AWS Certified AI Practitioner AIF-C01 Practice Test

AWS Certified AI Practitioner AIF-C01 Practice Test practice questions, page 18 of 20, with answer review and explanations.

Question 86

A student at a university is copying content from generative AI to write essays. Which challenge of responsible generative AI does this scenario represent?

  1. Toxicity
  2. Hallucinations
  3. Plagiarism
  4. Privacy

Question 87

A company needs to build its own large language model (LLM) based on only the company's private data. The company is concerned about the environmental effect of the training process. Which Amazon EC2 instance type has the LEAST environmental effect when training LLMs?

  1. Amazon EC2 C series
  2. Amazon EC2 G series
  3. Amazon EC2 P series
  4. Amazon EC2 Trn series

Question 88

A company wants to build an interactive application for children that generates new stories based on classic stories. The company wants to use Amazon Bedrock and needs to ensure that the results and topics are appropriate for children. Which AWS service or feature will meet these requirements?

  1. Amazon Rekognition
  2. Amazon Bedrock playgrounds
  3. Guardrails for Amazon Bedrock
  4. Agents for Amazon Bedrock

Question 89

A company is building an application that needs to generate synthetic data that is based on existing data. Which type of model can the company use to meet this requirement?

  1. Generative adversarial network (GAN)
  2. XGBoost
  3. Residual neural network
  4. WaveNet

Question 90

A digital devices company wants to predict customer demand for memory hardware. The company does not have coding experience or knowledge of ML algorithms and needs to develop a data-driven predictive model. The company needs to perform analysis on internal data and external data. Which solution will meet these requirements?

  1. Store the data in Amazon S3. Create ML models and demand forecast predictions by using Amazon SageMaker built-in algorithms that use the data from Amazon S3.
  2. Import the data into Amazon SageMaker Data Wrangler. Create ML models and demand forecast predictions by using SageMaker built-in algorithms.
  3. Import the data into Amazon SageMaker Data Wrangler. Build ML models and demand forecast predictions by using an Amazon Personalize Trending-Now recipe.
  4. Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas.