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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Fundamentals of AI and ML | 20% | - Recognize key concepts: data, models, training, inference, evaluation - Identify types of ML: supervised, unsupervised, reinforcement learning - Define artificial intelligence (AI), machine learning (ML), and deep learning - Describe common ML workflows and lifecycle |
| Topic 2: Guidelines for Responsible AI | 14% | - Identify risks and mitigation strategies for AI systems - Define responsible AI principles: fairness, transparency, privacy, safety - Explain bias detection and reduction - Describe ethical and societal impacts of AI |
| Topic 3: Security, Compliance, and Governance for AI Solutions | 14% | - Data protection and privacy in AI workflows - Security controls for AI data and models - Compliance requirements and regulations - Governance frameworks for AI lifecycle |
| Topic 4: Applications of Foundation Models | 28% | - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan - Describe use cases for text, image, video, and code generation - Recognize tools for building and deploying generative AI solutions - Explain integration of foundation models into applications |
| Topic 5: Fundamentals of Generative AI | 24% | - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) - Explain capabilities and use cases of generative AI - Differentiate between generative AI and traditional ML - Define generative AI and foundation models |
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NEW QUESTION # 253
A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality.
Which action must the company take to use the custom model through Amazon Bedrock?
Answer: C
Explanation:
To use a custom model that has been trained to improve summarization quality, the company must deploy the model on an Amazon SageMaker endpoint. This allows the model to be used for real-time inference through Amazon Bedrock or other AWS services. By deploying the model in SageMaker, the custom model can be accessed programmatically via API calls, enabling integration with Amazon Bedrock.
* Option B (Correct): "Deploy the custom model in an Amazon SageMaker endpoint for real-time inference": This is the correct answer because deploying the model on SageMaker enables it to serve real-time predictions and be integrated with Amazon Bedrock.
* Option A: "Purchase Provisioned Throughput for the custom model" is incorrect because provisioned throughput is related to database or storage services, not model deployment.
* Option C: "Register the model with the Amazon SageMaker Model Registry" is incorrect because while the model registry helps with model management, it does not make the model accessible for real- time inference.
* Option D: "Grant access to the custom model in Amazon Bedrock" is incorrect because Bedrock does not directly manage custom model access; it relies on deployed endpoints like those in SageMaker.
AWS AI Practitioner References:
* Amazon SageMaker Endpoints: AWS recommends deploying models to SageMaker endpoints to use them for real-time inference in various applications.
NEW QUESTION # 254
An AI practitioner is using an Amazon Bedrock base model to summarize session chats from the customer service department. The AI practitioner wants to store invocation logs to monitor model input and output data.
Which strategy should the AI practitioner use?
Answer: C
Explanation:
Amazon Bedrock provides an option to enable invocation logging to capture and store the input and output data of the models used. This is essential for monitoring and auditing purposes, particularly when handling customer data.
Option B (Correct): "Enable invocation logging in Amazon Bedrock": This is the correct answer as it directly enables the logging of all model invocations, ensuring transparency and traceability.
Option A: "Configure AWS CloudTrail" is incorrect because CloudTrail logs API calls but does not provide specific logging for model inputs and outputs.
Option C: "Configure AWS Audit Manager" is incorrect as Audit Manager is used for compliance reporting, not specific invocation logging for AI models.
Option D: "Configure model invocation logging in Amazon EventBridge" is incorrect as EventBridge is for event-driven architectures, not specifically designed for logging AI model inputs and outputs.
AWS AI Practitioner Reference:
Amazon Bedrock Logging Capabilities: AWS emphasizes using built-in logging features in Bedrock to maintain data integrity and transparency in model operations.
NEW QUESTION # 255
A social media company wants to use a large language model (LLM) to summarize messages. The company has chosen a few LLMs that are available on Amazon SageMaker JumpStart. The company wants to compare the generated output toxicity of these models.
Which strategy gives the company the ability to evaluate the LLMs with the LEAST operational overhead?
Answer: B
Explanation:
The least operational overhead comes from automated tools that can scan and evaluate LLM outputs for toxicity. AWS and SageMaker JumpStart support integrations with automatic evaluation tools and APIs (such as Amazon Comprehend or third-party toxicity classifiers).
* B is correct: Automated evaluation provides quick, scalable, and repeatable analysis, requiring minimal human intervention.
* A and C require manual effort, increasing operational overhead.
* D (RLHF) is resource-intensive and not designed for rapid, automated model comparison.
"Automated evaluation can quickly assess generated text for specific attributes like toxicity, sentiment, or compliance using pre-trained classifiers, reducing human involvement and operational complexity." (Reference: AWS SageMaker JumpStart Evaluation, AWS AI Practitioner Guide)
NEW QUESTION # 256
An AI practitioner trained a custom model on Amazon Bedrock by using a training dataset that contains confidential data. The AI practitioner wants to ensure that the custom model does not generate inference responses based on confidential data.
How should the AI practitioner prevent responses based on confidential data?
Answer: B
Explanation:
When a model is trained on a dataset containing confidential or sensitive data, the model may inadvertently learn patterns from this data, which could then be reflected in its inference responses. To ensure that a model does not generate responses based on confidential data, the most effective approach is to remove the confidential data from the training dataset and then retrain the model.
Explanation of Each Option:
Option A (Correct): " Delete the custom model. Remove the confidential data from the training dataset.
Retrain the custom model. " This option is correct because it directly addresses the core issue: the model has been trained on confidential data. The only way to ensure that the model does not produce inferences based on this data is to remove the confidential information from the training dataset and then retrain the model from scratch. Simply deleting the model and retraining it ensures that no confidential data is learned or retained by the model. This approach follows the best practices recommended by AWS for handling sensitive data when using machine learning services like Amazon Bedrock.
Option B: " Mask the confidential data in the inference responses by using dynamic data masking. " This option is incorrect because dynamic data masking is typically used to mask or obfuscate sensitive data in a database. It does not address the core problem of the model beingtrained on confidential data. Masking data in inference responses does not prevent the model from using confidential data it learned during training.
Option C: " Encrypt the confidential data in the inference responses by using Amazon SageMaker. " This option is incorrect because encrypting the inference responses does not prevent the model from generating outputs based on confidential data. Encryption only secures the data at rest or in transit but does not affect the model ' s underlying knowledge or training process.
Option D: " Encrypt the confidential data in the custom model by using AWS Key Management Service (AWS KMS). " This option is incorrect as well because encrypting the data within the model does not prevent the model from generating responses based on the confidential data it learned during training. AWS KMS can encrypt data, but it does not modify the learning that the model has already performed.
AWS AI Practitioner References:
Data Handling Best Practices in AWS Machine Learning: AWS advises practitioners to carefully handle training data, especially when it involves sensitive or confidential information. This includes preprocessing steps like data anonymization or removal of sensitive data before using it to train machine learning models.
Amazon Bedrock and Model Training Security: Amazon Bedrock provides foundational models and customization capabilities, but any training involving sensitive data should follow best practices, such as removing or anonymizing confidential data to prevent unintended data leakage.
NEW QUESTION # 257
Which option is a disadvantage of using generative AI models in production systems?
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
A known limitation of generative AI models is their tendency to hallucinate, meaning they may generate plausible but incorrect or fabricated information.
AWS generative AI guidance highlights:
* Non-deterministic behavior
* Risk of incorrect outputs
* Need for validation, monitoring, and guardrails in production systems Why the other options are incorrect:
* High accuracy (A) is an advantage, not a disadvantage.
* Deterministic behavior (B) is generally not true for generative models.
* Negligible resource usage (C) is incorrect; generative models are resource-intensive.
AWS AI document references:
* Generative AI Risks and Mitigations
* Responsible Deployment of Foundation Models
* Amazon Bedrock Guardrails Guidance
NEW QUESTION # 258
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