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| Section | Weight | Objectives |
|---|---|---|
| Responsible AI | 20% | - Implement responsible AI best practices
|
| AI Application Development | 30% | - Implement AI applications using AWS services
|
| AI/ML Fundamentals | 24% | - Understand the AI/ML lifecycle
|
| AWS AI Services Overview | 26% | - Identify appropriate AWS AI services for given scenarios
|
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NEW QUESTION # 182
Which technique breaks a complex task into smaller subtasks that are sent sequentially to a large language model (LLM)?
Answer: D
Explanation:
Prompt chaining is a technique where a complex task is broken into smaller subtasks, and the outputs of one subtask are used as inputs for the next, sequentially guiding a large language model (LLM) to solve the problem step-by-step. This method is particularly useful for complex tasks that require multiple reasoning steps.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Prompt chaining involves breaking a complex task into smaller subtasks and sequentially passing the output of one subtask as input to the next, enabling large language models to handle intricate problems by solving them step-by-step." (Source: AWS Bedrock User Guide, Prompt Engineering Techniques) Detailed Explanation:
* Option A: One-shot promptingOne-shot prompting provides a single example to guide the LLM, but it does not break tasks into smaller subtasks or handle sequential processing.
* Option B: Prompt chainingThis is the correct answer. Prompt chaining divides a complex task into smaller, manageable subtasks, solving them sequentially with the LLM, as described.
* Option C: Tree of thoughtsTree of thoughts involves exploring multiple reasoning paths simultaneously, not breaking tasks into sequential subtasks.
* Option D: Retrieval Augmented Generation (RAG)RAG retrieves external information to augment LLM responses but does not specifically break tasks into sequential subtasks.
References:
AWS Bedrock User Guide: Prompt Engineering Techniques (https://docs.aws.amazon.com/bedrock/latest
/userguide/prompt-engineering.html)
AWS AI Practitioner Learning Path: Module on Generative AI Prompting
Amazon Bedrock Developer Guide: Advanced Prompting Strategies (https://aws.amazon.com/bedrock/)
NEW QUESTION # 183
Which option is a disadvantage of using generative AI models in production systems?
Answer: B
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 # 184
A food service company wants to develop an ML model to help decrease daily food waste and increase sales revenue. The company needs to continuously improve the model's accuracy.
Which solution meets these requirements?
Answer: B
Explanation:
Amazon SageMaker is AWS's fully managed ML service that supports retraining and deploying models with new, recent data for continuous improvement. This directly meets the requirement to iterate and continuously improve model accuracy.
A is correct:
"Amazon SageMaker enables teams to retrain models using the most recent data, ensuring ongoing improvement in model accuracy." (Reference: Amazon SageMaker Overview) B (Amazon Personalize) is for recommendations, not general ML or waste reduction.
C (CloudWatch) is for monitoring, not ML training or deployment.
D (Rekognition) is for image/video analysis.
NEW QUESTION # 185
An ML research team develops custom ML models. The model artifacts are shared with other teams for integration into products and services. The ML team retains the model training code and dat a. The ML team wants to builk a mechanism that the ML team can use to audit models.
Which solution should the ML team use when publishing the custom ML models?
Answer: B
Explanation:
The ML research team needs a mechanism to audit custom ML models while sharing model artifacts with other teams. Amazon SageMaker Model Cards provide a structured way todocument model details, including intended uses, training data, and inference performance, making them ideal for auditing and ensuring transparency when publishing models.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"Amazon SageMaker Model Cards enable you to document critical details about your machine learning models, such as intended uses, training data, evaluation metrics, and inference details. Model Cards support auditing by providing a centralized record that can be reviewed by teams to understand model behavior and limitations." (Source: Amazon SageMaker Developer Guide, SageMaker Model Cards) Detailed Option A: Create documents with the relevant information. Store the documents in Amazon S3.While storing documents in S3 is feasible, it lacks the structured format and integration with SageMaker that Model Cards provide, making it less suitable for auditing purposes.
Option B: Use AWS AI Service Cards for transparency and understanding models.AWS AI Service Cards are not a standard feature in AWS documentation. This option appears to be a distractor and is not a valid solution.
Option C: Create Amazon SageMaker Model Cards with Intended uses and training and inference details.This is the correct answer. SageMaker Model Cards are specifically designed to document model details for auditing, transparency, and collaboration, meeting the team's requirements.
Option D: Create model training scripts. Commit the model training scripts to a Git repository.Sharing training scripts in a Git repository provides access to code but does not offer a structured auditing mechanism for model details like intended uses or inference performance.
Reference:
Amazon SageMaker Developer Guide: SageMaker Model Cards (https://docs.aws.amazon.com/sagemaker/latest/dg/model-cards.html) AWS AI Practitioner Learning Path: Module on Model Governance and Auditing AWS Documentation: Responsible AI with SageMaker (https://aws.amazon.com/sagemaker/)
NEW QUESTION # 186
A research company implemented a chatbot by using a foundation model (FM) from Amazon Bedrock. The chatbot searches for answers to questions from a large database of research papers.
After multiple prompt engineering attempts, the company notices that the FM is performing poorly because of the complex scientific terms in the research papers.
How can the company improve the performance of the chatbot?
Answer: A
Explanation:
Domain adaptation fine-tuning involves training a foundation model (FM) further using a specific dataset that includes domain-specific terminology and content, such as scientific terms in research papers. This process allows the model to better understand and handle complex terminology, improving its performance on specialized tasks.
* Option B (Correct): "Use domain adaptation fine-tuning to adapt the FM to complex scientific terms": This is the correct answer because fine-tuning the model on domain-specific data helps it learn and adapt to the specific language and terms used in the research papers, resulting in better performance.
* Option A: "Use few-shot prompting to define how the FM can answer the questions" is incorrect because while few-shot prompting can help in certain scenarios, it is less effective than fine-tuning for handling complex domain-specific terms.
* Option C: "Change the FM inference parameters" is incorrect because adjusting inference parameters will not resolve the issue of the model's lack of understanding of complex scientific terminology.
* Option D: "Clean the research paper data to remove complex scientific terms" is incorrect because removing the complex terms would result in the loss of important information and context, which is not a viable solution.
AWS AI Practitioner References:
* Domain Adaptation in Amazon Bedrock: AWS recommends fine-tuning models with domain- specific data to improve their performance on specialized tasks involving unique terminology.
NEW QUESTION # 187
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