AIF-C01 Quizfragen Und Antworten & AIF-C01 Prüfungs

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Amazon AIF-C01 Prüfungsplan:

ThemaEinzelheiten
Thema 1
  • 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.
Thema 2
  • 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 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
  • 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.

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Amazon AWS Certified AI Practitioner AIF-C01 Prüfungsfragen mit Lösungen (Q118-Q123):

118. Frage
An AI practitioner is determining the appropriate data type for various use cases.
Select the correct data type from the following list for each use case. Select each data type one time.

Antwort:

Begründung:

Explanation:
Sentiment analysis # Text data
Traffic sign recognition # Image data
Customer demographics & purchase history # Tabular data
Stock price forecasting # Time series data
AWS classifies NLP tasks like sentiment analysis under text data
Computer vision tasks such as object and sign recognition use image data Structured rows and columns (demographics, transactions) are tabular data Sequential data indexed by time (prices, metrics) is time series data


119. Frage
A company is building an ML model to analyze archived data. The company must perform inference on large datasets that are multiple GBs in size. The company does not need to access the model predictions immediately.
Which Amazon SageMaker inference option will meet these requirements?

Antwort: D

Begründung:
Batch transform in Amazon SageMaker is designed for offline processing of large datasets. It is ideal for scenarios where immediate predictions are not required, and the inference can be done on large datasets that are multiple gigabytes in size. This method processes data in batches, making it suitable for analyzing archived data without the need for real-time access to predictions.
* Option A (Correct): "Batch transform": This is the correct answer because batch transform is optimized for handling large datasets and is suitable when immediate access to predictions is not required.
* Option B: "Real-time inference" is incorrect because it is used for low-latency, real-time prediction needs, which is not required in this case.
* Option C: "Serverless inference" is incorrect because it is designed for small-scale, intermittent inference requests, not for large batch processing.
* Option D: "Asynchronous inference" is incorrect because it is used when immediate predictions are required, but with high throughput, whereas batch transform is more suitable for very large datasets.
AWS AI Practitioner References:
* Batch Transform on AWS SageMaker: AWS recommends using batch transform for large datasets when real-time processing is not needed, ensuring cost-effectiveness and scalability.


120. Frage
A company is using domain-specific models. The company wants to avoid creating new models from the beginning. The company instead wants to adapt pre-trained models to create models for new, related tasks.
Which ML strategy meets these requirements?

Antwort: D


121. 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:


122. Frage
A company wants to detect unauthorized API actions for foundation models (FMs) in Amazon Bedrock.
Which solution meets these requirements?

Antwort: B

Begründung:
AWS CloudTrail provides auditing and monitoring capabilities by recording API activity across AWS services, including Amazon Bedrock.
CloudTrail events can include details such as the requesting identity, API operation, request timestamp, source IP address, and relevant error information.
These records help security teams investigate unauthorized or suspicious attempts to access Amazon Bedrock resources.
CloudTrail captures Bedrock management events, while certain data events require additional logging configuration. Organizations can analyze these events and integrate monitoring workflows with other AWS security services.
AWS Trusted Advisor provides infrastructure optimization and security recommendations but does not directly audit Bedrock model inputs and outputs.
Amazon QuickSight provides business intelligence capabilities, while Amazon Personalize generates personalized recommendations.
Therefore, AWS CloudTrail is the appropriate service for monitoring API activity and investigating unauthorized access attempts involving foundation models.


123. Frage
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