TRY AMAZON AIF-C01 QUESTIONS - BEST WAY TO GO THROUGH AIF-C01 EXAM [2025]

Try Amazon AIF-C01 Questions - Best Way To Go Through AIF-C01 Exam [2025]

Try Amazon AIF-C01 Questions - Best Way To Go Through AIF-C01 Exam [2025]

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Amazon AWS Certified AI Practitioner Sample Questions (Q54-Q59):

NEW QUESTION # 54
A law firm wants to build an AI application by using large language models (LLMs). The application will read legal documents and extract key points from the documents.
Which solution meets these requirements?

  • A. Build an automatic named entity recognition system.
  • B. Develop a summarization chatbot.
  • C. Create a recommendation engine.
  • D. Develop a multi-language translation system.

Answer: B


NEW QUESTION # 55
A social media company wants to use a large language model (LLM) for content moderation. The company wants to evaluate the LLM outputs for bias and potential discrimination against specific groups or individuals.
Which data source should the company use to evaluate the LLM outputs with the LEAST administrative effort?

  • A. Content moderation guidelines
  • B. Benchmark datasets
  • C. Moderation logs
  • D. User-generated content

Answer: B


NEW QUESTION # 56
A company wants to create a chatbot by using a foundation model (FM) on Amazon Bedrock. The FM needs to access encrypted data that is stored in an Amazon S3 bucket.
The data is encrypted with Amazon S3 managed keys (SSE-S3).
The FM encounters a failure when attempting to access the S3 bucket data.
Which solution will meet these requirements?

  • A. Use prompt engineering techniques to tell the model to look for information in Amazon S3.
  • B. Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key.
  • C. Set the access permissions for the S3 buckets to allow public access to enable access over the internet.
  • D. Ensure that the S3 data does not contain sensitive information.

Answer: B

Explanation:
Amazon Bedrock needs the appropriate IAM role with permission to access and decrypt data stored in Amazon S3. If the data is encrypted with Amazon S3 managed keys (SSE-S3), the role that Amazon Bedrock assumes must have the required permissions to access and decrypt the encrypted data.
* Option A (Correct): "Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key": This is the correct solution as it ensures that the AI model can access the encrypted data securely without changing the encryption settings or compromising data security.
* Option B: "Set the access permissions for the S3 buckets to allow public access" is incorrect because it violates security best practices by exposing sensitive data to the public.
* Option C: "Use prompt engineering techniques to tell the model to look for information in Amazon S3" is incorrect as it does not address the encryption and permission issue.
* Option D: "Ensure that the S3 data does not contain sensitive information" is incorrect because it does not solve the access problem related to encryption.
AWS AI Practitioner References:
* Managing Access to Encrypted Data in AWS: AWS recommends using proper IAM roles and policies to control access to encrypted data stored in S3.


NEW QUESTION # 57
A large retailer receives thousands of customer support inquiries about products every day. The customer support inquiries need to be processed and responded to quickly. The company wants to implement Agents for Amazon Bedrock.
What are the key benefits of using Amazon Bedrock agents that could help this retailer?

  • A. Generation of custom foundation models (FMs) to predict customer needs
  • B. Selecting the foundation model (FM) based on predefined criteria and metrics
  • C. Automatically calling multiple foundation models (FMs) and consolidating the results
  • D. Automation of repetitive tasks and orchestration of complex workflows

Answer: D


NEW QUESTION # 58
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company needs the LLM to produce more consistent responses to the same input prompt.
Which adjustment to an inference parameter should the company make to meet these requirements?

  • A. Increase the temperature value
  • B. Increase the maximum generation length
  • C. Decrease the length of output tokens
  • D. Decrease the temperature value

Answer: D

Explanation:
The temperature parameter in a large language model (LLM) controls the randomness of the model's output.
A lower temperature value makes the output more deterministic and consistent, meaning that the model is less likely to produce different results for the same input prompt.
* Option A (Correct): "Decrease the temperature value": This is the correct answer because lowering the temperature reduces the randomness of the responses, leading to more consistent outputs for the same input.
* Option B: "Increase the temperature value" is incorrect because it would make the output more random and less consistent.
* Option C: "Decrease the length of output tokens" is incorrect as it does not directly affect the consistency of the responses.
* Option D: "Increase the maximum generation length" is incorrect because this adjustment affects the output length, not the consistency of the model's responses.
AWS AI Practitioner References:
* Understanding Temperature in Generative AI Models: AWS documentation explains that adjusting the temperature parameter affects the model's output randomness, with lower values providing more consistent outputs.


NEW QUESTION # 59
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