Free AIF-C01 Exam Braindumps
AWS Certified AI Practitioner (page: 6)

Updated On: 19-Oct-2026

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A company wants to build a new ML solution. The company already has data. The company needs to understand the ML lifecycle before building the solution.
Select and order the steps from the following list to correctly describe the ML lifecycle. Select each step one time.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



The ML lifecycle starts with preparing and cleaning the available data, followed by training the model on that data. The trained model is then evaluated to verify performance before being deployed into production for real-world use.



A company is building a solution to generate images for protective eyewear. The solution must have high accuracy and must minimize the risk of incorrect annotations.
Which solution will meet these requirements?

  1. Human-in-the-loop validation by using Amazon SageMaker Ground Truth Plus
  2. Data augmentation by using an Amazon Bedrock knowledge base
  3. Image recognition by using Amazon Rekognition
  4. Data summarization by using Amazon QuickSight Q

Answer(s): A

Explanation:

Human-in-the-loop validation ensures high accuracy by involving human reviewers to verify and correct annotations, minimizing the risk of errors in the generated images. The other options are not directly relevant for ensuring annotation accuracy in image generation.



Which option is a benefit of using Amazon SageMaker Model Cards to document AI models?

  1. Providing a visually appealing summary of a model’s capabilities.
  2. Standardizing information about a model’s purpose, performance, and limitations.
  3. Reducing the overall computational requirements of a model.
  4. Physically storing models for archival purposes.

Answer(s): B

Explanation:

Amazon SageMaker Model Cards provide a structured way to document key details about AI models, including their intended use, performance metrics, and limitations. This helps organizations maintain transparency,
compliance, and governance in AI model development, making it easier to track and manage models over time.



A company is building an AI application to summarize books of varying lengths. During testing, the application fails to summarize some books.
Why does the application fail to summarize some books?

  1. The temperature is set too high.
  2. The selected model does not support fine-tuning.
  3. The Top P value is too high.
  4. The input tokens exceed the model’s context size.

Answer(s): D

Explanation:

Language models have a maximum context size, which limits the number of input tokens they can process at once. If a book's length exceeds this limit, the model cannot handle the full input, leading to failure in summarization.



A company wants to develop a large language model (LLM) application by using Amazon Bedrock and customer data that is uploaded to Amazon S3. The company's security policy states that each team can access data for only the team's own customers.
Which solution will meet these requirements?

  1. Create an Amazon Bedrock custom service role for each team that has access to only the team's customer data.
  2. Create a custom service role that has Amazon S3 access. Ask teams to specify the customer name on each
    Amazon Bedrock request.
  3. Redact personal data in Amazon S3. Update the S3 bucket policy to allow team access to customer data.
  4. Create one Amazon Bedrock role that has full Amazon S3 access. Create IAM roles for each team that have access to only each team's customer folders.

Answer(s): A

Explanation:

Creating a custom Amazon Bedrock service role for each team with restricted access to only the team's customer data ensures compliance with the security policy, providing the necessary data segregation and access control. The other options do not effectively enforce team-specific access or may pose risks of broader access than allowed by the policy.



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