Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 271:

  HOTSPOT

  A company is designing a customer service chatbot by using a fine-tuned large language model (LLM). The company wants to ensure that the chatbot uses responsible AI characteristics.

  Select the correct responsible AI characteristic from the following list for each application design action. Each responsible AI characteristic should be selected one time or not at all.

  

  

  Explanation:

• Question 272:

  A medical company wants to modernize its onsite information processing application. The company wants to use generative AI to respond to medical questions from patients. Which AWS service should the company use to ensure responsible AI for the application?

  A. Guardrails for Amazon Bedrock
  B. Amazon Inspector
  C. Amazon Rekognition
  D. AWS Trusted Advisor
  A. Guardrails for Amazon Bedrock

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  Guardrails for Amazon Bedrock let you define and enforce safety, compliance, and bias controls on generative AI workloads. By embedding these guardrails into your application, you can ensure that patient- facing responses meet medical accuracy, privacy, and regulatory requirements without having to build custom monitoring or filtering pipelines yourself.

• Question 273:

  A company uses an open source pre-trained model to analyze user sentiment for a newly released product.

  Which action must the company perform, according to MLOps best practices?

  A. Use deep learning to perform hyperparameter tuning.
  B. Collect user reviews and label each review as positive or negative.
  C. Continuously monitor outputs in production.
  D. Perform feature engineering on the input dataset.
  C. Continuously monitor outputs in production.

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  Explanation

  MLOps best practices require continuous monitoring of model outputs in production to detect performance drift, bias, and degradation over time, ensuring the sentiment analysis model remains reliable and compliant after deployment.

• Question 274:

  A company acquires International Organization for Standardization (ISO) accreditation to manage AI risks and to use AI responsibly. What does this accreditation reflect about the company?

  A. All members of the company are ISO certified.
  B. All AI systems that the company uses are ISO certified.
  C. All AI application team members are ISO certified.
  D. The company's development framework is ISO certified.
  D. The company's development framework is ISO certified.

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  ISO accreditation for managing AI risks means that the company's development processes, controls, and frameworks for AI are certified to meet ISO standards. It does not certify individual employees or AI systems, but rather the organizational framework and practices.

• Question 275:

  A company is developing an ML model to predict heart disease risk. The model uses patient data, such as age, cholesterol, blood pressure, smoking status, and exercise habits. The dataset includes a target value that indicates whether a patient has heart disease. Which ML technique will meet these requirements?

  A. Unsupervised learning
  B. Supervised learning
  C. Reinforcement learning
  D. Semi-supervised learning
  B. Supervised learning

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  Supervised learning is used when the dataset includes both input features (like age, cholesterol, blood pressure, etc.) and a target value indicating the presence of heart disease. The model learns to predict the target value from labeled examples.

• Question 276:

  A company is using Retrieval Augmented Generation (RAG) with Amazon Bedrock and Stable Diffusion to generate product images based on text descriptions. The results are often random and lack specific details. The company wants to increase the specificity of the generated images. Which solution meets these requirements?

  A. Increase the number of generation steps.
  B. Use the MASK_IMAGE_BLACK mask source option.
  C. Increase the classi er-free guidance (CFG) scale.
  D. Increase the prompt strength.
  C. Increase the classi er-free guidance (CFG) scale.

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  In Stable Diffusion, the classifier-free guidance (CFG) scale parameter controls how closely the generated image adheres to the provided text prompt. By increasing the CFG scale, the model places more emphasis on the prompt, leading to images that more accurately reflect the specified details. However, it's important to balance this setting, as excessively high values can result in less diverse and potentially lower-quality images.

• Question 277:

  A company wants to improve a large language model (LLM) for content moderation within 3 months. The company wants the model to moderate content according to the company's values and ethics. The LLM must also be able to handle emerging trends and new types of problematic content.

  Which solution will meet these requirements?

  A. Conduct continuous pre-training on a large amount of text-based internet content.
  B. Create a high quality dataset of historical moderation decisions.
  C. Fine-tune the LLM on a diverse set of general ethical guidelines from various sources.
  D. Conduct reinforcement learning from human feedback (RLHF) by using real-time input from skilled moderators.
  D. Conduct reinforcement learning from human feedback (RLHF) by using real-time input from skilled moderators.

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  Explanation

  Reinforcement learning from human feedback allows the model to continuously learn from real-time input provided by skilled moderators, aligning it with company-specific values and ethics while adapting to emerging trends in content.

• Question 278:

  An ecommerce company wants to group customers based on their purchase history and preferences to personalize the user experience of the company's application. Which ML technique should the company use?

  A. Classification
  B. Clustering
  C. Regression
  D. Content generation
  B. Clustering

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  Clustering is an unsupervised learning technique that automatically groups data points - in this case, customers with similar purchase histories and preferences - into segments without needing predefined labels, enabling personalized experiences.

• Question 279:

  A company wants to improve the accuracy of the responses from a generative AI application. The application uses a foundation model (FM) on Amazon Bedrock. Which solution meets these requirements MOST cost-effectively?

  A. Fine-tune the FM.
  B. Retrain the FM.
  C. Train a new FM.
  D. Use prompt engineering.
  D. Use prompt engineering.

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  Using prompt engineering is the most cost-effective way to improve the accuracy of responses from a generative AI application without retraining or fine-tuning the foundation model (FM). Prompt engineering involves carefully designing the input prompts to guide the model toward producing better responses, improving relevance and accuracy.

• Question 280:

  Which strategy will evaluate the performance of a foundation model (FM) in real-world applications?

  A. Conducting A/B testing with users in a controlled environment
  B. Human evaluation by subject matter experts
  C. Measuring the model's accuracy on a training dataset
  D. Analysis of the model's internal representations and attention patterns
  A. Conducting A/B testing with users in a controlled environment

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  Explanation

  A/B testing with users in a controlled environment evaluates how the foundation model performs in an actual application setting by measuring user interactions and outcomes under realistic conditions.