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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Security, Compliance, and Governance for AI Solutions | 14% | - Compliance requirements and regulations - Data protection and privacy in AI workflows - Security controls for AI data and models - Governance frameworks for AI lifecycle |
| Topic 2: Guidelines for Responsible AI | 14% | - Identify risks and mitigation strategies for AI systems - Define responsible AI principles: fairness, transparency, privacy, safety - Describe ethical and societal impacts of AI - Explain bias detection and reduction |
| Topic 3: 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 |
| Topic 4: Fundamentals of Generative AI | 24% | - Explain capabilities and use cases of generative AI - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) - Define generative AI and foundation models - Differentiate between generative AI and traditional ML |
| Topic 5: Fundamentals of AI and ML | 20% | - Describe common ML workflows and lifecycle - Identify types of ML: supervised, unsupervised, reinforcement learning - Recognize key concepts: data, models, training, inference, evaluation - Define artificial intelligence (AI), machine learning (ML), and deep learning |
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NEW QUESTION # 358
A company has built a chatbot that can respond to natural language questions with images. The company wants to ensure that the chatbot does not return inappropriate or unwanted images.
Which solution will meet these requirements?
Answer: D
NEW QUESTION # 359
A company wants to assess the costs that are associated with using a large language model (LLM) to generate inferences. The company wants to use Amazon Bedrock to build generative AI applications.
Which factor will drive the inference costs?
Answer: A
Explanation:
In generative AI models, such as those built on Amazon Bedrock, inference costs are driven by the number of tokens processed. A token can be as short as one character or as long as one word, and the more tokens consumed during the inference process, the higher the cost.
Option A (Correct): "Number of tokens consumed": This is the correct answer because the inference cost is directly related to the number of tokens processed by the model.
Option B: "Temperature value" is incorrect as it affects the randomness of the model's output but not the cost directly.
Option C: "Amount of data used to train the LLM" is incorrect because training data size affects training costs, not inference costs.
Option D: "Total training time" is incorrect because it relates to the cost of training the model, not the cost of inference.
AWS AI Practitioner Reference:
Understanding Inference Costs on AWS: AWS documentation highlights that inference costs for generative models are largely based on the number of tokens processed.
NEW QUESTION # 360
A media company wants to analyze viewer behavior and demographics to recommend personalized content.
The company wants to deploy a customized ML model in its production environment. The company also wants to observe if the model quality drifts over time.
Which AWS service or feature meets these requirements?
Answer: B
Explanation:
The requirement is to deploy a customized machine learning (ML) model and monitor its quality for potential drift over time in a production environment. Let's evaluate each option:
* A. Amazon Rekognition: This service is designed for image and video analysis, such as object detection, facial recognition, and text extraction. It is not suited for deploying custom ML models or monitoring model quality drift.
* B. Amazon SageMaker Clarify: This feature helps detect bias in ML models and explains model predictions. While it addresses fairness and interpretability, it does not specifically focus on monitoring model quality drift over time in production.
* C. Amazon Comprehend: This is a natural language processing (NLP) service for extracting insights from text, such as sentiment analysis or entity recognition. It does not support deploying custom ML models or monitoring model performance drift.
* D. Amazon SageMaker Model Monitor: This feature is part of Amazon SageMaker and is specifically designed to monitor ML models in production. It tracks metrics such as data drift, model drift, and performance degradation over time, alerting users when issues are detected.
Exact Extract Reference: According to the AWS documentation on Amazon SageMaker, "Amazon SageMaker Model Monitor allows you to detect and remediate data and model quality issues in production. It continuously monitors the performance of deployed models, capturing data and model predictions to detect deviations from expected behavior, such as data drift or model performance degradation." (Source: AWS SageMaker Documentation - Model Monitoring, https://docs.aws.amazon.com/sagemaker/latest/dg/model- monitor.html).
This directly aligns with the requirement to observe model quality drift, making Amazon SageMaker Model Monitor the correct choice.
:
AWS SageMaker Documentation: Model Monitoring (https://docs.aws.amazon.com/sagemaker/latest/dg
/model-monitor.html)
AWS AI Practitioner Study Guide (conceptual alignment with monitoring deployed ML models)
NEW QUESTION # 361
Which term describes the numerical representations of real-world objects and concepts that AI and natural language processing (NLP) models use to improve understanding of textual information?
Answer: A
Explanation:
Embeddings are numerical representations of objects (such as words, sentences, or documents) that capture the objects' semantic meanings in a form that AI and NLP models can easily understand. These representations help models improve their understanding of textual information by representing concepts in a continuous vector space.
* Option A (Correct): "Embeddings": This is the correct term, as embeddings provide a way for models to learn relationships between different objects in their input space, improving their understanding and processing capabilities.
* Option B: "Tokens" are pieces of text used in processing, but they do not capture semantic meanings like embeddings do.
* Option C: "Models" are the algorithms that use embeddings and other inputs, not the representations themselves.
* Option D: "Binaries" refer to data represented in binary form, which is unrelated to the concept of embeddings.
AWS AI Practitioner References:
* Understanding Embeddings in AI and NLP: AWS provides resources and tools, like Amazon SageMaker, that utilize embeddings to represent data in formats suitable for machine learning models.
NEW QUESTION # 362
What does an F1 score measure in the context of foundation model (FM) performance?
Answer: B
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 # 363
......
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