Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 341:

  A healthcare company wants to analyze patient data. The data was gathered over the previous year to detect patterns in disease outbreaks. The company needs to create a trend analysis report for each month to present to public health officials. The company must provide insights into patient data from the most recent month of the current year. Which inference method will meet these requirements MOST cost-effectively?

  A. Real-time inference
  B. Batch transform
  C. Serverless inference
  D. Asynchronous inference
  B. Batch transform

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  Batch transform is the most cost-effective inference method for analyzing large amounts of data collected over a period (such as monthly patient data). It allows you to process and generate reports on all historical data in batches, rather than incurring the higher costs of real-time or serverless inference.

• Question 342:

  A team wants to test multiple LLMs for a customer support chatbot. They need to evaluate them using accuracy, relevance, tone, and hallucination rates. Which service provides these capabilities?

  A. Amazon SageMaker Canvas
  B. Amazon Bedrock model evaluation
  C. Amazon Rekognition
  D. AWS Audit Manager
  B. Amazon Bedrock model evaluation

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  Bedrock model evaluation offers structured scoring, including accuracy, coherence, safety, and hallucination detection. It enables comparing different LLMs to determine the best model for support use cases.

• Question 343:

  A design company is using a foundation model (FM) on Amazon Bedrock to generate images for various projects. The company wants to have control over how detailed or abstract each generated image appears Which model parameter should the company modify?

  A. Model checkpoint
  B. Batch size
  C. Generation step
  D. Token length
  C. Generation step

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  The "generation step" parameter controls how detailed or abstract an image appears during generation. By adjusting the number of generation steps, you can influence the level of refinement and detail in the output

  - more steps yield more detailed images, while fewer steps result in more abstract images.

• Question 344:

  A company wants to strengthen governance for its AI solutions. The company needs access to AWS compliance reports and also wants to assess its environment against compliance requirements.

  Which AWS services should the company use? (Select TWO.)

  A. AWS Artifact
  B. AWS Audit Manager
  C. Amazon Polly
  D. Amazon Personalize
  E. Amazon Neptune
  A. AWS Artifact
  B. AWS Audit Manager

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  Explanation

  AWS Artifact and AWS Audit Manager are the correct answers because Artifact provides access to AWS compliance reports and agreements, while Audit Manager helps assess and manage compliance activities. Together they support AI governance and compliance workflows.

  Option A (Correct): "AWS Artifact": This is correct because it provides access to compliance reports and related documentation.

  Option B (Correct): "AWS Audit Manager": This is correct because it helps evaluate and manage compliance evidence and assessments.

  Option C: "Amazon Polly" is incorrect because it is a text-to-speech service.

  Option D: "Amazon Personalize" is incorrect because it is used for recommendations.

  Option E: "Amazon Neptune" is incorrect because it is a graph database service.

• Question 345:

  Which option is a characteristic of transformer-based language models?

  A. Transformer-based language models use convolutional layers to apply filters across an input to capture local patterns through filtered views.
  B. Transformer-based language models can process only text data.
  C. Transformer-based language models use self-attention mechanisms to capture contextual relationships.
  D. Transformer-based language models process data sequences one element at a time in cyclic iterations.
  C. Transformer-based language models use self-attention mechanisms to capture contextual relationships.

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  A key characteristic of transformer models is their use of self-attention mechanisms, which allow the model to weigh the importance of different words in a sequence relative to each other, enabling a deep understanding of context and meaning across the entire input.

• Question 346:

  HOTSPOT

  A company wants to create an application to summarize meetings by using meeting audio recordings.

  Select and order the correct steps from the following list to create the application. Each step should be selected one time or not at all. (Select and order THREE.)

  

  

  Explanation:

• Question 347:

  A company wants to use a large language model (LLM) to generate responses based on company data.

  Which strategy will meet this requirement?

  A. Use first-party pre-trained models from Amazon Bedrock.
  B. Use pre-trained open source models.
  C. Fine-tune a custom model on company data.
  D. Use pre-trained third-party models from Amazon Bedrock.
  C. Fine-tune a custom model on company data.

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  Explanation

  Fine-tuning a custom model on company data adapts the model's behavior and responses to the organization's specific domain, terminology, and knowledge, enabling it to generate responses based on that data.

• Question 348:

  A company wants to generate synthetic data responses for multiple prompts from a large volume of data. The company wants to use an API method to generate the responses. The company does not need to generate the responses immediately.

  Which solution meets these requirements with the LEAST development effort?

  A. Input the prompts into the model. Generate responses by using real-time inference.
  B. Use Amazon Bedrock batch inference. Generate responses asynchronously.
  C. Use Amazon Bedrock agents. Build an agent system to process the prompts recursively.
  D. Use AWS Lambda functions to automate the task. Submit one prompt after another and store each response.
  B. Use Amazon Bedrock batch inference. Generate responses asynchronously.

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  Explanation

  Amazon Bedrock batch inference allows large volumes of prompts to be processed asynchronously through a managed API, making it ideal for generating synthetic data at scale without requiring immediate responses or custom orchestration logic.

• Question 349:

  A company wants to keep its foundation model (FM) relevant by using the most recent data. The company wants to implement a model training strategy that includes regular updates to the FM. Which solution meets these requirements?

  A. Batch learning
  B. Continuous pre-training
  C. Static training
  D. Latent training
  B. Continuous pre-training

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  Continuous pre-training keeps a foundation model "always learning" by periodically (or even continuously) ingesting the latest data and updating its weights. This approach ensures the model stays current without having to start training from scratch every time new data arrives, unlike static or strictly batch-oriented methods.

• Question 350:

  An education company wants to build a private tutor application. The application will give users the ability to enter text or provide a picture of a question. The application will respond with a written answer and an explanation of the written answer.

  Which model type meets these requirements?

  A. Computer vision model
  B. Multimodal LLM
  C. Diffusion model
  D. Text-to-speech model
  B. Multimodal LLM

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  Explanation

  A multimodal large language model can process both text and image inputs and generate text outputs, enabling the application to accept written questions or images of questions and return a written answer with an explanation.