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[General] AIF-C01 Latest Exam Labs & AIF-C01 Exam Test

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【General】 AIF-C01 Latest Exam Labs & AIF-C01 Exam Test

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Amazon AIF-C01 Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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.
Topic 2
  • 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.
Topic 3
  • 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.
Topic 4
  • 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.
Topic 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 Sample Questions (Q220-Q225):NEW QUESTION # 220
A publishing company built a Retrieval Augmented Generation (RAG) based solution to give its users the ability to interact with published content. New content is published daily. The company wants to provide a near real-time experience to users.
Which steps in the RAG pipeline should the company implement by using offline batch processing to meet these requirements? (Select TWO.)
  • A. Generation of content embeddings
  • B. Response generation for the user
  • C. Creation of the search index
  • D. Retrieval of relevant content
  • E. Generation of embeddings for user queries
Answer: A,C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In a RAG (Retrieval Augmented Generation) architecture, there are steps that can be optimized using offline batch processing, particularly for operations that do not require real-time updates:
* A. Generation of content embeddings:When new content is published, it can be processed in batches to generate embeddings (vector representations) offline. These embeddings are then used at query time for similarity search. As new documents come in daily, batch processing is ideal for generating embeddings for all new content together.
"Content/document embeddings are typically generated offline, as this operation can be computationally expensive and does not need to happen in real-time." (Reference: AWS GenAI RAG Blog, Amazon Bedrock RAG Pattern)
* C. Creation of the search index:After generating the content embeddings, these are indexed in a vector database or search service. This indexing is also typically performed in batch as part of the offline pipeline.
"Building or updating the vector index is often performed as a batch operation, reflecting the latest state of the content repository." (Reference: AWS RAG Pattern Whitepaper) B, D, and E are real-time steps. Embeddings for user queries (B), retrieval of relevant content (D), and response generation (E) must be processed in real-time to provide an interactive experience.
References:
Retrieval Augmented Generation (RAG) on AWS
Amazon Bedrock RAG Documentation

NEW QUESTION # 221
A company uses Amazon SageMaker for its ML pipeline in a production environment. The company has large input data sizes up to 1 GB and processing times up to 1 hour. The company needs near real-time latency.
Which SageMaker inference option meets these requirements?
  • A. Asynchronous inference
  • B. Real-time inference
  • C. Batch transform
  • D. Serverless inference
Answer: B

NEW QUESTION # 222
Which term is an example of output vulnerability?
  • A. Parameter stealing
  • B. Data leakage
  • C. Model misuse
  • D. Data poisoning
Answer: B

NEW QUESTION # 223
A company wants to create a chatbot by using a foundation model (FM) on Amazon Bedrock. The FM needs to access encrypted data that is stored in an Amazon S3 bucket.
The data is encrypted with Amazon S3 managed keys (SSE-S3).
The FM encounters a failure when attempting to access the S3 bucket data.
Which solution will meet these requirements?
  • A. Ensure that the S3 data does not contain sensitive information.
  • B. Use prompt engineering techniques to tell the model to look for information in Amazon S3.
  • C. Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key.
  • D. Set the access permissions for the S3 buckets to allow public access to enable access over the internet.
Answer: C
Explanation:
Amazon Bedrock needs the appropriate IAM role with permission to access and decrypt data stored in Amazon S3. If the data is encrypted with Amazon S3 managed keys (SSE-S3), the role that Amazon Bedrock assumes must have the required permissions to access and decrypt the encrypted data.
Option A (Correct): "Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key": This is the correct solution as it ensures that the AI model can access the encrypted data securely without changing the encryption settings or compromising data security.
Option B: "Set the access permissions for the S3 buckets to allow public access" is incorrect because it violates security best practices by exposing sensitive data to the public.
Option C: "Use prompt engineering techniques to tell the model to look for information in Amazon S3" is incorrect as it does not address the encryption and permission issue.
Option D: "Ensure that the S3 data does not contain sensitive information" is incorrect because it does not solve the access problem related to encryption.
AWS AI Practitioner Reference:
Managing Access to Encrypted Data in AWS: AWS recommends using proper IAM roles and policies to control access to encrypted data stored in S3.

NEW QUESTION # 224
A company uses a foundation model (FM) from Amazon Bedrock for an AI search tool. The company wants to fine-tune the model to be more accurate by using the company's data.
Which strategy will successfully fine-tune the model?
  • A. Provide labeled data with the prompt field and the completion field.
  • B. Purchase Provisioned Throughput for Amazon Bedrock.
  • C. Train the model on journals and textbooks.
  • D. Prepare the training dataset by creating a .txt file that contains multiple lines in .csv format.
Answer: A

NEW QUESTION # 225
......
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Posted at 16 hour before        Only Author  2#
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