 Zertifizierung mit ihren professionellen Perspektiven. Mit unseren Schulungsunterlagen zur Amazon AIF-C01 Zertifizierung können Sie die Amazon AIF-C01 Prüfung leichter bestehen, statt zu viel Zeit zu kosten. Nach dem Kauf unserer Produkte werden Sie einjährige Aktualisierung genießen.
Amazon AIF-C01 Prüfungsplan:
ThemaEinzelheiten
Thema 1
  • 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.
Thema 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.
Thema 3
  • 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.
Thema 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.
Thema 5
  • 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.

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Amazon AWS Certified AI Practitioner AIF-C01 Prüfungsfragen mit Lösungen (Q118-Q123):118. Frage
A company deployed a model to production. After 4 months, the model inference quality degraded. The company wants to receive a notification if the model inference quality degrades. The company also wants to ensure that the problem does not happen again.
Which solution will meet these requirements?
  • A. Build a new model. Monitor model drift by using Amazon SageMaker Feature Store.
  • B. Build a new model. Monitor model drift by using Amazon SageMaker JumpStart.
  • C. Retrain the model. Monitor model drift by using Amazon SageMaker Model Monitor.
  • D. Retrain the model. Monitor model drift by using Amazon SageMaker Clarify.
Antwort: C
Begründung:
The company needs to address the degradation in model inference quality after 4 months in production and prevent future occurrences by receiving notifications. Retraining the model can address the current degradation, likely caused by data drift (changes in the data distribution over time). Amazon SageMaker Model Monitor is designed to detect and monitor model drift, alerting the company when inference quality degrades, thus meeting both requirements.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"Amazon SageMaker Model Monitor enables you to monitor machine learning models in production for data drift, model performance degradation, and other quality issues. It can detect drift in feature distributions and inference quality, sending notifications when deviations are detected, allowing you to take corrective actions such as retraining the model." (Source: Amazon SageMaker Developer Guide, Monitoring Models with SageMaker Model Monitor) Detailed Explanation:
Option A: Retrain the model. Monitor model drift by using Amazon SageMaker Clarify.SageMaker Clarify is used for bias detection and explainability, not for monitoring model drift or inference quality in production.
This option does not fully meet the requirements.
Option B: Retrain the model. Monitor model drift by using Amazon SageMaker Model Monitor.This is the correct answer. Retraining addresses the current degradation, and SageMaker Model Monitor can detect future drift in inference quality, sending notifications to prevent recurrence, as required.
Option C: Build a new model. Monitor model drift by using Amazon SageMaker Feature Store.SageMaker Feature Store is for managing and sharing features, not for monitoring model drift or inference quality.
Building a new model may not be necessary if retraining can address the issue.
Option D: Build a new model. Monitor model drift by using Amazon SageMaker JumpStart.SageMaker JumpStart provides pre-trained models and solutions for quick deployment, but it does not offer specific tools for monitoring model drift or inference quality in production.
References:
Amazon SageMaker Developer Guide: Monitoring Models with SageMaker Model Monitor (https://docs.aws.
amazon.com/sagemaker/latest/dg/model-monitor.html)
AWS AI Practitioner Learning Path: Module on Model Monitoring and Maintenance AWS Documentation: Addressing Model Drift in Production (https://aws.amazon.com/sagemaker/)

119. Frage
A company wants to develop a solution that uses generative AI to create content for product advertisements, Including sample images and slogans.
Select the correct model type from the following list for each action. Each model type should be selected one time. (Select THREE.)
* Diffusion model
* Object detection model
* Transformer-based model

Antwort:
Begründung:

Reference:
Transformer-based models (such as GPT or Amazon Titan Text) are designed for generating and understanding natural language. These models can generate coherent, contextually relevant slogans based on product information.
Object detection models are designed to identify and locate objects within images, which makes them suitable for verifying that specific brand elements (like logos or products) are correctly positioned in the generated content.

120. Frage
A company wants to enhance response quality for a large language model (LLM) for complex problem-solving tasks. The tasks require detailed reasoning and a step-by-step explanation process.
Which prompt engineering technique meets these requirements?
  • A. Chain-of-thought prompting
  • B. Zero-shot prompting
  • C. Directional stimulus prompting
  • D. Few-shot prompting
Antwort: A
Begründung:
The company wants to enhance the response quality of an LLM for complex problem-solving tasks requiring detailed reasoning and step-by-step explanations. Chain-of-thought prompting encourages the LLM to break down the problem into intermediate steps, providing a clear reasoning process before arriving at the final answer, which is ideal for this requirement.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Chain-of-thought prompting improves the reasoning capabilities of large language models by encouraging them to break down complex tasks into intermediate steps, providing a step-by-step explanation that leads to the final answer. This technique is particularly effective for problem-solving tasks requiring detailed reasoning." (Source: AWS Bedrock User Guide, Prompt Engineering Techniques) Detailed Option A: Few-shot promptingFew-shot prompting provides a few examples to guide the LLM but does not explicitly encourage step-by-step reasoning or detailed explanations.
Option B: Zero-shot promptingZero-shot prompting relies on the LLM's pre-trained knowledge without examples, making it less effective for complex tasks requiring detailed reasoning.
Option C: Directional stimulus promptingDirectional stimulus prompting is not a standard technique in AWS documentation, likely a distractor, and does not address step-by-step reasoning.
Option D: Chain-of-thought promptingThis is the correct answer. Chain-of-thought prompting enhances response quality for complex tasks by guiding the LLM to reason step-by-step, providing detailed explanations.
Reference:
AWS Bedrock User Guide: Prompt Engineering Techniques (https://docs.aws.amazon.com/bedr ... pt-engineering.html) AWS AI Practitioner Learning Path: Module on Generative AI Prompting Amazon Bedrock Developer Guide: Advanced Prompting Strategies (https://aws.amazon.com/bedrock/) Below are the corrected and formatted questions based on the provided input, following the specified format. Each question is aligned with the main topics from the AWS AI Practitioner certification, and answers are provided with comprehensive explanations referencing official AWS documentation or study guides. Since the exact AWS AI Practitioner documents are not publicly available in full, I will rely on authoritative AWS documentation, whitepapers, and blogs available as of May 17, 2025, to ensure accuracy. If specific document excerpts are unavailable, I will use the most relevant AWS resources and clearly note the references.

121. Frage
Which feature of Amazon OpenSearch Service gives companies the ability to build vector database applications?
  • A. Scalable index management and nearest neighbor search capability
  • B. Integration with Amazon S3 for object storage
  • C. Ability to perform real-time analysis on streaming data
  • D. Support for geospatial indexing and queries
Antwort: A
Begründung:
Amazon OpenSearch Service (formerly Amazon Elasticsearch Service) has introduced capabilities to support vector search, which allows companies to build vector database applications. This is particularly useful in machine learning, where vector representations (embeddings) of data are often used to capture semantic meaning.
Scalable index management and nearest neighbor search capability are the core features enabling vector database functionalities in OpenSearch. The service allows users to index high-dimensional vectors and perform efficient nearest neighbor searches, which are crucial for tasks such as recommendation systems, anomaly detection, and semantic search.
Here is why option C is the correct answer:
Scalable Index Management: OpenSearch Service supports scalable indexing of vector data. This means you can index a large volume of high-dimensional vectors and manage these indexes in a cost-effective and performance-optimized way. The service leverages underlying AWS infrastructure to ensure that indexing scales seamlessly with data size.
Nearest Neighbor Search Capability: OpenSearch Service's nearest neighbor search capability allows for fast and efficient searches over vector data. This is essential for applications like product recommendation engines, where the system needs to quickly find the most similar items based on a user's query or behavior.
AWS AI Practitioner Reference:
According to AWS documentation, OpenSearch Service's support for nearest neighbor search using vector embeddings is a key feature for companies building machine learning applications that require similarity search.
The service uses Approximate Nearest Neighbors (ANN) algorithms to speed up searches over large datasets, ensuring high performance even with large-scale vector data.
The other options do not directly relate to building vector database applications:
A . Integration with Amazon S3 for object storage is about storing data objects, not vector-based searching or indexing.
B . Support for geospatial indexing and queries is related to location-based data, not vectors used in machine learning.
D . Ability to perform real-time analysis on streaming data relates to analyzing incoming data streams, which is different from the vector search capabilities.

122. Frage
A company wants to develop ML applications to improve business operations and efficiency.
Select the correct ML paradigm from the following list for each use case. Each ML paradigm should be selected one or more times. (Select FOUR.)
* Supervised learning
* Unsupervised learning

Antwort:
Begründung:

Reference:
AWS AI Practitioner Learning Path: Module on Machine Learning Strategies Amazon SageMaker Developer Guide: Supervised and Unsupervised Learning (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html) AWS Documentation: Introduction to Machine Learning Paradigms (https://aws.amazon.com/machine-learning/)

123. Frage
......
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