AWS Certified AI Practitioner AIF-C01 Practice Test

AWS Certified AI Practitioner AIF-C01 Practice Test practice questions, page 8 of 20, with answer review and explanations.

Question 36

A law firm wants to build an AI application by using large language models (LLMs). The application will read legal documents and extract key points from the documents. Which solution meets these requirements?

  1. Build an automatic named entity recognition system.
  2. Create a recommendation engine.
  3. Develop a summarization chatbot.
  4. Develop a multi-language translation system.

Question 37

A company wants to classify human genes into 20 categories based on gene characteristics. The company needs an ML algorithm to document how the inner mechanism of the model affects the output. Which ML algorithm meets these requirements?

  1. Decision trees
  2. Linear regression
  3. Logistic regression
  4. Neural networks

Question 38

A company has built an image classification model to predict plant diseases from photos of plant leaves. The company wants to evaluate how many images the model classified correctly. Which evaluation metric should the company use to measure the model's performance?

  1. R-squared score
  2. Accuracy
  3. Root mean squared error (RMSE)
  4. Learning rate

Question 39

A company is using a pre-trained large language model (LLM) to build a chatbot for product recommendations. The company needs the LLM outputs to be short and written in a specific language. Which solution will align the LLM response quality with the company's expectations?

  1. Adjust the prompt.
  2. Choose an LLM of a different size.
  3. Increase the temperature.
  4. Increase the Top K value.

Question 40

A company uses Amazon SageMaker for its ML pipeline in a production environment. The company has large input data sizes up to 1 GB and processing times up to 1 hour. The company needs near real-time latency. Which SageMaker inference option meets these requirements?

  1. Real-time inference
  2. Serverless inference
  3. Asynchronous inference
  4. Batch transform