AWS Certified AI Practitioner AIF-C01 Practice Test

AWS Certified AI Practitioner AIF-C01 Practice Test practice questions, page 9 of 20, with answer review and explanations.

Question 41

A company is using domain-specific models. The company wants to avoid creating new models from the beginning. The company instead wants to adapt pre-trained models to create models for new, related tasks. Which ML strategy meets these requirements?

  1. Increase the number of epochs.
  2. Use transfer learning.
  3. Decrease the number of epochs.
  4. Use unsupervised learning.

Question 42

A company is building a solution to generate images for protective eyewear. The solution must have high accuracy and must minimize the risk of incorrect annotations. Which solution will meet these requirements?

  1. Human-in-the-loop validation by using Amazon SageMaker Ground Truth Plus
  2. Data augmentation by using an Amazon Bedrock knowledge base
  3. Image recognition by using Amazon Rekognition
  4. Data summarization by using Amazon QuickSight Q

Question 43

A company wants to create a chatbot by using a foundation model (FM) on Amazon Bedrock. The FM needs to access encrypted data that is stored in an Amazon S3 bucket. The data is encrypted with Amazon S3 managed keys (SSE-S3). The FM encounters a failure when attempting to access the S3 bucket data. Which solution will meet these requirements?

  1. Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key.
  2. Set the access permissions for the S3 buckets to allow public access to enable access over the internet.
  3. Use prompt engineering techniques to tell the model to look for information in Amazon S3.
  4. Ensure that the S3 data does not contain sensitive information.

Question 44

A company wants to use language models to create an application for inference on edge devices. The inference must have the lowest latency possible. Which solution will meet these requirements?

  1. Deploy optimized small language models (SLMs) on edge devices.
  2. Deploy optimized large language models (LLMs) on edge devices.
  3. Incorporate a centralized small language model (SLM) API for asynchronous communication with edge devices.
  4. Incorporate a centralized large language model (LLM) API for asynchronous communication with edge devices.

Question 45

A company wants to build an ML model by using Amazon SageMaker. The company needs to share and manage variables for model development across multiple teams. Which SageMaker feature meets these requirements?

  1. Amazon SageMaker Feature Store
  2. Amazon SageMaker Data Wrangler
  3. Amazon SageMaker Clarify
  4. Amazon SageMaker Model Cards