Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 451:

  HOTSPOT

  A company is developing an AI application to help the company approve or deny personal loans. The application must follow the principles of responsible AI.

  Select the correct responsible AI principle from the following list for each action. Select each responsible AI principle one time or not at all.

  

  

  Explanation:

  Encrypt the application data, and isolate the application on a private network -> Privacy and security Evaluate how different population groups will be impacted -> Fairness Test the application with unexpected data to ensure the application will work in unique situations -> Robustness

  Protecting data and networks is part of privacy and security.

  Assessing impacts on different groups ensures fairness.

  Testing with unexpected data checks the application's robustness.

• Question 452:

  A media streaming platform wants to provide movie recommendations to users based on the users' account history. Which AWS service meets these requirements?

  A. Amazon Polly
  B. Amazon Comprehend
  C. Amazon Transcribe
  D. Amazon Personalize
  D. Amazon Personalize

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  Amazon Personalize is an AWS service designed to deliver personalized recommendations, such as movie suggestions, to users based on their account history and preferences.

• Question 453:

  A retail company wants to build an ML model to recommend products to customers. The company wants to build the model based on responsible practices. Which practice should the company apply when collecting data to decrease model bias?

  A. Use data from only customers who match the demography of the company's overall customer base.
  B. Collect data from customers who have a past purchase history.
  C. Ensure that the data is balanced and collected from a diverse group.
  D. Ensure that the data is from a publicly available dataset.
  C. Ensure that the data is balanced and collected from a diverse group.

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  By gathering training data that reflects the full spectrum of your customer population - across demographics, behaviors, and preferences - you reduce skew toward any one subgroup and help the recommender treat all users equitably.

• Question 454:

  A company wants to implement a large language model (LLM) based chatbot to provide customer service agents with real-time contextual responses to customers' inquiries. The company will use the company's policies as the knowledge base. Which solution will meet these requirements MOST cost-effectively?

  A. Retrain the LLM on the company policy data.
  B. Fine-tune the LLM on the company policy data.
  C. Implement Retrieval Augmented Generation (RAG) for in-context responses.
  D. Use pre-training and data augmentation on the company policy data.
  C. Implement Retrieval Augmented Generation (RAG) for in-context responses.

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  Retrieval Augmented Generation (RAG) integrates external data sources with LLMs to produce accurate and contextually relevant outputs without the need for extensive retraining. By connecting the chatbot to the company's policy documents, RAG enables the model to retrieve pertinent information in real-time, ensuring responses are both accurate and up-to-date. This approach is cost-effective as it leverages existing data without the computational expenses associated with retraining or fine-tuning large models.

• Question 455:

  A company wants its AI models to be transparent and explainable.

  Which combination of Amazon SageMaker AI features will meet these requirements? (Choose two.)

  A. SageMaker Model Cards
  B. SageMaker Pipelines
  C. SageMaker Clarity
  D. SageMaker Model Monitor
  E. SageMaker Debugger
  A. SageMaker Model Cards
  C. SageMaker Clarity

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  Explanation

  SageMaker Model Cards provide standardized documentation that describes a model's intended use, training data, performance, and limitations, supporting transparency. SageMaker Clarity helps explain model behavior by detecting bias and providing feature attribution, making model decisions more interpretable and explainable.

• Question 456:

  A company built an AI-powered resume screening system. The company used a large dataset to train the model. The dataset contained resumes that were not representative of all demographics. Which core dimension of responsible AI does this scenario present?

  A. Fairness.
  B. Explainability.
  C. Privacy and security.
  D. Transparency.
  A. Fairness.

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  Fairness refers to the absence of bias in AI models. Using non- representative datasets leads to biased predictions, affecting specific demographics unfairly. Explainability, privacy, and transparency are important but not directly related to this scenario.

  References:

  AWS Responsible AI Framework.

• Question 457:

  A company has implemented a large language model (LLM) solution by using a pre-trained model. The company needs to ensure that the model's responses are transparent and accurate. The company wants to ground the model's responses in factual information from the company's authoritative data sources.

  Which technique should the company use to meet these requirements?

  A. Prompt engineering
  B. Reinforcement learning
  C. Retrieval Augmented Generation (RAG)
  D. Static knowledge base
  C. Retrieval Augmented Generation (RAG)

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  Explanation

  Retrieval Augmented Generation (RAG) grounds the model's responses in authoritative company data by retrieving relevant information at inference time, which improves factual accuracy and makes responses more transparent and trustworthy.

• Question 458:

  A company plans to build an AI model for the company's global customer base. The company wants to train the model on a dataset that reflects user diversity.

  Which action will meet this requirement?

  A. Balance class representation in the dataset.
  B. Use a regional dataset with complete data.
  C. Oversample majority class data.
  D. Drop minority class data records.
  A. Balance class representation in the dataset.

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  Explanation

  Balancing class representation ensures that all groups in the dataset are adequately represented, which helps the model learn patterns across diverse users and reduces bias.

• Question 459:

  A company wants to fine-tune a foundation model (FM) to answer questions for a specific domain. The company wants to use instruction-based fine-tuning. How should the company prepare the training data?

  A. Gather company internal documents and industry-specific materials. Merge the documents and materials into a single file.
  B. Collect external company reviews from various online sources. Manually label each review as either positive or negative.
  C. Create pairs of questions and answers that specifically address topics related to the company's industry domain.
  D. Create few-shot prompts to instruct the model to answer only domain knowledge.
  C. Create pairs of questions and answers that specifically address topics related to the company's industry domain.

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  Instruction-based fine-tuning requires a dataset of instructionesponse examples. By curating question?answer pairs focused on the company's domain, you teach the model exactly how to interpret domain- specific queries and generate the correct responses during inference.

• Question 460:

  Which option is a disadvantage of using generative AI models in production systems?

  A. Possible high accuracy and reliability
  B. Deterministic and consistent behavior
  C. Negligible computational resource requirements
  D. Hallucinations and inaccuracies
  D. Hallucinations and inaccuracies

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  Explanation

  Generative AI models can produce hallucinations and inaccurate outputs, which introduces reliability and trust risks when deployed in production systems.