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Free Amazon AIF-C01 Exam 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.



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