Amazon AIF-C01 Exam Prep
AWS Certified AI Practitioner (Page 16 )

Updated On: 20-Aug-2026

An accounting firm wants to implement a large language model (LLM) to automate document processing. The firm must proceed responsibly to avoid potential harms.
What should the firm do when developing and deploying the LLM? (Choose two.)

  1. Include fairness metrics for model evaluation.
  2. Adjust the temperature parameter of the model.
  3. Modify the training data to mitigate bias.
  4. Avoid overfitting on the training data.
  5. Apply prompt engineering techniques.

Answer(s): A,C

Explanation:

Include fairness metrics for model evaluation: Fairness metrics help ensure that the LLM is unbiased and treats all cases equitably, which is essential for responsible AI use. Modify the training data to mitigate bias: Adjusting the training data helps reduce any inherent bias that might exist, contributing to a more fair and responsible LLM.
The other options are related to general model optimization but do not directly address responsible AI practices regarding potential harms like bias and fairness.



A company is building an ML model. The company collected new data and analyzed the data by creating a correlation matrix, calculating statistics, and visualizing the data.
Which stage of the ML pipeline is the company currently in?

  1. Data pre-processing
  2. Feature engineering
  3. Exploratory data analysis
  4. Hyperparameter tuning

Answer(s): C

Explanation:

The company is currently in the exploratory data analysis (EDA) stage, which involves summarizing data through statistics, visualizations, and correlation matrices to understand the dataset before moving on to modeling. The other options are subsequent steps in the ML pipeline.



A company has documents that are missing some words because of a database error. The company wants to build an ML model that can suggest potential words to fill in the missing text.
Which type of model meets this requirement?

  1. Topic modeling
  2. Clustering models
  3. Prescriptive ML models
  4. BERT-based models

Answer(s): D

Explanation:

BERT-based models are well-suited for natural language understanding tasks, including filling in missing words, because they use contextual information to predict missing tokens in a text. The other types of models are not designed for this type of text completion task.



A company wants to display the total sales for its top-selling products across various retail locations in the past 12 months.
Which AWS solution should the company use to automate the generation of graphs?

  1. Amazon Q in Amazon EC2
  2. Amazon Q Developer
  3. Amazon Q in Amazon QuickSight
  4. Amazon Q in AWS Chatbot

Answer(s): C

Explanation:

Amazon Q in Amazon QuickSight allows users to ask questions in natural language and automatically generate graphs and visualizations to display insights, such as total sales for top-selling products. The other options do not provide the same functionality for generating visual analytics.



A company is building a chatbot to improve user experience. The company is using a large language model (LLM) from Amazon Bedrock for intent detection. The company wants to use few-shot learning to improve intent detection accuracy.
Which additional data does the company need to meet these requirements?

  1. Pairs of chatbot responses and correct user intents
  2. Pairs of user messages and correct chatbot responses
  3. Pairs of user messages and correct user intents
  4. Pairs of user intents and correct chatbot responses

Answer(s): C

Explanation:

Few-shot learning involves providing the model with a few examples to help it understand how to perform the task. For intent detection, the company needs pairs of user messages and the correct user intents, which will help the LLM improve its accuracy in detecting user intents. The other options do not provide the necessary pairing for improving intent detection.



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