Free AIF-C01 Exam Braindumps
AWS Certified AI Practitioner (page: 3)

Updated On: 19-Oct-2026

A company has fine-tuned an Amazon Bedrock foundation model (FM) to produce short document summaries.
The company wants an automated metric that compares each model-generated summary with its human-written reference summary.
Which metric will meet these requirements?

  1. F1 score
  2. Recall-Oriented Understudy for Gisting Evaluation (ROUGE)
  3. Perplexity
  4. Fréchet inception distance (FID)

Answer(s): B

Explanation:

Recall-Oriented Understudy for Gisting Evaluation is used to evaluate generated text summaries by comparing them with human-written reference summaries. It measures overlap in words or sequences, making it suitable for automated summary quality evaluation.



A company is training a foundation model (FM). The company wants the FM training data to be well managed,
secure, and compliant throughout the data lifecycle. The data must follow governance practices.
Which option is an AWS data governance strategy for managing data across the data lifecycle?

  1. Allow unrestricted access to all data for every employee.
  2. Store data indefinitely without classification or retention policies.
  3. Rely solely on manual reviews for data quality and compliance.
  4. Establish data lifecycle policies, access controls, logging, and monitoring.

Answer(s): D

Explanation:

Establishing data lifecycle policies, access controls, logging, and monitoring helps ensure that training data is managed securely, governed properly, and kept compliant throughout its lifecycle.



A company wants to use a pre-trained generative AI model to generate content for its marketing campaigns.
The company needs to ensure that the generated content aligns with the company's brand voice and messaging requirements.
Which solution meets these requirements?

  1. Optimize the model's architecture and hyperparameters to improve the model's overall performance.
  2. Increase the model's complexity by adding more layers to the model's architecture.
  3. Create effective prompts that provide clear instructions and context to guide the model's generation.
  4. Select a large, diverse dataset to pre-train a new generative model.

Answer(s): C

Explanation:

Creating effective prompts helps guide the pre-trained generative AI model to produce content that aligns with the company's brand voice and messaging. The other options either involve model architecture changes or require extensive training, which are not necessary for aligning content generation.



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?

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

Answer(s): A

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.



A company wants to use large language models (LLMs) with Amazon Bedrock to develop a chat interface for the company's product manuals. The manuals are stored as PDF files.
Which solution meets these requirements MOST cost-effectively?

  1. Use prompt engineering to add one PDF file as context to the user prompt when the prompt is submitted to
    Amazon Bedrock.
  2. Use prompt engineering to add all the PDF files as context to the user prompt when the prompt is submitted to Amazon Bedrock.
  3. Use all the PDF documents to fine-tune a model with Amazon Bedrock. Use the fine-tuned model to process user prompts.
  4. Upload PDF documents to an Amazon Bedrock knowledge base. Use the knowledge base to provide context when users submit prompts to Amazon Bedrock.

Answer(s): D

Explanation:

Using an Amazon Bedrock knowledge base allows the model to efficiently access relevant information from the
PDF manuals when needed, reducing the cost compared to continuously fine-tuning a model or providing all
PDFs as context in each prompt. This approach ensures that only necessary context is provided, making it cost-effective.



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