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| Section | Weight | Objectives |
|---|---|---|
| Applications of Foundation Models | 28% | - Describe use cases for text, image, video, and code generation - 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 |
| Security, Compliance, and Governance for AI Solutions | 14% | - Security controls for AI data and models - Compliance requirements and regulations - Data protection and privacy in AI workflows - Governance frameworks for AI lifecycle |
| Fundamentals of AI and ML | 20% | - Recognize key concepts: data, models, training, inference, evaluation - Define artificial intelligence (AI), machine learning (ML), and deep learning - Identify types of ML: supervised, unsupervised, reinforcement learning - Describe common ML workflows and lifecycle |
| Fundamentals of Generative AI | 24% | - 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) - Define generative AI and foundation models |
| Guidelines for Responsible AI | 14% | - Define responsible AI principles: fairness, transparency, privacy, safety - Identify risks and mitigation strategies for AI systems - Describe ethical and societal impacts of AI - Explain bias detection and reduction |
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NEW QUESTION # 84
A company has developed an ML model for image classification. The company wants to deploy the model to production so that a web application can use the model.
The company needs to implement a solution to host the model and serve predictions without managing any of the underlying infrastructure.
Which solution will meet these requirements?
Answer: A
Explanation:
Amazon SageMaker Serverless Inference is the correct solution for deploying an ML model to production in a way that allows a web application to use the model without the need to manage the underlying infrastructure.
* Amazon SageMaker Serverless Inference provides a fully managed environment for deploying machine learning models. It automatically provisions, scales, and manages the infrastructure required to host the model, removing the need for the company to manage servers or other underlying infrastructure.
* Why Option A is Correct:
* No Infrastructure Management: SageMaker Serverless Inference handles the infrastructure management for deploying and serving ML models. The company can simply provide the model and specify the required compute capacity, and SageMaker will handle the rest.
* Cost-Effectiveness: The serverless inference option is ideal for applications with intermittent or unpredictable traffic, as the company only pays for the compute time consumed while handling requests.
* Integration with Web Applications: This solution allows the model to be easily accessed by web applications via RESTful APIs, making it an ideal choice for hosting the model and serving predictions.
* Why Other Options are Incorrect:
* B. Use Amazon CloudFront to deploy the model: CloudFront is a content delivery network (CDN) service for distributing content, not for deploying ML models or serving predictions.
* C. Use Amazon API Gateway to host the model and serve predictions: API Gateway is used for creating, deploying, and managing APIs, but it does not provide the infrastructure or the required environment to host and run ML models.
* D. Use AWS Batch to host the model and serve predictions: AWS Batch is designed for running batch computing workloads and is not optimized for real-time inference or hosting machine learning models.
Thus, A is the correct answer, as it aligns with the requirement of deploying an ML model without managing any underlying infrastructure.
NEW QUESTION # 85
A company is building a contact center application and wants to gain insights from customer conversations.
The company wants to analyze and extract key information from the audio of the customer calls.
Which solution meets these requirements?
Answer: B
Explanation:
Amazon Transcribe is the correct solution for converting audio from customer calls into text, allowing the company to analyze and extract key information from the conversations.
* Amazon Transcribe:
* It is a fully managed automatic speech recognition (ASR) service that converts speech into text, making it easier to perform text-based analysis on audio data.
* After transcribing the audio, further analysis can be performed using other AWS services like Amazon Comprehend to extract insights such as sentiment, key phrases, or entities.
* Why Option B is Correct:
* Conversion to Text: Transcribing audio recordings is the first step in gaining insights from spoken conversations, allowing for further processing.
* Enables Further Analysis: Once the audio is transcribed into text, other tools and services can be used to analyze the content more deeply.
* Why Other Options are Incorrect:
* A. Amazon Lex: Is used for building conversational interfaces, not for transcribing or analyzing audio from customer calls.
* C. Amazon SageMaker Model Monitor: Monitors ML models for bias and data drift, not for audio analysis.
* D. Amazon Comprehend: Can analyze text but cannot transcribe audio; it would be used after transcription is completed.
NEW QUESTION # 86
A student at a university is copying content from generative AI to write essays.
Which challenge of responsible generative AI does this scenario represent?
Answer: C
NEW QUESTION # 87
An animation company wants to provide subtitles for its content. Which AWS service meets this requirement?
Answer: C
Explanation:
Amazon Transcribe is the AWS service that converts speech to text, enabling the generation of subtitles (closed captions) for audio and video content automatically.
C is correct:
"Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for developers to add speech-to-text capability to applications." This feature supports creating subtitles and transcripts for media files.
(Reference: Amazon Transcribe Overview, AWS AI Practitioner Official Study Guide) A (Comprehend) is for NLP/text analytics.
B (Polly) is text-to-speech.
D (Translate) translates text, but does not create subtitles from audio/video.
NEW QUESTION # 88
A financial company wants to build workflows for human review of ML predictions. The company wants to define confidence thresholds for its use case and adjust the threshold over time.
Which AWS service meets these requirements?
Answer: C
Explanation:
The correct answer is B because Amazon Augmented AI (Amazon A2I) allows developers to integrate human review workflows into ML systems. It supports defining confidence thresholds, such that only low- confidence predictions are sent to human reviewers.
From AWS documentation:
"Amazon A2I provides built-in human review workflows for ML predictions. You can configure confidence thresholds to determine when human review is triggered, enabling continual adjustment based on accuracy needs." This supports use cases where business decisions (like financial approvals) require manual oversight for edge cases.
Explanation of other options:
A). Amazon Personalize is used for recommendation systems, not human review.
C). Amazon Inspector is for security assessment and vulnerability scanning.
D). AWS Audit Manager is used for compliance audits, not ML prediction reviews.
Referenced AWS AI/ML Documents and Study Guides:
* Amazon A2I Developer Guide - Confidence Threshold Configuration
* AWS ML Specialty Guide - Human-in-the-Loop Systems
* AWS Responsible AI Documentation - Review and Escalation Workflows
NEW QUESTION # 89
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