AIF-C01 Exam Dumps : AWS Certified AI Practitioner Exam

Partial dependence plots (PDPs) are visual tools used to show the relationship between a feature (or a set of features) in the data and the predicted outcome of a machine learning model. They are highly effective for providing transparency and explainability of the model's behavior to stakeholders by illustrating how different input variables impact the model's predictions.

Option B (Correct): "Partial dependence plots (PDPs)": This is the correct answer because PDPs help to interpret how the model's predictions change with varying values of input features, providing stakeholders with a clearer understanding of the model's decision-making process.

Option A: "Code for model training" is incorrect because providing the raw code for model training may not offer transparency or explainability to non-technical stakeholders.

Option C: "Sample data for training" is incorrect as sample data alone does not explain how the model works or its decision-making process.

Option D: "Model convergence tables" is incorrect. While convergence tables can show the training process, they do not provide insights into how input features affect the model's predictions.

AWS AI Practitioner References:

Explainability in AWS Machine Learning: AWS provides various tools for model explainability, such as Amazon SageMaker Clarify, which includes PDPs to help explain the impact of different features on the model’s predictions.

Amazon Textract (E) automatically extracts text and structured data from scanned documents, such as PDFs.

Amazon Bedrock (B) offers access to LLMs (such as Amazon Titan or Anthropic Claude) for tasks like summarization and generating embeddings for search.

Workflow:

Amazon Textract extracts text from PDFs in S3.

Amazon Bedrock LLMs summarize the extracted text.

(Optional: Summaries can be indexed using Amazon OpenSearch or another search solution.)

A (Translate) is for language translation, not extraction or summarization.

C (Transcribe) is for audio to text, not PDFs.

D (Polly) is for text-to-speech.

“Amazon Textract extracts text, forms, and tables from scanned documents... Bedrock provides generative AI models to perform summarization and other text generation tasks.”

(Reference: Amazon Textract, Amazon Bedrock, AWS GenAI RAG Reference)

Generative AI is a type of AI that creates new content, such as text, images, or audio, often mimicking human-like outputs. A practical use case for generative AI is employing a chatbot to provide human-like responses to customer queries in real time, as it leverages the ability of large language models (LLMs) to generate natural language responses dynamically.

Exact Extract from AWS AI Documents:

From the AWS Bedrock User Guide:

"Generative AI enables applications like chatbots to produce human-like text responses in real time, enhancing customer support by providing natural and contextually relevant answers to user queries."

(Source: AWS Bedrock User Guide, Introduction to Generative AI)

Detailed Explanation:

Option A: Using an ML model to forecast product demandForecasting product demand typically involves predictive analytics using supervised learning (e.g., regression models), not generative AI, which focuses on creating new content.

Option B: Employing a chatbot to provide human-like responses to customer queries in real timeThis is the correct answer. Generative AI, particularly LLMs, is commonly used to power chatbots that generate human-like responses, making this a practical use case.

Option C: Using an analytics dashboard to track website traffic and user behaviorAn analytics dashboard involves data visualization and analysis, not generative AI, which is about creating new content.

Option D: Implementing a rule-based recommendation engine to suggest products to customersA rule-based recommendation engine relies on predefined rules, not generative AI. Generative AI could be used for more dynamic recommendations, but this scenario does not describe such a case.