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Amazon AIF-C01 Exam Questions
AWS Certified AI Practitioner (Page 13 )

Updated On: 19-Apr-2026
Page 8 of 43
PDF with 422 Questions

A loan company is building a generative AI-based solution to offer new applicants discounts based on specific business criteria. The company wants to build and use an AI model responsibly to minimize bias that could negatively affect some customers.

Which actions should the company take to meet these requirements? (Choose two.)

  1. Detect imbalances or disparities in the data.
  2. Ensure that the model runs frequently.
  3. Evaluate the model's behavior so that the company can provide transparency to stakeholders.
  4. Use the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) technique to ensure that the model is 100% accurate.
  5. Ensure that the model's inference time is within the accepted limits.

Answer(s): A,C

Explanation:

Detect imbalances or disparities in the data: Identifying and addressing data imbalances helps minimize biases that could negatively affect customers.
Evaluate the model's behavior so that the company can provide transparency to stakeholders:
Evaluating the model and ensuring transparency is important for responsible AI usage, as it helps stakeholders understand how decisions are made.
The other options are either not directly related to minimizing bias or do not address responsible AI development.



A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality.

Which action must the company take to use the custom model through Amazon Bedrock?

  1. Purchase Provisioned Throughput for the custom model.
  2. Deploy the custom model in an Amazon SageMaker endpoint for real-time inference.
  3. Register the model with the Amazon SageMaker Model Registry.
  4. Grant access to the custom model in Amazon Bedrock.

Answer(s): A



A company needs to choose a model from Amazon Bedrock to use internally. The company must identify a model that generates responses in a style that the company's employees prefer.

What should the company do to meet these requirements?

  1. Evaluate the models by using built-in prompt datasets.
  2. Evaluate the models by using a human workforce and custom prompt datasets.
  3. Use public model leaderboards to identify the model.
  4. Use the model InvocationLatency runtime metrics in Amazon CloudWatch when trying models.

Answer(s): B

Explanation:

Evaluating models using a human workforce and custom prompt datasets ensures that the model generates responses in the style that aligns with the company's preferences. The other options either do not provide direct feedback on style preferences or are not specific enough for determining suitability based on employee preferences.



A student at a university is copying content from generative AI to write essays.

Which challenge of responsible generative AI does this scenario represent?

  1. Toxicity
  2. Hallucinations
  3. Plagiarism
  4. Privacy

Answer(s): C

Explanation:

Copying content from generative AI to write essays without proper attribution constitutes plagiarism, which is a key challenge of responsible generative AI. The other options are unrelated to this specific issue.



A company needs to build its own large language model (LLM) based on only the company's private data. The company is concerned about the environmental effect of the training process.

Which Amazon EC2 instance type has the LEAST environmental effect when training LLMs?

  1. Amazon EC2 C series
  2. Amazon EC2 G series
  3. Amazon EC2 P series
  4. Amazon EC2 Trn series

Answer(s): D

Explanation:

Amazon EC2 Trn series instances (powered by AWS Trainium chips) are designed to provide efficient and environmentally friendly training of large machine learning models. They are optimized for energy efficiency, which reduces the environmental impact of the training process. The other instance types are not specifically optimized for minimizing environmental effects during training.



A company wants to build an interactive application for children that generates new stories based on classic stories. The company wants to use Amazon Bedrock and needs to ensure that the results and topics are appropriate for children.

Which AWS service or feature will meet these requirements?

  1. Amazon Rekognition
  2. Amazon Bedrock playgrounds
  3. Guardrails for Amazon Bedrock
  4. Agents for Amazon Bedrock

Answer(s): C

Explanation:

Guardrails for Amazon Bedrock can help ensure that the output generated by Amazon Bedrock is appropriate for children. Guardrails are used to apply content moderation, guidelines, and ensure safety by filtering potentially harmful or inappropriate content, which is essential when building an interactive application for children.



A company is building an application that needs to generate synthetic data that is based on existing data.

Which type of model can the company use to meet this requirement?

  1. Generative adversarial network (GAN)
  2. XGBoost
  3. Residual neural network
  4. WaveNet

Answer(s): A



A digital devices company wants to predict customer demand for memory hardware. The company does not have coding experience or knowledge of ML algorithms and needs to develop a data-driven predictive model. The company needs to perform analysis on internal data and external data.

Which solution will meet these requirements?

  1. Store the data in Amazon S3. Create ML models and demand forecast predictions by using Amazon SageMaker built-in algorithms that use the data from Amazon S3.
  2. Import the data into Amazon SageMaker Data Wrangler. Create ML models and demand forecast predictions by using SageMaker built-in algorithms.
  3. Import the data into Amazon SageMaker Data Wrangler. Build ML models and demand forecast predictions by using an Amazon Personalize Trending-Now recipe.
  4. Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas.

Answer(s): D

Explanation:

Amazon SageMaker Canvas is a no-code tool that allows users to build ML models and make predictions without requiring programming knowledge. It is ideal for users with no coding experience, providing an easy interface for importing data and generating predictive models. The other options require more technical expertise or are not designed for no-code model building.



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