Free AIF-C01 Exam Braindumps
AWS Certified AI Practitioner Questions 251–255 (page: 52)

Updated On: 1-Oct-2026

A company built a deep learning model for object detection and deployed the model to production.
Which AI process occurs when the model analyzes a new image to identify objects?

  1. Training
  2. Inference
  3. Model deployment
  4. Bias correction

Answer(s): B

Explanation:

Inference is the process where the model analyzes new data (in this case, a new image) to make predictions or identify objects. The other options are related to different stages of the AI lifecycle, such as building or preparing the model.



A company deployed an AI/ML solution to help customer service agents respond to frequently asked questions.
The questions can change over time. The company wants to give customer service agents the ability to ask questions and receive automatically generated answers to common customer questions.
Which strategy will meet these requirements MOST cost-effectively?

  1. Fine-tune the model regularly.
  2. Train the model by using context data.
  3. Pre-train and benchmark the model by using context data.
  4. Use Retrieval Augmented Generation (RAG) with prompt engineering techniques.

Answer(s): D

Explanation:

Retrieval Augmented Generation (RAG) is a cost-effective approach that enhances AI-generated responses by retrieving relevant information from external knowledge sources. Instead of fine-tuning or re-training a model,
RAG dynamically pulls the most recent and relevant data at query time. This is particularly useful in scenarios where questions change over time, ensuring that the AI/ML solution provides accurate and up-to-date responses without requiring expensive and time-consuming model retraining. Prompt engineering techniques further optimize how the model processes and generates responses, improving accuracy and relevance.



A company has an ML model. The company wants to know how the model makes predictions.
Which term refers to understanding model predictions?

  1. Model interpretability
  2. Model training
  3. Model interoperability
  4. Model performance

Answer(s): A

Explanation:

Model interpretability refers to understanding how a model makes its predictions, providing insights into the reasoning behind the model’s outputs.



A company is using Amazon Bedrock to build an assistant for its online store. The company wants to ensure that the assistant does not generate harmful responses based on hate speech, insults, sexual content, or violence.
Which strategy will prevent harmful responses in Amazon Bedrock?

  1. Use Amazon SageMaker built-in algorithms to filter harmful content.
  2. Use Amazon Comprehend toxicity detection to identify harmful content.
  3. Configure Guardrails for Amazon Bedrock to filter harmful content.
  4. Train a custom model according to the company’s responsible AI policies.

Answer(s): C

Explanation:

Guardrails for Amazon Bedrock provides content filters that can detect and block harmful model inputs and outputs across categories including hate, insults, sexual content, and violence.



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.



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