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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 |
| Topic 2: Fundamentals of AI and ML | 20% | - Recognize key concepts: data, models, training, inference, evaluation - Define artificial intelligence (AI), machine learning (ML), and deep learning - Describe common ML workflows and lifecycle - Identify types of ML: supervised, unsupervised, reinforcement learning |
| Topic 3: Applications of Foundation Models | 28% | - Recognize tools for building and deploying generative AI solutions - Explain integration of foundation models into applications - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan - Describe use cases for text, image, video, and code generation |
| Topic 4: Fundamentals of Generative AI | 24% | - Define generative AI and foundation models - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) - Differentiate between generative AI and traditional ML - Explain capabilities and use cases of generative AI |
| Topic 5: Security, Compliance, and Governance for AI Solutions | 14% | - Data protection and privacy in AI workflows - Compliance requirements and regulations - Security controls for AI data and models - Governance frameworks for AI lifecycle |
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NEW QUESTION # 399
A company is using a foundation model (FM) to generate creative marketing slogans for various products. The company wants to reuse a standard template with common instructions when generating slogans for different products. However, the company needs to add short descriptions for each product.
Which Amazon Bedrock solution will meet these requirements?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Prompt management in Amazon Bedrock enables:
Reuse of standardized prompt templates
Parameterization of prompts with dynamic inputs
Consistent instruction application across use cases
AWS Bedrock guidance describes prompt management as the recommended solution for maintaining reusable prompt templates while injecting product-specific content.
Why the other options are incorrect:
Knowledge Bases (B) provide retrieval, not prompt templating.
Model evaluation (C) assesses quality, not generation.
Cross-region inference (D) addresses availability, not prompt reuse.
AWS AI document references:
Amazon Bedrock Prompt Management
Prompt Templates and Reusability
Managing Generative AI Prompts
NEW QUESTION # 400
A medical company is customizing a foundation model (FM) for diagnostic purposes. The company needs the model to be transparent and explainable to meet regulatory requirements.
Which solution will meet these requirements?
Answer: A
Explanation:
Amazon SageMaker Clarify provides transparency and explainability for machine learning models by generating metrics, reports, and examples that help to understand model predictions. For a medical company that needs a foundation model to be transparent and explainable to meet regulatory requirements, SageMaker Clarify is the most suitable solution.
* Amazon SageMaker Clarify:
* It helps in identifying potential bias in the data and model, and also explains model behavior by generating feature attributions, providing insights into which features are most influential in the model's predictions.
* These capabilities are critical in medical applications where regulatory compliance often mandates transparency and explainability to ensure that decisions made by the model can be trusted and audited.
* Why Option B is Correct:
* Transparency and Explainability: SageMaker Clarify is explicitly designed to provide insights into machine learning models' decision-making processes, helping meet regulatory requirements by explaining why a model made a particular prediction.
* Compliance with Regulations: The tool is suitable for use in sensitive domains, such as healthcare, where there is a need for explainable AI.
* Why Other Options are Incorrect:
* A. Amazon Inspector: Focuses on security assessments, not on explainability or model transparency.
* C. Amazon Macie: Provides data security by identifying and protecting sensitive data, but does not help in making models explainable.
* D. Amazon Rekognition: Used for image and video analysis, not relevant to making models explainable.
Thus, B is the correct answer for meeting transparency and explainability requirements for the foundation model
NEW QUESTION # 401
A bank is fine-tuning a large language model (LLM) on Amazon Bedrock to assist customers with questions about their loans. The bank wants to ensure that the model does not reveal any private customer data.
Which solution meets these requirements?
Answer: A
Explanation:
The goal is to prevent a fine-tuned large language model (LLM) on Amazon Bedrock from revealing private customer data. Let's analyze the options:
A . Amazon Bedrock Guardrails: Guardrails in Amazon Bedrock allow users to define policies to filter harmful or sensitive content in model inputs and outputs. While useful for real-time content moderation, they do not address the risk of private data being embedded in the model during fine-tuning, as the model could still memorize sensitive information.
B . Remove personally identifiable information (PII) from the customer data before fine-tuning the LLM: Removing PII (e.g., names, addresses, account numbers) from the training dataset ensures that the model does not learn or memorize sensitive customer data, reducing the risk of data leakage. This is a proactive and effective approach to data privacy during model training.
C . Increase the Top-K parameter of the LLM: The Top-K parameter controls the randomness of the model's output by limiting the number of tokens considered during generation. Adjusting this parameter affects output diversity but does not address the privacy of customer data embedded in the model.
D . Store customer data in Amazon S3. Encrypt the data before fine-tuning the LLM: Encrypting data in Amazon S3 protects data at rest and in transit, but during fine-tuning, the data is decrypted and used to train the model. If PII is present, the model could still learn and potentially expose it, so encryption alone does not solve the problem.
Exact Extract Reference: AWS emphasizes data privacy in AI/ML workflows, stating, "To protect sensitive data, you can preprocess datasets to remove personally identifiable information (PII) before using them for model training. This reduces the risk of models inadvertently learning or exposing sensitive information." (Source: AWS Best Practices for Responsible AI, https://aws.amazon.com/machine-learning/responsible-ai/). Additionally, the Amazon Bedrock documentation notes that users are responsible for ensuring compliance with data privacy regulations during fine-tuning (https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization.html).
Removing PII before fine-tuning is the most direct and effective way to prevent the model from revealing private customer data, making B the correct answer.
Reference:
AWS Bedrock Documentation: Model Customization (https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization.html) AWS Responsible AI Best Practices (https://aws.amazon.com/machine-learning/responsible-ai/) AWS AI Practitioner Study Guide (emphasis on data privacy in LLM fine-tuning)
NEW QUESTION # 402
A company is developing an ML model to support the company's retail application. The company wants to use information that the model has produced from previous tasks to increase the learning speed of the model.
Which model training solution will meet these requirements?
Answer: A
Explanation:
Transfer learning is a machine learning technique that reuses knowledge learned from previous tasks to improve training efficiency and performance on new tasks. AWS documentation explains that transfer learning allows models to start from pretrained weights or representations, reducing training time and the amount of data required.
In this retail application scenario, the company wants to leverage information from prior tasks to increase learning speed, which is a defining characteristic of transfer learning. AWS emphasizes that transfer learning is especially effective when tasks are related, such as customer behavior analysis, product recommendations, or demand forecasting.
By initializing a model with learned features from an existing task, transfer learning enables faster convergence and improved accuracy compared to training from scratch. AWS frequently recommends this approach when computational efficiency and rapid iteration are important.
The other options do not satisfy the requirement. Supervised learning defines how labels are used but does not reuse prior knowledge. Hyperparameter tuning optimizes model configuration but does not leverage previous task outputs. Regularization techniques reduce overfitting but do not accelerate learning through knowledge reuse.
AWS documentation positions transfer learning as a foundational concept in modern ML workflows, particularly for retail, personalization, and natural language processing use cases. Therefore, transfer learning is the correct solution.
NEW QUESTION # 403
A company is developing an ML model to make loan approvals. The company must implement a solution to detect bias in the model. The company must also be able to explain the model's predictions.
Which solution will meet these requirements?
Answer: C
Explanation:
Amazon SageMaker Clarify provides built-in tools to detect bias in data and models, and to generate detailed explainability reports for model predictions, including SHAP values and feature importance.
A is correct:
"Amazon SageMaker Clarify provides bias detection, explainability for ML models, and comprehensive reports to satisfy regulatory and ethical requirements." (Reference: Amazon SageMaker Clarify Overview)
"Amazon SageMaker Clarify provides bias detection, explainability for ML models, and comprehensive reports to satisfy regulatory and ethical requirements." (Reference: Amazon SageMaker Clarify Overview) B (Data Wrangler) is for data preparation, not bias/explainability.
C (Model Cards) document models, but don't detect bias or explain predictions.
D (AI Service Cards) provide transparency for AWS AI services, not custom model explainability.
NEW QUESTION # 404
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