AWS Certified AI Practitioner AIF-C01 Practice Test

AWS Certified AI Practitioner AIF-C01 Practice Test practice questions, page 16 of 20, with answer review and explanations.

Question 76

A company is building a large language model (LLM) question answering chatbot. The company wants to decrease the number of actions call center employees need to take to respond to customer questions. Which business objective should the company use to evaluate the effect of the LLM chatbot?

  1. Website engagement rate
  2. Average call duration
  3. Corporate social responsibility
  4. Regulatory compliance

Question 77

Which functionality does Amazon SageMaker Clarify provide?

  1. Integrates a Retrieval Augmented Generation (RAG) workflow
  2. Monitors the quality of ML models in production
  3. Documents critical details about ML models
  4. Identifies potential bias during data preparation

Question 78

A company is developing a new model to predict the prices of specific items. The model performed well on the training dataset. When the company deployed the model to production, the model's performance decreased significantly. What should the company do to mitigate this problem?

  1. Reduce the volume of data that is used in training.
  2. Add hyperparameters to the model.
  3. Increase the volume of data that is used in training.
  4. Increase the model training time.

Question 79

An ecommerce company wants to build a solution to determine customer sentiments based on written customer reviews of products. Which AWS services meet these requirements? (Choose two.)

  1. Amazon Lex
  2. Amazon Comprehend
  3. Amazon Polly
  4. Amazon Bedrock
  5. Amazon Rekognition

Question 80

A company wants to use large language models (LLMs) with Amazon Bedrock to develop a chat interface for the company's product manuals. The manuals are stored as PDF files. Which solution meets these requirements MOST cost-effectively?

  1. Use prompt engineering to add one PDF file as context to the user prompt when the prompt is submitted to Amazon Bedrock.
  2. Use prompt engineering to add all the PDF files as context to the user prompt when the prompt is submitted to Amazon Bedrock.
  3. Use all the PDF documents to fine-tune a model with Amazon Bedrock. Use the fine-tuned model to process user prompts.
  4. Upload PDF documents to an Amazon Bedrock knowledge base. Use the knowledge base to provide context when users submit prompts to Amazon Bedrock.