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NEW QUESTION # 204
A company wants to improve the accuracy of the responses from a generative AI application. The application uses a foundation model (FM) on Amazon Bedrock.
Which solution meets these requirements MOST cost-effectively?
Answer: B
Explanation:
The company wants to improve the accuracy of a generative AI application using a foundation model (FM) on Amazon Bedrock in the most cost-effective way. Prompt engineering involves optimizing the input prompts to guide the FM to produce more accurate responses without modifying the model itself. This approach is cost-effective because it does not require additional computational resources or training, unlike fine-tuning or retraining.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Prompt engineering is a cost-effective technique to improve the performance of foundation models. By crafting precise and context-rich prompts, users can guide the model to generate more accurate and relevant responses without the need for fine-tuning or retraining." (Source: AWS Bedrock User Guide, Prompt Engineering for Foundation Models) Detailed Option A: Fine-tune the FM.Fine-tuning involves retraining the FM on a custom dataset, which requirescomputational resources, time, and cost (e.g., for Amazon Bedrock fine-tuning jobs). It is not the most cost-effective solution.
Option B: Retrain the FM.Retraining an FM from scratch is highly resource-intensive and expensive, as it requires large datasets and significant compute power. This is not cost-effective.
Option C: Train a new FM.Training a new FM is the most expensive option, as it involves building a model from the ground up, requiring extensive data, compute resources, and expertise. This is not cost-effective.
Option D: Use prompt engineering.This is the correct answer. Prompt engineering adjusts the input prompts to improve the FM's responses without incurring additional compute costs, making it the most cost-effective solution for improving accuracy on Amazon Bedrock.
Reference:
AWS Bedrock User Guide: Prompt Engineering for Foundation Models (https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-engineering.html) AWS AI Practitioner Learning Path: Module on Generative AI Optimization Amazon Bedrock Developer Guide: Cost Optimization for Generative AI (https://aws.amazon.com/bedrock/)
NEW QUESTION # 205
A publishing company built a Retrieval Augmented Generation (RAG) based solution to give its users the ability to interact with published content. New content is published daily. The company wants to provide a near real-time experience to users.
Which steps in the RAG pipeline should the company implement by using offline batch processing to meet these requirements? (Select TWO.)
Answer: C,E
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In a RAG (Retrieval Augmented Generation) architecture, there are steps that can be optimized using offline batch processing, particularly for operations that do not require real-time updates:
A . Generation of content embeddings:When new content is published, it can be processed in batches to generate embeddings (vector representations) offline. These embeddings are then used at query time for similarity search. As new documents come in daily, batch processing is ideal for generating embeddings for all new content together.
"Content/document embeddings are typically generated offline, as this operation can be computationally expensive and does not need to happen in real-time." (Reference: AWS GenAI RAG Blog, Amazon Bedrock RAG Pattern) C . Creation of the search index:After generating the content embeddings, these are indexed in a vector database or search service. This indexing is also typically performed in batch as part of the offline pipeline.
"Building or updating the vector index is often performed as a batch operation, reflecting the latest state of the content repository." (Reference: AWS RAG Pattern Whitepaper) B, D, and E are real-time steps. Embeddings for user queries (B), retrieval of relevant content (D), and response generation (E) must be processed in real-time to provide an interactive experience.
Reference:
Retrieval Augmented Generation (RAG) on AWS
Amazon Bedrock RAG Documentation
NEW QUESTION # 206
A financial company is developing a generative AI application for loan approval decisions. The company needs the application output to be responsible and fair.
Which solution meets these requirements?
Answer: D
Explanation:
The correct answer is A because ensuring responsibility and fairness in ML begins with bias detection in the training data. Including a balanced representation of all demographics ensures the model learns fairly across different groups, which is critical in regulated industries like finance.
From AWS documentation:
"A key principle of responsible AI is building models that do not propagate or amplify bias. Fairness begins with training data. Reviewing and augmenting data for representation is essential." Explanation of other options:
B . The number of hidden layers doesn't inherently improve fairness or responsibility.
C . Keeping decisions opaque violates explainability principles in responsible AI.
D . A static dataset can become outdated and may not reflect real-world shifts, which limits fairness assessment over time.
Referenced AWS AI/ML Documents and Study Guides:
Amazon SageMaker Clarify Documentation - Bias Detection and Explainability AWS Responsible AI Guidelines AWS ML Specialty Study Guide - Fairness and Governance
NEW QUESTION # 207
A company wants to make a chatbot to help customers. The chatbot will help solve technical problems without human intervention.
The company chose a foundation model (FM) for the chatbot. The chatbot needs to produce responses that adhere to company tone.
Which solution meets these requirements?
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Prompt engineering is the primary method for controlling tone, style, and behavior of foundation model responses.
AWS generative AI guidance explains that:
* Prompts can define tone, voice, and response structure
* Iterative refinement ensures consistent outputs
* Prompt refinement requires no model retraining
Why the other options are incorrect:
* Token limits (A) affect length, not tone.
* Batch inferencing (B) affects processing mode, not response style.
* Higher temperature (D) increases randomness, reducing consistency.
AWS AI document references:
* Prompt Engineering Best Practices
* Controlling Model Output Tone
* Using Prompts with Foundation Models on AWS
NEW QUESTION # 208
A company is building an AI application to summarize books of varying lengths. During testing, the application fails to summarize some books. Why does the application fail to summarize some books?
Answer: D
Explanation:
Comprehensive and Detailed
Foundation models have a context window (max tokens), which limits the size of the input text (prompt + instructions).
If the input (e.g., a very long book) exceeds this limit, the model cannot process it, causing failure.
Temperature (A) and Top P (C) control randomness, not input size.
Fine-tuning (B) is irrelevant to input truncation failures.
Reference:
AWS Documentation - Amazon Bedrock Model Parameters (context size limits)
NEW QUESTION # 209
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