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| Section | Weight | Objectives |
|---|---|---|
| Security, Compliance, and Governance for AI Solutions | 14% | - Data protection and privacy in AI workflows - Compliance requirements and regulations - Governance frameworks for AI lifecycle - Security controls for AI data and models |
| Guidelines for Responsible AI | 14% | - Describe ethical and societal impacts of AI - Identify risks and mitigation strategies for AI systems - Explain bias detection and reduction - Define responsible AI principles: fairness, transparency, privacy, safety |
| Fundamentals of AI and ML | 20% | - Define artificial intelligence (AI), machine learning (ML), and deep learning - Describe common ML workflows and lifecycle - Recognize key concepts: data, models, training, inference, evaluation - Identify types of ML: supervised, unsupervised, reinforcement learning |
| Fundamentals of Generative AI | 24% | - Define generative AI and foundation models - Differentiate between generative AI and traditional ML - Explain capabilities and use cases of generative AI - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) |
| Applications of Foundation Models | 28% | - Recognize tools for building and deploying generative AI solutions - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan - Explain integration of foundation models into applications - Describe use cases for text, image, video, and code generation |
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NEW QUESTION # 381
What is an example of structured data?
Answer: D
Explanation:
The correct answer is C - A CSV file that consists of measurement data, which is a classic example of structured data. According to AWS documentation, structured data is data that is organized in a predefined schema, typically stored in tabular formats with fixed rows, columns, and data types. CSV files, relational databases, and spreadsheets fall into this category because their structure allows deterministic querying and processing. Measurement data contained in a CSV file is easy to analyze with SQL, Amazon Athena, Amazon Redshift, or SageMaker Data Wrangler. In contrast, options A and D (text comments and transcriptions) are examples of unstructured data, requiring NLP techniques. Option B (video files) represents unstructured multimedia data, requiring computer vision. Structured data is essential for supervised ML models, business analytics, and statistical modeling. AWS emphasizes that structured data provides the simplest path to feature engineering and model training due to its consistent format.
Referenced AWS Documentation:
* AWS Data Analytics Whitepaper - Structured vs. Unstructured Data
* Amazon SageMaker Data Wrangler Documentation
NEW QUESTION # 382
Which option is a characteristic of AI governance frameworks for building trust and deploying human- centered AI technologies?
Answer: C
NEW QUESTION # 383
A manufacturing company wants to create product descriptions in multiple languages.
Which AWS service will automate this task?
Answer: B
Explanation:
The manufacturing company needs to create product descriptions in multiple languages, which requires automated language translation. Amazon Translate is a fully managed service that uses machine learning to provide high-quality translation between languages, making it the ideal solution for this task.
Exact Extract from AWS AI Documents:
From the Amazon Translate Developer Guide:
"Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. It can be used to automatically translate text, such as product descriptions, into multiple languages to reach a global audience." (Source: Amazon Translate Developer Guide, Introduction to Amazon Translate) Detailed Option A: Amazon TranslateThis is the correct answer. Amazon Translate automates the translation of text into multiple languages, directly addressing the company's need to create product descriptions in different languages.
Option B: Amazon TranscribeAmazon Transcribe converts speech to text, which is unrelated to translating text into multiple languages. This option is incorrect.
Option C: Amazon KendraAmazon Kendra is an intelligent search service that uses machine learning to provide answers from documents, not for translating text. This option is irrelevant.
Option D: Amazon PollyAmazon Polly is a text-to-speech service that generates spoken audio from text, not for translating text into other languages. This option does not meet the requirements.
Reference:
Amazon Translate Developer Guide: Introduction to Amazon Translate (https://docs.aws.amazon.com/translate/latest/dg/what-is.html) AWS AI Practitioner Learning Path: Module on Natural Language Processing Services AWS Documentation: Language Translation with Amazon Translate (https://aws.amazon.com/translate/)
NEW QUESTION # 384
A law firm wants to build an AI application by using large language models (LLMs). The application will read legal documents and extract key points from the documents.
Which solution meets these requirements?
Answer: A
Explanation:
A summarization chatbot is ideal for extracting key points from legal documents. Large language models (LLMs) can be used to summarize complex texts, such as legal documents, making them more accessible and understandable.
* Option C (Correct): "Develop a summarization chatbot": This is the correct answer because a summarization chatbot uses LLMs to condense and extract key information from text, which is precisely the requirement for reading and summarizing legal documents.
* Option A: "Build an automatic named entity recognition system" is incorrect because it focuses on identifying specific entities, not summarizing documents.
* Option B: "Create a recommendation engine" is incorrect as it is used to suggest products or content, not summarize text.
* Option D: "Develop a multi-language translation system" is incorrect because translation is unrelated to summarizing text.
AWS AI Practitioner References:
* Using LLMs for Text Summarization on AWS: AWS supports developing summarization tools using its AI services, including Amazon Bedrock.
NEW QUESTION # 385
A company wants to classify human genes into 20 categories based on gene characteristics. The company needs an ML algorithm to document how the inner mechanism of the model affects the output.
Which ML algorithm meets these requirements?
Answer: C
NEW QUESTION # 386
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