Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 351:

  A company receives a large amount of unstructured user feedback in text format. The company wants to analyze the sentiment of the user feedback. Which solution will meet these requirements?

  A. Use a large language model (LLM) to perform natural language processing (NLP) for sentiment analysis.
  B. Use a regression algorithm to classify the feedback based on predefined categories. Then, analyze user sentiment.
  C. Use a recommendation engine algorithm to detect user sentiment.
  D. Use a time series algorithm to predict user sentiment based on past feedback.
  A. Use a large language model (LLM) to perform natural language processing (NLP) for sentiment analysis.

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  A large language model (LLM) can analyze unstructured text using NLP techniques to accurately determine the sentiment (positive, negative, or neutral) of user feedback. This approach is well-suited for handling large volumes of text data.

• Question 352:

  A company has thousands of unlabeled customer comments and wants to group comments that discuss similar issues.

  Which machine learning technique will meet this requirement?

  A. Supervised learning
  B. Unsupervised learning
  C. Reinforcement learning
  D. Transfer learning
  B. Unsupervised learning

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  Explanation

  Unsupervised learning is the correct answer because the data is unlabeled and the goal is to discover natural groupings in the comments. Clustering is a common unsupervised technique for this type of task.

  Option B (Correct): "Unsupervised learning": This is correct because it can identify patterns and group similar data without predefined labels.

  Option A: "Supervised learning" is incorrect because supervised learning requires labeled examples.

  Option C: "Reinforcement learning" is incorrect because it focuses on learning from rewards and actions over time.

  Option D: "Transfer learning" is incorrect because it is a model adaptation strategy, not the core technique for grouping unlabeled comments.

• Question 353:

  A grocery store wants to create a chatbot to help customers find products in the store. The chatbot must check the inventory in real time and provide the product location in the store. Which prompt engineering technique should the store use to build the chatbot?

  A. Zero-shot prompting
  B. Few-shot prompting
  C. Least-to-most prompting
  D. Reasoning and acting (ReAct) prompting
  D. Reasoning and acting (ReAct) prompting

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  ReAct prompting interleaves the model's chain-of-thought reasoning with explicit "actions" (such as calling your real-time inventory API), then uses the API response to inform its final answer. By structuring your prompts to have the LLM think, choose the "CheckInventory" action with the product name, receive the live location data, and then respond to the user, you seamlessly integrate real-time lookups and precise product locations into the chatbot's replies.

• Question 354:

  An education company waftion. The application will give users the ability to enter text or provide a picture of a question. The application will respond with a written answer and an explanation of the written answer. Which model type meets these requirements?

  A. Computer vision model
  B. Large multi-modal language model
  C. Diffusion model
  D. Text-to-speech model
  B. Large multi-modal language model

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  A large multi-modal language model can natively ingest both text and images as inputs and generate text outputs, making it ideal for a system that accepts typed questions or photos of questions and returns written answers with explanations.

• Question 355:

  A medical company is customizing a foundation model (FM) for diagnostic purposes. The company needs the model to be transparent and explainable to meet regulatory requirements. Which solution will meet these requirements?

  A. Configure the security and compliance by using Amazon Inspector.
  B. Generate simple metrics, reports, and examples by using Amazon SageMaker Clarify.
  C. Encrypt and secure training data by using Amazon Macie.
  D. Gather more data. Use Amazon Rekognition to add custom labels to the data.
  B. Generate simple metrics, reports, and examples by using Amazon SageMaker Clarify.

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  Amazon SageMaker Clarify provides transparency and explainability for machine learning models by generating metrics, reports, and examples that help to understand model predictions. For a medical company that needs a foundation model to be transparent and explainable to meet regulatory requirements, SageMaker Clarify is the most suitable solution.

• Question 356:

  Why does overfilting occur in ML models?

  A. The training dataset does not reptesent all possible input values.
  B. The model contains a regularization method.
  C. The model training stops early because of an early stopping criterion.
  D. The training dataset contains too many features.
  A. The training dataset does not reptesent all possible input values.

• Question 357:

  A company wants to use an ML model to analyze customer reviews on social media. The model must determine if each review has a neutral, positive, or negative sentiment. Which model evaluation strategy will meet these requirements?

  A. Open-ended generation
  B. Text summarization
  C. Machine translation
  D. Classification
  D. Classification

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  Classification is the evaluation strategy that assigns input data (such as customer reviews) to predefined categories - in this case, neutral, positive, or negative sentiment. This approach is standard for sentiment analysis tasks.

• Question 358:

  A company is building a chatbot. The chatbot uses a large language model (LLM) and answers customer questions about products. The company wants the chatbot to answer only product questions. The company does not want the chatbot to answer questions about other topics.

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

  A. Set guardrails on the LLM prompt template.
  B. Write custom application logic to identify questions about other topics.
  C. Reduce the information the LLM can access.
  D. Set the temperature parameter value to a lower number.
  A. Set guardrails on the LLM prompt template.

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  Explanation

  Setting guardrails on the prompt template is the least-overhead way to constrain the chatbot to product-related topics by defining allowed behavior and preventing responses outside the intended scope.

• Question 359:

  A documentary filmmaker wants to reach more viewers. The filmmaker wants to automatically add subtitles and voice-overs in multiple languages to their films. Which combination of steps will meet these requirements? (Choose two.)

  A. Use Amazon Transcribe and Amazon Translate to generate subtitles in other languages.
  B. Use Amazon Textract and Amazon Translate to generate subtitles in other languages.
  C. Use Amazon Polly to generate voice-overs in other languages.
  D. Use Amazon Translate to generate voice-overs in other languages.
  E. Use Amazon Textract to generate voice-overs in other languages.
  A. Use Amazon Transcribe and Amazon Translate to generate subtitles in other languages.
  C. Use Amazon Polly to generate voice-overs in other languages.

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  Use Amazon Transcribe and Amazon Translate to generate subtitles in other languages: Amazon Transcribe converts spoken dialogue to text (subtitles), and Amazon Translate can then translate these subtitles into multiple languages. Use Amazon Polly to generate voice-overs in other languages: Amazon Polly converts translated text into lifelike speech, enabling the creation of multilingual voice-overs.

• Question 360:

  An AI practitioner is developing a prompt for large language models (LLMs) in Amazon Bedrock. The AI practitioner must ensure that the prompt works across all Amazon Bedrock LLMs.

  Which characteristic can differ across the LLMs?

  A. Maximum token count
  B. On-demand inference parameter support
  C. The ability to control model output randomness
  D. Compatibility with Amazon Bedrock Guardrails
  A. Maximum token count

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  Explanation

  Different large language models have varying maximum token limits, which affects how much input and output text a prompt can include, so prompts must account for these differences to work consistently across models.