Amazon AIF-C01 Exam Prep
AWS Certified AI Practitioner (Page 17 )

Updated On: 20-Aug-2026

A company is using few-shot prompting on a base model that is hosted on Amazon Bedrock. The model currently uses 10 examples in the prompt. The model is invoked once daily and is performing well. The company wants to lower the monthly cost.
Which solution will meet these requirements?

  1. Customize the model by using fine-tuning.
  2. Decrease the number of tokens in the prompt.
  3. Increase the number of tokens in the prompt.
  4. Use Provisioned Throughput.

Answer(s): B

Explanation:

Decreasing the number of tokens in the prompt reduces the amount of data being processed, thereby lowering the cost of using the model. Since the model is performing well, reducing the prompt size is a cost-effective way to maintain performance while lowering expenses. The other options either increase costs or are unrelated to prompt size.



An AI practitioner is using a large language model (LLM) to create content for marketing campaigns. The generated content sounds plausible and factual but is incorrect.
Which problem is the LLM having?

  1. Data leakage
  2. Hallucination
  3. Overfitting
  4. Underfitting

Answer(s): B

Explanation:

Hallucination occurs when a large language model generates content that appears plausible and factual but is incorrect or fabricated. This is a common issue with LLMs. The other options do not describe this particular behavior.



An AI practitioner trained a custom model on Amazon Bedrock by using a training dataset that contains confidential data. The AI practitioner wants to ensure that the custom model does not generate inference responses based on confidential data.
How should the AI practitioner prevent responses based on confidential data?

  1. Delete the custom model. Remove the confidential data from the training dataset. Retrain the custom model.
  2. Mask the confidential data in the inference responses by using dynamic data masking.
  3. Encrypt the confidential data in the inference responses by using Amazon SageMaker.
  4. Encrypt the confidential data in the custom model by using AWS Key Management Service (AWS KMS).

Answer(s): A

Explanation:

To ensure that the custom model does not generate responses based on confidential data, the best approach is to retrain the model without including the confidential data. This prevents the model from learning patterns associated with that sensitive information, thereby avoiding its use in inference. The other options do not address the root cause of the issue—removing confidential data from the training process.



A company has built a solution by using generative AI. The solution uses large language models (LLMs) to translate training manuals from English into other languages. The company wants to evaluate the accuracy of the solution by examining the text generated for the manuals.
Which model evaluation strategy meets these requirements?

  1. Bilingual Evaluation Understudy (BLEU)
  2. Root mean squared error (RMSE)
  3. Recall-Oriented Understudy for Gisting Evaluation (ROUGE)
  4. F1 score

Answer(s): A

Explanation:

The BLEU (Bilingual Evaluation Understudy) score is a common metric used to evaluate the accuracy of machine translation by comparing the generated translation with reference translations. It is specifically designed for translation tasks, whereas the other metrics are not suitable for evaluating translation quality.



A large retailer receives thousands of customer support inquiries about products every day. The customer support inquiries need to be processed 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?

  1. Generation of custom foundation models (FMs) to predict customer needs
  2. Automation of repetitive tasks and orchestration of complex workflows
  3. Automatically calling multiple foundation models (FMs) and consolidating the results
  4. Selecting the foundation model (FM) based on predefined criteria and metrics

Answer(s): B

Explanation:

Amazon Bedrock agents help automate repetitive tasks and orchestrate complex workflows, which is ideal for handling thousands of customer support inquiries efficiently. This helps reduce response times and improves productivity. The other options do not directly address automation and orchestration of tasks for customer support.



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