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| Section | Weight | Objectives |
|---|---|---|
| Fundamentals of Generative AI | 24% | - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) - Differentiate between generative AI and traditional ML - Define generative AI and foundation models - Explain capabilities and use cases of generative AI |
| Applications of Foundation Models | 28% | - 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 - Recognize tools for building and deploying generative AI solutions |
| Guidelines for Responsible AI | 14% | - Identify risks and mitigation strategies for AI systems - Explain bias detection and reduction - Define responsible AI principles: fairness, transparency, privacy, safety - Describe ethical and societal impacts of AI |
| Fundamentals of AI and ML | 20% | - Identify types of ML: supervised, unsupervised, reinforcement learning - Describe common ML workflows and lifecycle - Define artificial intelligence (AI), machine learning (ML), and deep learning - Recognize key concepts: data, models, training, inference, evaluation |
| 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 |
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NEW QUESTION # 87
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: D
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 # 88
A bank has fine-tuned a large language model (LLM) to expedite the loan approval process. During an external audit of the model, the company discovered that the model was approving loans at a faster pace for a specific demographic than for other demographics.
How should the bank fix this issue MOST cost-effectively?
Answer: B
Explanation:
"The most effective method to reduce model bias is to curate and include diverse, representative training data, then retrain or fine-tune the model." (Reference: AWS Responsible AI, SageMaker Clarify Bias Mitigation) B (RAG) is unrelated to model fairness or bias mitigation; it's for grounding LLMs with external knowledge.
C (AWS Trusted Advisor) is for AWS resource optimization/security-not for ML model bias detection or mitigation.
D (Pre-train a new LLM) would be extremely costly and is unnecessary; fine-tuning with better data is much more efficient.
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The best practice for mitigating bias in AI/ML models, according to AWS and responsible AI frameworks, is to ensure that the training data is representative and diverse. If a model demonstrates bias (such as favoring a particular demographic), the recommended, cost-effective approach is to collect additional data from underrepresented groups and retrain (fine-tune) the model with the improved dataset.
Reference:
Responsible AI on AWS
Amazon SageMaker Clarify: Detecting and Mitigating Bias
AWS Certified AI Practitioner Exam Guide
NEW QUESTION # 89
A company manually reviews all submitted resumes in PDF format. As the company grows, the company expects the volume of resumes to exceed the company's review capacity. The company needs an automated system to convert the PDF resumes into plain text format for additional processing.
Which AWS service meets this requirement?
Answer: B
NEW QUESTION # 90
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: C
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 Reference:
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 # 91
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:
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 # 92
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