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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Security, Compliance, and Governance for AI Solutions | 14% | - Compliance requirements and regulations - Security controls for AI data and models - Governance frameworks for AI lifecycle - Data protection and privacy in AI workflows |
| Topic 2: Guidelines for Responsible AI | 14% | - Define responsible AI principles: fairness, transparency, privacy, safety - Explain bias detection and reduction - Identify risks and mitigation strategies for AI systems - Describe ethical and societal impacts of AI |
| Topic 3: Fundamentals of AI and ML | 20% | - Recognize key concepts: data, models, training, inference, evaluation - Describe common ML workflows and lifecycle - Define artificial intelligence (AI), machine learning (ML), and deep learning - Identify types of ML: supervised, unsupervised, reinforcement learning |
| Topic 4: 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 |
| Topic 5: Applications of Foundation Models | 28% | - Describe use cases for text, image, video, and code generation - 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 |
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NEW QUESTION # 269
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: D
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.
A). Include more diverse training data. Fine-tune the model again by using the new data:
"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.
References:
Responsible AI on AWS
Amazon SageMaker Clarify: Detecting and Mitigating Bias
AWS Certified AI Practitioner Exam Guide
NEW QUESTION # 270
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:
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 # 271
A research group wants to test different generative AI models to create research papers. The research group has defined a prompt and needs a method to assess the models' output. The research group wants to use a team of scientists to perform the output assessments.
Which solution will meet these requirements?
Answer: A
Explanation:
The correct answer is C because Amazon Bedrock's model evaluation feature allows users to compare outputs from different foundation models using human evaluation or automatic metrics. It enables the creation of structured evaluations where human reviewers (in this case, scientists) can assess model responses based on custom criteria like relevance, coherence, or accuracy.
From AWS documentation:
"Amazon Bedrock provides model evaluation capabilities that support both automatic and human evaluation. You can define custom evaluation prompts and collect assessments from reviewers to compare foundation model outputs for tasks such as summarization, text generation, and more." This solution is ideal for research workflows requiring domain experts to provide feedback on LLM-generated content.
Explanation of other options:
A . Amazon Personalize is used for building recommendation systems, not for evaluating model output.
B . Amazon Rekognition is used for analyzing images and videos (e.g., moderation, facial recognition), not textual output.
D . Amazon Comprehend provides NLP services like sentiment analysis, but sentiment is not sufficient for full quality evaluation of research paper generation.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Developer Guide - Model Evaluation Overview
AWS Generative AI Best Practices
AWS ML Specialty Study Guide - Evaluation and Feedback Loops in LLMs
NEW QUESTION # 272
A company wants to create a chatbot by using a foundation model (FM) on Amazon Bedrock. The FM needs to access encrypted data that is stored in an Amazon S3 bucket.
The data is encrypted with Amazon S3 managed keys (SSE-S3).
The FM encounters a failure when attempting to access the S3 bucket data.
Which solution will meet these requirements?
Answer: D
NEW QUESTION # 273
What does an F1 score measure in the context of foundation model (FM) performance?
Answer: A
Explanation:
The F1 score is the harmonic mean of precision and recall, making it a balanced metric for evaluating model performance when there is an imbalance between false positives and false negatives. Speed, cost, and energy efficiency are unrelated to the F1 score. References: AWS Foundation Models Guide.
NEW QUESTION # 274
......
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