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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Passing Score: | 700 / 1000 |
| Exam Price: | USD 100 |
| Available Languages: | Portuguese (Brazil), Korean, English, Simplified Chinese, Japanese |
| Exam Duration: | 90 minutes |
| Related Certifications: | AWS Certified Cloud Practitioner AWS Certified Data Engineer - Associate AWS Certified Machine Learning Engineer - Associate |
| Certificate Validity Period: | 3 years |
| Exam Format: | Multiple response, Multiple choice |
| Real Exam Qty: | 80 |
| Sample Questions: | Amazon AIF-C01 Sample Questions |
| Exam Way: | Online proctored exam (Pearson VUE) or in-person testing center |
| Pre Condition: | None required. Recommended: General IT cloud knowledge and basic understanding of AI/ML concepts. AWS Cloud Practitioner certification is a recommended prerequisite but not mandatory. |
| Official Syllabus URL: | https://aws.amazon.com/certification/certified-ai-practitioner/ |
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NEW QUESTION # 27
What does an F1 score measure in the context of foundation model (FM) performance?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
The F1 score is a standard evaluation metric that represents the harmonic mean of precision and recall.
In AWS ML evaluation guidance:
* Precision measures correctness of positive predictions
* Recall measures coverage of actual positive cases
* F1 score balances both metrics into a single performance indicator
This makes the F1 score particularly useful when evaluating classification performance of foundation models.
Why the other options are incorrect:
* Speed (B) is a latency metric.
* Cost (C) measures operational efficiency.
* Energy efficiency (D) is unrelated to predictive accuracy.
AWS AI document references:
* Model Evaluation Metrics on AWS
* Classification Performance Measurement
* Amazon SageMaker Evaluation Best Practices
NEW QUESTION # 28
A company has multiple datasets that contain historical data. The company wants to use ML technologies to process each dataset.
Select the correct ML technology from the following list for each dataset. Select each ML technology one time or not at all. (Select THREE.)
* Computer vision
* Natural language processing (NLP)
* Reinforcement learning
* Time series forecasting
Answer:
Explanation:
Explanation:
Dataset 1: A dataset that contains text-based customer reviews # Natural language processing (NLP)
* NLP is designed for analyzing text (sentiment analysis, text classification, etc.).
Dataset 2: A dataset that contains images of animals labeled with their species names # Computer vision
* Computer vision models classify or detect objects in images.
Dataset 3: A dataset that contains daily sales volumes for products # Time series forecasting
* Time series forecasting predicts future values based on historical sequential data (like sales, demand, stock prices).
NEW QUESTION # 29
An airline company wants to build a conversational AI assistant to answer customer questions about flight schedules, booking, and payments. The company wants to use large language models (LLMs) and a knowledge base to create a text-based chatbot interface.
Which solution will meet these requirements with the LEAST development effort?
Answer: A
Explanation:
The airline company aims to build a conversational AI assistant using large language models (LLMs) and a knowledge base to create a text-based chatbot with minimal development effort. Retrieval Augmented Generation (RAG) on Amazon Bedrock is an ideal solution because it combines LLMs with a knowledge base to provide accurate, contextually relevant responses without requiring extensive model training or custom development. RAG retrieves relevant information from a knowledge base and uses an LLM to generate responses, simplifying the development process.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Retrieval Augmented Generation (RAG) in Amazon Bedrock enables developers to build conversational AI applications by combining foundation models with external knowledge bases. This approach minimizes development effort by leveraging pre-trained models and integrating them with data sources, such as FAQs or databases, to provide accurate and contextually relevant responses." (Source: AWS Bedrock User Guide, Retrieval Augmented Generation) Detailed Option A: Train models on Amazon SageMaker Autopilot.SageMaker Autopilot is designed for automated machine learning (AutoML) tasks like classification or regression, not for building conversational AI with LLMs and knowledge bases. It requires significant data preparation and is not optimized for chatbot development, making it less suitable.
Option B: Develop a Retrieval Augmented Generation (RAG) agent by using Amazon Bedrock.This is the correct answer. RAG on Amazon Bedrock allows the company to use pre-trained LLMs and integrate them with a knowledge base (e.g., flight schedules or FAQs) to build a chatbot with minimal effort. It avoids the need for extensive training or coding, aligning with the requirement for least development effort.
Option C: Create a Python application by using Amazon Q Developer.While Amazon Q Developer can assist with code generation, building a chatbot from scratch in Python requires significant development effort, including integrating LLMs and a knowledge base manually, which is more complex than using RAG on Bedrock.
Option D: Fine-tune models on Amazon SageMaker Jumpstart.Fine-tuning models on SageMaker Jumpstart requires preparing training data and customizing LLMs, which involves more effort than using a pre-built RAG solution on Bedrock. This option is not the least effort-intensive.
Reference:
AWS Bedrock User Guide: Retrieval Augmented Generation (https://docs.aws.amazon.com/bedrock/latest/userguide/rag.html) AWS AI Practitioner Learning Path: Module on Generative AI and Conversational AI Amazon Bedrock Developer Guide: Building Conversational AI (https://aws.amazon.com/bedrock/)
NEW QUESTION # 30
A company wants to develop an AI assistant for employees to query internal data.
Which AWS service will meet this requirement?
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Amazon Q Business is a managed AI assistant designed to:
* Allow employees to query internal enterprise data
* Provide conversational answers
* Respect enterprise security and access controls
AWS guidance positions Amazon Q Business as the solution for internal knowledge discovery and enterprise AI assistance.
Why the other options are incorrect:
* Rekognition (A) analyzes images.
* Textract (B) extracts text from documents.
* Lex (C) builds conversational interfaces but does not provide enterprise data integration out of the box.
AWS AI document references:
* Amazon Q Business Overview
* Enterprise AI Assistants on AWS
* Secure Access to Internal Data with AI
NEW QUESTION # 31
A company is deploying AI/ML models by using AWS services. The company wants to offer transparency into the models' decision-making processes and provide explanations for the model outputs.
Answer: C
Explanation:
Comprehensive and Detailed
Amazon SageMaker Model Cards document model details, performance, intended use cases, and risk considerations. They support responsible AI by improving transparency and governance.
Rekognition is computer vision.
Comprehend is NLP for entity/sentiment.
Lex is conversational AI.
Reference:
AWS Documentation - SageMaker Model Cards
NEW QUESTION # 32
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