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Amazon AIF-C01 Exam Questions
AWS Certified AI Practitioner (Page 7 )

Updated On: 19-Apr-2026
Page 7 of 43
PDF with 422 Questions

A company is training a foundation model (FM). The company wants to increase the accuracy of the model up to a specific acceptance level.

Which solution will meet these requirements?

  1. Decrease the batch size.
  2. Increase the epochs.
  3. Decrease the epochs.
  4. Increase the temperature parameter.

Answer(s): B

Explanation:

Increasing the number of epochs allows the model to train for more iterations, improving its accuracy until the model reaches an optimal level. The other options are either less effective or unrelated to improving accuracy.



A company is building a large language model (LLM) question answering chatbot. The company wants to decrease the number of actions call center employees need to take to respond to customer questions.

Which business objective should the company use to evaluate the effect of the LLM chatbot?

  1. Website engagement rate
  2. Average call duration
  3. Corporate social responsibility
  4. Regulatory compliance

Answer(s): B

Explanation:

Reducing the average call duration directly indicates how effectively the LLM chatbot is helping call center employees answer customer questions, thus reducing the number of actions needed. The other options are not directly related to the performance of a call center chatbot.



Which functionality does Amazon SageMaker Clarify provide?

  1. Integrates a Retrieval Augmented Generation (RAG) workflow
  2. Monitors the quality of ML models in production
  3. Documents critical details about ML models
  4. Identifies potential bias during data preparation

Answer(s): D

Explanation:

Amazon SageMaker Clarify helps detect potential bias in datasets and models during data preparation, training, and deployment. It also provides tools for explainability. The other options are functionalities that do not directly match SageMaker Clarify's core features.



A company is developing a new model to predict the prices of specific items. The model performed well on the training dataset.
When the company deployed the model to production, the model's performance decreased significantly.

What should the company do to mitigate this problem?

  1. Reduce the volume of data that is used in training.
  2. Add hyperparameters to the model.
  3. Increase the volume of data that is used in training.
  4. Increase the model training time.

Answer(s): C

Explanation:

Increasing the volume of data used in training helps the model generalize better to new, unseen data, reducing overfitting and improving performance in production. The other options either do not address the issue of model generalization or are unlikely to effectively solve the problem.



An ecommerce company wants to build a solution to determine customer sentiments based on written customer reviews of products.

Which AWS services meet these requirements? (Choose two.)

  1. Amazon Lex
  2. Amazon Comprehend
  3. Amazon Polly
  4. Amazon Bedrock
  5. Amazon Rekognition

Answer(s): B,D

Explanation:

Amazon Comprehend: This service is specifically designed for natural language processing (NLP) tasks, including sentiment analysis, making it ideal for analyzing customer reviews.
Amazon Bedrock: Bedrock can be used to leverage foundation models, which can also be employed for sentiment analysis tasks.
The other options are not suitable for sentiment analysis of written customer reviews.



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.



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?

  1. User-generated content
  2. Moderation logs
  3. Content moderation guidelines
  4. Benchmark datasets

Answer(s): D

Explanation:

Benchmark datasets are standardized datasets specifically designed for evaluating models for bias and fairness, allowing for efficient assessment with minimal administrative effort. The other options would require more manual processing and might not provide a consistent basis for evaluating bias and discrimination.



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.



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