Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 301:

  HOTSPOT

  Select and order the steps from the following list to correctly describe the ML lifecycle for a new custom model. Select each step one time.

  

  

  Explanation:

• Question 302:

  An online media streaming company wants to give its customers the ability to perform natural language- based image search and filtering. The company needs a vector database that can help with similarity searches and nearest neighbor queries. Which AWS service meets these requirements?

  A. Amazon Comprehend
  B. Amazon Personalize
  C. Amazon Polly
  D. Amazon OpenSearch Service
  D. Amazon OpenSearch Service

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  Amazon OpenSearch Service supports vector search capabilities, allowing you to perform similarity searches and nearest neighbor queries - key requirements for natural language-based image search and filtering.

• Question 303:

  A company wants to learn about generative AI applications in an experimental environment. Which solution will meet this requirement MOST cost-effectively?

  A. Amazon Q Developer
  B. Amazon SageMaker JumpStart
  C. Amazon Bedrock PartyRock
  D. Amazon Q Business
  C. Amazon Bedrock PartyRock

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  Amazon Bedrock PartyRock provides a highly cost-effective and experimental environment for learning about generative AI applications, allowing users to quickly build, test, and iterate on AI apps without incurring significant costs.

• Question 304:

  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?

  A. Deploy optimized small language models (SLMs) on edge devices.
  B. Deploy optimized large language models (LLMs) on edge devices.
  C. Incorporate a centralized small language model (SLM) API for asynchronous communication with edge devices.
  D. Incorporate a centralized large language model (LLM) API for asynchronous communication with edge devices.
  A. Deploy optimized small language models (SLMs) on edge devices.

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  To achieve the lowest latency possible for inference on edge devices, deploying optimized small language models (SLMs) is the most effective solution. SLMs require fewer resources and have faster inference times, making them ideal for deployment on edge devices where processing power and memory are limited.

  Option A (Correct): "Deploy optimized small language models (SLMs) on edge devices":This is the correct answer because SLMs provide fast inference with low latency, which is crucial for edge deployments.

  Option B:"Deploy optimized large language models (LLMs) on edge devices" is incorrect because LLMs are resource-intensive and may not perform well on edge devices due to their size and computational demands.

  Option C:"Incorporate a centralized small language model (SLM) API for asynchronous communication with edge devices" is incorrect because it introduces network latency due to the need for communication with a centralized server.

  Option D:"Incorporate a centralized large language model (LLM) API for asynchronous communication with edge devices" is incorrect for the same reason, with even greater latency due to the larger model size.

  References:

  Optimizing AI Models for Edge Devices on AWS:AWS recommends using small, optimized models for edge deployments to ensure minimal latency and efficient performance.

• Question 305:

  A company is building a generative AI application on AWS. The application will help improve reading comprehension for students. The application must give students the ability to add illustrations to stories. Which solution will meet this requirement?

  A. Use Amazon Bedrock Stable Diffusion 3.5 Large to generate images based on text inputs.
  B. Use Amazon Polly to create an audiobook based on story texts.
  C. Use Amazon Rekognition to analyze image contents and detect text attributes.
  D. Create a standard prompt template. Use Amazon Q Business to illustrate stories.
  A. Use Amazon Bedrock Stable Diffusion 3.5 Large to generate images based on text inputs.

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  Amazon Bedrock Stable Diffusion 3.5 Large is a generative AI model designed to create high-quality images from text prompts, allowing students to add custom illustrations to their stories and enhancing reading comprehension through visual aids.

• Question 306:

  Which term is the speed at which a pre-trained foundation model (FM) processes requests and delivers output?

  A. Model size
  B. Inference latency
  C. Context window
  D. Fine-tuning
  B. Inference latency

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  Explanation

  Inference latency refers to the time a foundation model takes to process an input request and produce an output, representing the speed of model response during inference.

• Question 307:

  A company is deploying AI/ML models by using AWS services. The company wants to offer transparency into the models' decision-making processes and provide explanations for the model outputs. Which AWS service or feature meets these requirements?

  A. Amazon SageMaker Model Cards
  B. Amazon Rekognition
  C. Amazon Comprehend
  D. Amazon Lex
  A. Amazon SageMaker Model Cards

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  Amazon SageMaker Model Cards help provide transparency into AI/ML models by documenting key details such as model purpose, training data, performance metrics, and limitations. This documentation enables organizations to explain model outputs and decision-making processes, ensuring accountability and compliance with responsible AI principles.

• Question 308:

  A logistics company wants to classify support emails as "urgent" or "non-urgent." The company has a labeled dataset of past emails. Which ML method should the company use?

  A. Clustering
  B. Regression
  C. Binary classification
  D. Topic modeling
  C. Binary classification

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  Binary classification is designed to separate data into two classes -- in this case, urgent vs. non-urgent. It uses labeled data to learn patterns distinguishing the two categories, aligning perfectly with the company's needs.

• Question 309:

  A company wants to use foundational models (FMs) to develop and deploy an AI model.

  Which AWS service or resource will meet these requirements with the LEAST development effort?

  A. Amazon Bedrock
  B. Amazon SageMaker AI
  C. Amazon Bedrock PartyRock
  D. Amazon Q Developer
  A. Amazon Bedrock

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  Explanation

  Amazon Bedrock provides managed access to foundation models with built-in deployment, customization, and inference capabilities, enabling development and deployment of AI solutions with minimal setup and development effort.

• Question 310:

  A company uses a foundation model (FM) from Amazon Bedrock for an AI search tool. The company wants to fine-tune the model to be more accurate by using the company's data. Which strategy will successfully fine-tune the model?

  A. Provide labeled data with the prompt field and the completion field.
  B. Prepare the training dataset by creating a .txt file that contains multiple lines in .csv format.
  C. Purchase Provisioned Throughput for Amazon Bedrock.
  D. Train the model on journals and textbooks.
  A. Provide labeled data with the prompt field and the completion field.

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  Providing labeled data with both a prompt field and a completion field is the correct strategy for fine-tuning a foundation model (FM) on Amazon Bedrock.