Amazon AIF-C01 Exam Prep
AWS Certified AI Practitioner (Page 14 )

Updated On: 20-Aug-2026

An AI practitioner is using an Amazon Bedrock base model to summarize session chats from the customer service department. The AI practitioner wants to store invocation logs to monitor model input and output data.
Which strategy should the AI practitioner use?

  1. Configure AWS CloudTrail as the logs destination for the model.
  2. Enable model invocation logging in Amazon Bedrock.
  3. Configure AWS Audit Manager as the logs destination for the model.
  4. Configure model invocation logging in Amazon EventBridge.

Answer(s): B

Explanation:

Enabling invocation logging in Amazon Bedrock allows the AI practitioner to monitor and store the input and output data for model invocations. The other options are not directly used for logging model invocations in Amazon Bedrock.



A company is building an ML model to analyze archived data. The company must perform inference on large datasets that are multiple GBs in size. The company does not need to access the model predictions immediately.
Which Amazon SageMaker inference option will meet these requirements?

  1. Batch transform
  2. Real-time inference
  3. Serverless inference
  4. Asynchronous inference

Answer(s): A

Explanation:

Batch transform is ideal for processing large datasets that do not require real-time predictions. It allows the company to perform inference on multiple GBs of data efficiently without needing immediate results. The other options are more suitable for scenarios requiring real-time or near real-time access.



Which term describes the numerical representations of real-world objects and concepts that AI and natural language processing (NLP) models use to improve understanding of textual information?

  1. Embeddings
  2. Tokens
  3. Models
  4. Binaries

Answer(s): A

Explanation:

Embeddings are numerical representations of real-world objects and concepts that help AI and NLP models understand and work with textual information more effectively by capturing relationships and similarities between words or phrases. The other options do not describe this concept.



A research company implemented a chatbot by using a foundation model (FM) from Amazon Bedrock. The chatbot searches for answers to questions from a large database of research papers.
After multiple prompt engineering attempts, the company notices that the FM is performing poorly because of the complex scientific terms in the research papers.
How can the company improve the performance of the chatbot?

  1. Use few-shot prompting to define how the FM can answer the questions.
  2. Use domain adaptation fine-tuning to adapt the FM to complex scientific terms.
  3. Change the FM inference parameters.
  4. Clean the research paper data to remove complex scientific terms.

Answer(s): B

Explanation:

Domain adaptation fine-tuning allows the FM to better understand the complex scientific terms by training it with domain-specific data, improving its performance on such specialized content. The other options are either insufficient or not directly related to handling complex terminology effectively.



A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company needs the LLM to produce more consistent responses to the same input prompt.
Which adjustment to an inference parameter should the company make to meet these requirements?

  1. Decrease the temperature value.
  2. Increase the temperature value.
  3. Decrease the length of output tokens.
  4. Increase the maximum generation length.

Answer(s): A

Explanation:

Decreasing the temperature value makes the model's output more deterministic and consistent by reducing randomness in response generation. The other adjustments do not directly ensure consistent responses.



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