Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 261:

  A social media company wants to prevent users from posting discriminatory content on the company's application. The company wants to use Amazon Bedrock as part of the solution. How can the company use Amazon Bedrock to meet these requirements?

  A. Give users the ability to interact based on user preferences.
  B. Block interactions related to predefined topics.
  C. Restrict user conversations to predefined topics.
  D. Provide a variety of responses to select from for user engagement.
  B. Block interactions related to predefined topics.

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  Amazon Bedrock's built-in content moderation lets you define categories or keywords (e.g., hate speech, discriminatory language) and automatically block or filter any user inputs or outputs that fall under those topics. By configuring Bedrock's safety filters with your predefined "disallowed" categories, the application will prevent discriminatory content from ever being posted.

• Question 262:

  A company is using AI to build a toy recommendation website that suggests toys based on a customer's interests and age. The company notices that the AI tends to suggest stereotypically gendered toys.

  Which AWS service or feature should the company use to investigate the bias?

  A. Amazon Rekognition
  B. Amazon Q Developer
  C. Amazon Comprehend
  D. Amazon SageMaker Clarify
  D. Amazon SageMaker Clarify

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  Explanation

  Amazon SageMaker Clarify is designed to detect, measure, and analyze bias in datasets and machine learning models, enabling investigation of biased outcomes such as stereotypical recommendations.

• Question 263:

  A company wants to build a question-answering application over internal policy documents. The company wants the application to retrieve only relevant passages for each question instead of placing all documents directly into every prompt.

  Which approach is the MOST cost-effective?

  A. Add every policy document to each prompt
  B. Retrain a foundation model from scratch on the policy documents
  C. Use Retrieval Augmented Generation (RAG)
  D. Increase the maximum output token length
  C. Use Retrieval Augmented Generation (RAG)

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  Explanation

  Retrieval Augmented Generation (RAG) is the correct answer because it retrieves relevant context from the document set at query time instead of sending all documents with every request. This reduces unnecessary token usage and improves relevance.

  Option C (Correct): "Use Retrieval Augmented Generation (RAG)": This is correct because RAG combines retrieval with generation to provide targeted context efficiently.

  Option A: "Add every policy document to each prompt" is incorrect because it is inefficient and increases token consumption.

  Option B: "Retrain a foundation model from scratch" is incorrect because it has much higher cost and operational effort.

  Option D: "Increase the maximum output token length" is incorrect because output length does not solve document retrieval efficiency.

• Question 264:

  A company wants to increase employee productivity by using a generative AI solution to write code to test software applications.

  Which solution will meet these requirements with the LEAST operational effort?

  A. Amazon Q Business
  B. Amazon Bedrock Agents
  C. Amazon Q Developer
  D. Amazon SageMaker Clarify
  C. Amazon Q Developer

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  Explanation

  Amazon Q Developer is a managed generative AI service specifically designed to assist developers by automatically generating code, tests, and software-related content with minimal setup and operational overhead.

• Question 265:

  A media company wants to detect whether customer reviews contain inappropriate or harmful language before storing them. Which AWS solution should the company use?

  A. Amazon Rekognition
  B. Amazon Bedrock Guardrails
  C. Amazon Translate
  D. AWS WAF
  B. Amazon Bedrock Guardrails

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  Bedrock Guardrails include content moderation capabilities that classify and block harmful text, enabling safe handling of user-generated content before storage.

• Question 266:

  A company deployed an AI/ML solution to help customer service agents respond to frequently asked questions. The questions can change over time. The company wants to give customer service agents the ability to ask questions and receive automatically generated answers to common customer questions. Which strategy will meet these requirements MOST cost-effectively?

  A. Fine-tune the model regularly.
  B. Train the model by using context data.
  C. Pre-train and benchmark the model by using context data.
  D. Use Retrieval Augmented Generation (RAG) with prompt engineering techniques.
  D. Use Retrieval Augmented Generation (RAG) with prompt engineering techniques.

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  RAG combines large pre-trained models with retrieval mechanisms to fetch relevant context from a knowledge base. This approach is cost-effective as it eliminates the need for frequent model retraining while ensuring responses are contextually accurate and up to date.

  References:

  AWS RAG Techniques.

• Question 267:

  A hospital wants to use a generative AI solution with speech-to-text functionality to help improve employee skills in dictating clinical notes. Which AWS service meets these requirements?

  A. Amazon Q Developer
  B. Amazon Polly
  C. Amazon Rekognition
  D. AWS HealthScribe
  D. AWS HealthScribe

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  AWS HealthScribe is specifically designed for healthcare workflows, providing accurate speech-to-text transcription of clinical conversations and generating structured clinical notes, meeting the hospital's need to improve employee dictation of clinical documentation.

• Question 268:

  A company wants to build an ML model to detect abnormal patterns in sensor data. The company does not have labeled data for training. Which ML method will meet these requirements?

  A. Linear regression
  B. Classification
  C. Decision tree
  D. Autoencoders
  D. Autoencoders

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  Autoencoders are a type of unsupervised learning method used to detect abnormal patterns (anomalies) in sensor data when labeled data is not available. They learn to reconstruct normal patterns, making deviations (anomalies) easily detectable.

• Question 269:

  A healthcare company wants to use AI to analyze clinical reports. The company must ensure sensitive patient data is never sent to external model providers. Which statement describes how Amazon Bedrock addresses this concern?

  A. Bedrock encrypts data but shares summaries with model providers.
  B. Bedrock never shares customer prompts or outputs with model providers.
  C. Bedrock stores prompts in Amazon S3 for vendor audits.
  D. Bedrock transmits anonymized inputs to model creators for optimization.
  B. Bedrock never shares customer prompts or outputs with model providers.

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  Amazon Bedrock ensures data privacy by keeping all inputs and outputs within AWS. No customer content is shared with third-party model providers, enabling safe use of sensitive clinical data in compliance-sensitive environments.

• Question 270:

  A company wants to use a pre-trained foundation model for a specialized legal document task. The model performs poorly because it does not understand many legal terms used by the company.

  Which action is MOST appropriate to improve performance?

  A. Reduce the number of output tokens
  B. Use domain adaptation fine-tuning
  C. Decrease the storage class of the documents
  D. Replace embeddings with raw text only
  B. Use domain adaptation fine-tuning

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  Explanation

  Domain adaptation fine-tuning is the correct answer because it helps adapt a pre-trained model to the vocabulary and patterns of a specialized domain such as legal language.

  Option B (Correct): "Use domain adaptation fine-tuning": This is correct because it improves domain-specific understanding.

  Option A: "Reduce the number of output tokens" is incorrect because it does not address legal terminology comprehension.

  Option C: "Decrease the storage class of the documents" is incorrect because storage class does not improve model performance.

  Option D: "Replace embeddings with raw text only" is incorrect because that does not solve the domain adaptation problem.