Free AIF-C01 Exam Braindumps
AWS Certified AI Practitioner (page: 13)

Updated On: 19-Oct-2026

Which AWS service creates business intelligence reports and automatically generates executive summaries based on data that users provide?

  1. Amazon Q in QuickSight
  2. Amazon Rekognition
  3. Amazon Textract
  4. Amazon Polly

Answer(s): A

Explanation:

Amazon Q in QuickSight provides generative BI capabilities, allowing users to create reports, analyze data,
and automatically generate natural language summaries and insights from datasets.



A company needs to evaluate AI-generated summaries against reference text. The company must measure semantic similarity and coverage of key information.
Which metrics meet these requirements? (Choose two.)

  1. F1 score
  2. BERTScore
  3. Bilingual Evaluation Understudy (BLEU) score
  4. Accuracy
  5. Recall-Oriented Understudy for Gisting Evaluation (ROUGE)

Answer(s): B,E

Explanation:

BERTScore evaluates semantic similarity by comparing contextual embeddings between generated and reference text. Recall-Oriented Understudy for Gisting Evaluation measures overlap and coverage of important words or phrases, making it well suited for evaluating summaries against reference text.



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?

  1. Amazon Macie
  2. Amazon Transcribe
  3. Amazon Bedrock
  4. Amazon Textract

Answer(s): C

Explanation:

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.



A company wants to create a chatbot that answers questions about human resources policies. The company is using a large language model (LLM) and has a large digital documentation base.
Which technique should the company use to optimize the generated responses?

  1. Use Retrieval Augmented Generation (RAG).
  2. Use few-shot prompting.
  3. Set the temperature to 1.
  4. Decrease the token size.

Answer(s): A

Explanation:

RAG lets the chatbot pull in precise, up-to-date passages from your HR documentation at inference time,
grounding its answers in the actual policy text and ensuring accuracy without overloading the LLM’s context window.



A company creates video content. The company wants to use generative AI to generate new creative content and to reduce video creation time.
Which solution will meet these requirements in the MOST operationally efficient way?

  1. Use the Amazon Titan Image Generator model on Amazon Bedrock to generate intermediate images. Use video editing software to create videos.
  2. Use the Amazon Nova Canvas model on Amazon Bedrock to generate intermediate images. Use video editing software to create videos.
  3. Use the Amazon Nova Reel model on Amazon Bedrock to generate videos.
  4. Use the Amazon Nova Pro model on Amazon Bedrock to generate videos.

Answer(s): C

Explanation:

The Amazon Nova Reel model on Amazon Bedrock is specifically designed for generative video creation,
enabling the company to efficiently generate new creative content and significantly reduce video creation time in an operationally efficient manner.



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