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| Section | Objectives |
|---|---|
| Topic 1: Fundamentals of Generative AI | - AWS generative AI services overview (e.g., Amazon Bedrock) - Foundation models and prompt engineering basics - Large language models (LLMs) concepts |
| Topic 2: Applications of Foundation Models | - Content generation and summarization - Use cases for generative AI in business - AI-powered assistants and automation |
| Topic 3: Responsible AI and Security | - Bias, fairness, and explainability - AI ethics and responsible use - Security, privacy, and governance in AI systems |
| Topic 4: Fundamentals of Artificial Intelligence and Machine Learning | - Core AI and ML concepts - Data fundamentals for AI/ML - Common ML workflows and lifecycle |
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NEW QUESTION # 187
A company wants to improve multiple ML models.
Select the correct technique from the following list of use cases. Each technique should be selected one time or not at all. (Select THREE.) Few-shot learning Fine-tuning Retrieval Augmented Generation (RAG) Zero-shot learning
Answer:
Explanation:
NEW QUESTION # 188
A company is training a foundation model (FM). The company wants to increase the accuracy of the model up to a specific acceptance level.
Which solution will meet these requirements?
Answer: B
Explanation:
Increasing the number of epochs during model training allows the model to learn from the data over more iterations, potentially improving its accuracy up to a certain point. This is a common practice when attempting to reach a specific level of accuracy.
* Option B (Correct): "Increase the epochs": This is the correct answer because increasing epochs allows the model to learn more from the data, which can lead to higher accuracy.
* Option A: "Decrease the batch size" is incorrect as it mainly affects training speed and may lead to overfitting but does not directly relate to achieving a specific accuracy level.
* Option C: "Decrease the epochs" is incorrect as it would reduce the training time, possibly preventing the model from reaching the desired accuracy.
* Option D: "Increase the temperature parameter" is incorrect because temperature affects the randomness of predictions, not model accuracy.
AWS AI Practitioner References:
* Model Training Best Practices on AWS: AWS suggests adjusting training parameters, like the number of epochs, to improve model performance.
NEW QUESTION # 189
A company wants to assess internet quality in remote areas of the world. The company needs to collect internet speed data and store the data in Amazon RDS. The company will analyze internet speed variation throughout each day. The company wants to create an AI model to predict potential internet disruptions.
Which type of data should the company collect for this task?
Answer: D
NEW QUESTION # 190
A financial company has offices in different countries worldwide. The company requires that all API calls between generative AI applications and foundation models (FM) must not travel across the public internet.
Which AWS service should the company use?
Answer: D
Explanation:
AWS PrivateLink provides private connectivity between VPCs, AWS services, and on-premises networks, ensuring traffic does not traverse the public internet.
A is correct:
"AWS PrivateLink provides private connectivity to services across VPCs, keeping API traffic off the public internet." (Reference: AWS PrivateLink Overview) B (Amazon Q) is a generative AI assistant, not a network security/control tool.
C (CloudFront) is a CDN, not for private API calls.
D (CloudTrail) is for logging and monitoring, not secure connectivity.
NEW QUESTION # 191
A company wants to enhance response quality for a large language model (LLM) for complex problem- solving tasks. The tasks require detailed reasoning and a step-by-step explanation process.
Which prompt engineering technique meets these requirements?
Answer: B
Explanation:
The company wants to enhance the response quality of an LLM for complex problem-solving tasks requiring detailed reasoning and step-by-step explanations. Chain-of-thought prompting encourages the LLM to break down the problem into intermediate steps, providing a clear reasoning process before arriving at the final answer, which is ideal for this requirement.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Chain-of-thought prompting improves the reasoning capabilities of large language models by encouraging them to break down complex tasks into intermediate steps, providing a step-by-step explanation that leads to the final answer. This technique is particularly effective for problem-solving tasks requiring detailed reasoning." (Source: AWS Bedrock User Guide, Prompt Engineering Techniques) Detailed Explanation:
* Option A: Few-shot promptingFew-shot prompting provides a few examples to guide the LLM but does not explicitly encourage step-by-step reasoning or detailed explanations.
* Option B: Zero-shot promptingZero-shot prompting relies on the LLM's pre-trained knowledge without examples, making it less effective for complex tasks requiring detailed reasoning.
* Option C: Directional stimulus promptingDirectional stimulus prompting is not a standard technique in AWS documentation, likely a distractor, and does not address step-by-step reasoning.
* Option D: Chain-of-thought promptingThis is the correct answer. Chain-of-thought prompting enhances response quality for complex tasks by guiding the LLM to reason step-by-step, providing detailed explanations.
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 # 192
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