Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 71:

  A company has developed an ML model for image classification. The company wants to deploy the model to production so that a web application can use the model.

  The company needs to implement a solution to host the model and serve predictions without managing any of the underlying infrastructure.

  Which solution will meet these requirements?

  A. Use Amazon SageMaker Serverless Inference to deploy the model.
  B. Use Amazon CloudFront to deploy the model.
  C. Use Amazon API Gateway to host the model and serve predictions.
  D. Use AWS Batch to host the model and serve predictions.
  A. Use Amazon SageMaker Serverless Inference to deploy the model.

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  Amazon SageMaker Serverless Inference is the correct solution for deploying an ML model to production in a way that allows a web application to use the model without the need to manage the underlying infrastructure. Amazon SageMaker Serverless Inferenceprovides a fully managed environment for deploying machine learning models. It automatically provisions, scales, and manages the infrastructure required to host the model, removing the need for the company to manage servers or other underlying infrastructure.

• Question 72:

  A company has developed an ML model to approve or reject loan applications. The model's decision- making process must be transparent and explainable to comply with regulatory requirements. The company must document the decision-making process for audit purposes. Which solution will meet these requirements?

  A. Amazon Textract
  B. Amazon SageMaker Model Card
  C. AWS Cloud Formation
  D. Amazon Comprehend
  B. Amazon SageMaker Model Card

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  Amazon SageMaker Model Card enables you to document a model's decision-making process, including transparency and explainability details, which helps meet regulatory and audit requirements for AI-driven decisions such as loan approvals.

• Question 73:

  A company is building a mobile app for users who have a visual impairment. The app must be able to hear what users say and provide voice responses. Which solution will meet these requirements?

  A. Use a deep learning neural network to perform speech recognition.
  B. Build ML models to search for patterns in numeric data.
  C. Use generative AI summarization to generate human-like text.
  D. Build custom models for image classification and recognition.
  A. Use a deep learning neural network to perform speech recognition.

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  To meet the requirements of enabling the app to "hear what users say and provide voice responses," the solution must include speech recognition and text-to-speech capabilities. Using a deep learning neural network for speech recognition allows the app to convert spoken words into text. Once the input is understood, text-to-speech systems can provide voice responses back to users. This approach is fundamental in applications that assist users with visual impairments by enabling interaction through spoken language.

• Question 74:

  A company is developing a product recommendation application by using a generative AI model. The company must minimize the application's environmental impact.

  Which solution will meet these requirements?

  A. Optimize the deployed model architecture to prioritize computational efficiency during model inference.
  B. Adopt a distributed inference approach by using multiple smaller models across multiple Availability Zones.
  C. Adopt a hybrid strategy by deploying the model on premises and storing the data on AWS.
  D. Deploy multiple models and use a dynamic model selection mechanism that queries different models randomly.
  A. Optimize the deployed model architecture to prioritize computational efficiency during model inference.

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  Explanation

  Optimizing the model architecture for computational efficiency reduces the compute resources and energy required during inference, which directly lowers the environmental impact of running the generative AI application.

• Question 75:

  A company is building a chatbot to improve user experience. The company is using a large language model (LLM) from Amazon Bedrock for intent detection. The company wants to use few-shot learning to improve intent detection accuracy.

  Which additional data does the company need to meet these requirements?

  A. Pairs of chatbot responses and correct user intents
  B. Pairs of user messages and correct chatbot responses
  C. Pairs of user messages and correct user intents
  D. Pairs of user intents and correct chatbot responses
  C. Pairs of user messages and correct user intents

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  Few-shot learning involves providing a model with a few examples (shots) to learn from. For improving intent detection accuracy in a chatbot using a large language model (LLM), the data should consist of pairs of user messages and their corresponding correct intents.

• Question 76:

  HOTSPOT

  A company is building an AI solution by using Amazon SageMaker AI. The company wants to use SageMaker AI features to facilitate application development.

  Select the correct SageMaker AI feature from the following list for each use case. Select each feature one time.

  

  

  Explanation:

  Determine the most suitable model to use for a business case -> Model Cards Prepare data through a low-code or no-code interface -> Data Wrangler Identify biases or imbalances in the data -> Clarify

  Model Cards help summarize and communicate model characteristics for selection.

  Data Wrangler provides a no-code/low-code interface for data preparation.

  Clarify is used to detect bias and imbalances in datasets and models.

• Question 77:

  A company deploys a foundation model (FM). The company notices that the FM is producing answers to user-submitted questions about politics. The company wants to ensure that the model does not send answers to political questions to users.

  Which AWS solution will meet this requirement?

  A. Amazon Bedrock Guardrails
  B. Amazon Bedrock Agents
  C. Amazon SageMaker Clarify
  D. Amazon SageMaker Model Monitor
  A. Amazon Bedrock Guardrails

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  Explanation

  Amazon Bedrock Guardrails allows you to define and enforce content policies, such as blocking or filtering responses to sensitive topics like politics, ensuring the model does not return prohibited outputs.

• Question 78:

  A company's employees provide product descriptions and recommendations to customers when customers call the customer service center. These recommendations are based on where the customers are located. The company wants to use foundation models (FMs) to automate this process. Which AWS service meets these requirements?

  A. Amazon Macie
  B. Amazon Transcribe
  C. Amazon Bedrock
  D. Amazon Textract
  C. Amazon Bedrock

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  Amazon Bedrock enables companies to use foundation models (FMs) to build and automate tasks like generating product descriptions and recommendations. It allows the integration of pre-trained FMs into applications without managing infrastructure, making it an ideal choice for automating customer service tasks. With Amazon Bedrock, the company can leverage FMs to generate tailored recommendations based on customer locations, enabling dynamic and efficient customer interactions.

• Question 79:

  A financial company uses an ML model to detect potentially fraudulent transactions. The company needs to ensure that some types of predictions receive review by human analysts before the company acts upon the predictions.

  Which AWS solution will meet this requirement?

  A. Amazon SageMaker Clarify
  B. Amazon SageMaker Ground Truth
  C. Amazon Augmented AI (Amazon A2I)
  D. Amazon SageMaker Model Monitor
  C. Amazon Augmented AI (Amazon A2I)

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  Explanation

  Amazon Augmented AI (Amazon A2I) adds human review workflows to ML predictions so selected fraud detection results can be routed to human analysts before action is taken.

• Question 80:

  A company notices that its foundation model (FM) generates images that are unrelated to the prompts. The company wants to modify the prompt techniques to decrease unrelated images. Which solution meets these requirements?

  A. Use zero-shot prompts.
  B. Use negative prompts.
  C. Use positive prompts.
  D. Use ambiguous prompts.
  B. Use negative prompts.

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  Negative prompts are used to explicitly instruct the model about what to avoid or not generate. By providing the model with specific guidance on what is not desired (e.g., by including terms or concepts that should not appear in the image), the model can better focus on generating relevant and related content. This technique can help reduce unrelated or irrelevant images by constraining the model's creative generation process.