Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 471:

  A company has developed a generative text summarization model by using Amazon Bedrock. The company will use Amazon Bedrock automatic model evaluation capabilities. Which metric should the company use to evaluate the accuracy of the model?

  A. Area Under the ROC Curve (AUC) score
  B. F1 score
  C. BERTScore
  D. Real world knowledge (RWK) score
  C. BERTScore

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  BERTScore is a metric specifically designed for evaluating the quality of text generated by models, particularly in tasks like text summarization and machine translation. It uses contextual embeddings from a pre-trained BERT model to compare generated text with reference text at the word level, making it well- suited for evaluating the accuracy of generative text summarization models. BERTScore assesses the semantic similarity between the generated text and the reference text, providing a more nuanced evaluation compared to traditional methods that focus on exact matches.

• Question 472:

  A company is building a custom AI solution in Amazon SageMaker Studio to analyze financial transactions for fraudulent activity in real time. The company needs to ensure that the connectivity from SageMaker Studio to Amazon Bedrock traverses the company's VPC.

  Which solution meets these requirements?

  A. Configure AWS Identity and Access Management (IAM) roles and policies for SageMaker Studio to access Amazon Bedrock.
  B. Configure Amazon Macie to proxy requests from SageMaker Studio to Amazon Bedrock.
  C. Configure AWS PrivateLink endpoints for the Amazon Bedrock API endpoints in the VPC that SageMaker Studio is connected to.
  D. Configure a new VPC for the Amazon Bedrock usage. Register the VPCs as peers.
  C. Configure AWS PrivateLink endpoints for the Amazon Bedrock API endpoints in the VPC that SageMaker Studio is connected to.

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  Explanation

  AWS PrivateLink enables private connectivity to AWS service APIs through VPC interface endpoints, ensuring SageMaker Studio traffic to the Amazon Bedrock API stays within the company's VPC and does not traverse the public internet.

• Question 473:

  A company is integrating AI into its employee recruitment and hiring solution. The company wants to mitigate bias risks and ensure responsible AI practices while prioritizing equitable hiring decisions. Which core dimensions of responsible AI should the company consider? (Choose two.)

  A. Fairness
  B. Tolerance
  C. Flexibility
  D. Open source
  E. Transparency
  A. Fairness
  E. Transparency

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  Fairness - Ensuring equitable outcomes across all demographic groups helps mitigate bias risks in hiring decisions. Transparency - Providing clear explanations of how the AI makes decisions builds trust and allows auditing for bias.

• Question 474:

  A financial institution is using Amazon Bedrock to develop an AI application. The application is hosted in a VPC. To meet regulatory compliance standards, the VPC is not allowed access to any internet traffic.

  Which AWS service or feature will meet these requirements?

  A. AWS PrivateLink
  B. Amazon Macie
  C. Amazon CloudFront
  D. Internet gateway
  A. AWS PrivateLink

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  AWS PrivateLink enables private connectivity between VPCs and AWS services without exposing traffic to the public internet. This feature is critical for meeting regulatory compliance standards that require isolation from public internet traffic.

  Option A (Correct): "AWS PrivateLink":This is the correct answer because it allows secure access to Amazon Bedrock and other AWS services from a VPC without internet access, ensuring compliance with regulatory standards.

  Option B:"Amazon Macie" is incorrect because it is a security service for data classification and protection, not for managing private network traffic.

  Option C:"Amazon CloudFront" is incorrect because it is a content delivery network service and does not provide private network connectivity.

  Option D:"Internet gateway" is incorrect as it enables internet access, which violates the VPC's no-internet-traffic policy.

  References:

  AWS PrivateLink Documentation:AWS highlights PrivateLink as a solution for connecting VPCs to AWS services privately, which is essential for organizations with strict regulatory requirements.

• Question 475:

  Which AI technique combines large language models (LLMs) with external knowledge bases to improve response accuracy?

  A. Reinforcement learning (RL)
  B. Natural language processing (NLP)
  C. Retrieval Augmented Generation (RAG)
  D. Transfer learning
  C. Retrieval Augmented Generation (RAG)

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  Explanation

  Retrieval Augmented Generation integrates large language models with external knowledge sources by retrieving relevant information at inference time and grounding the model's responses in that data, improving accuracy and factual reliability.

• Question 476:

  A company manually reviews all submitted resumes in PDF format. As the company grows, the company expects the volume of resumes to exceed the company's review capacity. The company needs an automated system to convert the PDF resumes into plain text format for additional processing. Which AWS service meets this requirement?

  A. Amazon Textract
  B. Amazon Personalize
  C. Amazon Lex
  D. Amazon Transcribe
  A. Amazon Textract

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  Amazon Textract is a service that automatically extracts text and data from scanned documents, including PDFs. It is the best choice for converting resumes from PDF format to plain text for further processing.

• Question 477:

  A company is evaluating a foundation model that summarizes long support cases into short case notes.

  Which metric is MOST relevant for this summarization task?

  A. BLEU
  B. ROUGE
  C. Mean squared error
  D. Average precision
  B. ROUGE

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  Explanation

  ROUGE is the correct answer because it is widely used to evaluate summarization quality by comparing generated summaries with reference summaries.

  Option B (Correct): "ROUGE": This is correct because it is intended for summarization evaluation.

  Option A: "BLEU" is incorrect because it is more commonly used for translation tasks.

  Option C: "Mean squared error" is incorrect because it is a regression metric.

  Option D: "Average precision" is incorrect because it is not the standard choice for text summarization evaluation.

• Question 478:

  A company wants to build an AI assistant to provide responses to user queries. The AI assistant must evaluate specific data sources, query external APIs, generate response options, and compare and prioritize response options. Which Amazon Bedrock feature or resource will meet these requirements?

  A. Prompt Management
  B. Response streaming
  C. Knowledge Bases
  D. Agents
  D. Agents

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  Amazon Bedrock Agents enable AI assistants to interact with specific data sources, query external APIs, generate and compare response options, and prioritize results, making them the ideal feature for building advanced, task-oriented chatbots with decision-making and orchestration capabilities.

• Question 479:

  An education company is building a chatbot whose target audience is teenagers. The company is training a custom large language model (LLM). The company wants the chatbot to speak in the target audience's language style by using creative spelling and shortened words. Which metric will assess the LLM's performance?

  A. F1 score
  B. BERTScore
  C. Recall-Oriented Understudy for Gisting Evaluation (ROUGE)
  D. Bilingual Evaluation Understudy (BLEU) score
  D. Bilingual Evaluation Understudy (BLEU) score

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  BLEU evaluates surface-level n-gram overlap between the LLM's outputs and reference examples--in this case, targets written in creative spelling and shorthand, making it well suited to measure how closely the model's style matches the teenager-oriented language.

• Question 480:

  A company is using Amazon SageMaker to deploy a model that identifies if social media posts contain certain topics. The company needs to show how different input features influence model behavior. Which SageMaker feature meets these requirements?

  A. SageMaker Canvas
  B. SageMaker Clarify
  C. SageMaker Feature Store
  D. SageMaker Ground Truth
  B. SageMaker Clarify

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  SageMaker Clarify provides tools for model interpretability, allowing you to visualize and understand how different input features influence the behavior and predictions of your deployed model.