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| Section | Weight | Objectives |
|---|---|---|
| Applications of Foundation Models | 28% | - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan - Explain integration of foundation models into applications - Recognize tools for building and deploying generative AI solutions - Describe use cases for text, image, video, and code generation |
| Fundamentals of Generative AI | 24% | - Define generative AI and foundation models - Differentiate between generative AI and traditional ML - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) - Explain capabilities and use cases of generative AI |
| Fundamentals of AI and ML | 20% | - Define artificial intelligence (AI), machine learning (ML), and deep learning - Recognize key concepts: data, models, training, inference, evaluation - Identify types of ML: supervised, unsupervised, reinforcement learning - Describe common ML workflows and lifecycle |
| Security, Compliance, and Governance for AI Solutions | 14% | - Security controls for AI data and models - Governance frameworks for AI lifecycle - Compliance requirements and regulations - Data protection and privacy in AI workflows |
| Guidelines for Responsible AI | 14% | - Describe ethical and societal impacts of AI - Identify risks and mitigation strategies for AI systems - Define responsible AI principles: fairness, transparency, privacy, safety - Explain bias detection and reduction |
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NEW QUESTION # 341
A company has terabytes of data in a database that the company can use for business analysis. The company wants to build an AI-based application that can build a SQL query from input text that employees provide.
The employees have minimal experience with technology.
Which solution meets these requirements?
Answer: A
Explanation:
Generative Pre-trained Transformers (GPT) are suitable for building an AI-based application that can generate SQL queries from natural language input provided by employees.
* GPT for Natural Language Processing:
* GPT models are designed for understanding and generating human-like text based on natural language input.
* They can be fine-tuned to interpret specific tasks, such as converting natural language queries into SQL queries.
* Why Option A is Correct:
* Natural Language Understanding: GPT is highly effective for tasks that require understanding of human language and generating structured outputs like SQL.
* User-Friendly: Requires minimal technology experience from employees, as they provide simple text input.
* Why Other Options are Incorrect:
* B. Residual neural network: Typically used in computer vision tasks, not for natural language- to-SQL conversion.
* C. Support vector machine: Used for classification tasks, not for generating structured queries from text.
* D. WaveNet: A deep generative model for audio data, unrelated to text-to-SQL tasks.
NEW QUESTION # 342
A manufacturing company wants to create product descriptions in multiple languages.
Which AWS service will automate this task?
Answer: D
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 Explanation:
* 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.
References:
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 # 343
A company wants to use foundation models (FMs) to develop and deploy an AI model.
Which AWS service or resource will meet these requirements with the LEAST development effort?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Amazon Bedrock is the managed AWS service designed specifically to develop, customize, and deploy foundation models with minimal effort.
Amazon Bedrock:
* Provides direct access to multiple foundation models
* Eliminates infrastructure management
* Supports customization, evaluation, and deployment
Why the other options are incorrect:
* SageMaker AI (B) requires more setup and ML expertise.
* PartyRock (C) is for experimentation, not deployment.
* Amazon Q Developer (D) is a productivity assistant.
AWS AI document references:
* Amazon Bedrock Overview
* Foundation Model Deployment on AWS
* Reducing Development Effort with Managed AI Services
NEW QUESTION # 344
A company trained an ML model on Amazon SageMaker to predict customer credit risk. The model shows 90% recall on training data and 40% recall on unseen testing data.
Which conclusion can the company draw from these results?
Answer: D
Explanation:
The ML model shows 90% recall on training data but only 40% recall on unseen testing data, indicating a significant performance drop. This discrepancy suggests the model has learned the training data too well, including noise and specific patterns that do not generalize to new data, which is a classic sign of overfitting.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"Overfitting occurs when a model performs well on training data but poorly on unseen test data, as it has learned patterns specific to the training set, including noise, that do not generalize. A large gap between training and testing performance metrics, such as recall, is a common indicator of overfitting." (Source: Amazon SageMaker Developer Guide, Model Evaluation and Overfitting) Detailed Option A: The model is overfitting on the training data.This is the correct answer. The significant drop in recall from 90% (training) to 40% (testing) indicates the model is overfitting, as it performs well on training data but fails to generalize to unseen data.
Option B: The model is underfitting on the training data.Underfitting occurs when the model performs poorly on both training and testing data due to insufficient learning. With 90% recall on training data, the model is not underfitting.
Option C: The model has insufficient training data.Insufficient training data could lead to poor performance, but the high recall on trainingdata (90%) suggests the model has learned the training data well, pointing to overfitting rather than a lack of data.
Option D: The model has insufficient testing data.Insufficient testing data might lead to unreliable test metrics, but it does not explain the large performance gap between training and testing, which is more indicative of overfitting.
Reference:
Amazon SageMaker Developer Guide: Model Evaluation and Overfitting (https://docs.aws.amazon.com/sagemaker/latest/dg/model-evaluation.html) AWS AI Practitioner Learning Path: Module on Model Performance and Evaluation AWS Documentation: Understanding Overfitting and Underfitting (https://aws.amazon.com/machine-learning/)
NEW QUESTION # 345
What is tokenization used for in natural language processing (NLP)?
Answer: D
Explanation:
The verified answer is C. To break text into smaller units for processing. AWS defines tokenization as the process of breaking input text into smaller units called tokens. These tokens can be words, subwords, or characters, and tokenization is used as a preprocessing step for natural language processing tasks. This exactly matches option C.
Tokenization is fundamental because machine learning and generative AI systems do not process raw human language in the same way humans read sentences. Text must first be split into units the model can represent, count, embed, and process mathematically. For example, a sentence might be split into words, subword pieces, punctuation, or characters depending on the tokenizer and model architecture. These tokens can then be converted into numerical representations for downstream tasks such as classification, summarization, question answering, translation, or text generation.
Option A is incorrect because encryption protects data confidentiality by transforming readable text into unreadable ciphertext. Tokenization in NLP is not encryption. The original purpose is linguistic and computational processing, not data protection.
Option B is incorrect because compression reduces file size. Tokenization may reduce text into units, but its purpose is not storage compression. It prepares text for analysis by NLP systems.
Option D is incorrect because translation is a downstream NLP task that converts text from one language to another. Tokenization may be used before translation, but it is not itself translation.
The wording "break text into smaller units" is the defining phrase. Therefore, the correct answer is C.
NEW QUESTION # 346
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