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問題 #216
A retail company is tagging its product inventory. A tag is automatically assigned to each product based on the product description. The company created one product category by using a large language model (LLM) on Amazon Bedrock in few-shot learning mode.
The company collected a labeled dataset and wants to scale the solution to all product categories.
Which solution meets these requirements?
答案：D
解題說明：
When you have a labeled dataset and need to scale a generative AI solution for more complex or diverse product categories, fine-tuning the foundation model with your dataset is the best approach for consistent, accurate tagging.
D is correct:
"Fine-tuning a foundation model with your labeled data allows the model to generalize to new categories and improve tagging accuracy for your inventory." (Reference: Amazon Bedrock Fine-Tuning, AWS Generative AI)
"Fine-tuning a foundation model with your labeled data allows the model to generalize to new categories and improve tagging accuracy for your inventory." (Reference: Amazon Bedrock Fine-Tuning, AWS Generative AI) A (zero-shot) and B (prompt templates) do not leverage the labeled data or scale as accurately.
C (continued pre-training) uses unlabeled data, not labeled.
問題 #217
A company has set up a translation tool to help its customer service team handle issues from customers around the world. The company wants to evaluate the performance of the translation tool. The company sets up a parallel data process that compares the responses from the tool to responses from actual humans. Both sets of responses are generated on the same set of documents.
Which strategy should the company use to evaluate the translation tool?
答案：B
解題說明：
Comprehensive and Detailed Explanation From Exact AWS AI documents:
BLEU is a widely used metric for evaluating machine translation by comparing machine-generated translations against reference (human) translations.
In this scenario:
* Both systems are evaluated on the same dataset
* The goal is to compare model output against human output
AWS ML evaluation guidance describes BLEU as suitable for relative comparison of translation quality across systems.
Why the other options are incorrect:
* Absolute quality (A, C) is difficult to measure without human judgment.
* BERTScore (D) is more computationally complex and less standard for operational comparisons.
AWS AI document references:
* Evaluating Machine Translation Models
* NLP Model Evaluation Metrics
* Comparing AI and Human Translations
問題 #218
A company is implementing the Amazon Titan foundation model (FM) by using Amazon Bedrock. The company needs to supplement the model by using relevant data from the company's private data sources.
Which solution will meet this requirement?
答案：A
解題說明：
Creating an Amazon Bedrock knowledge base allows the integration of external or private data sources with a foundation model (FM) like Amazon Titan. This integration helps supplement the model with relevant data from the company's private data sources to enhance its responses.
Option C (Correct): "Create an Amazon Bedrock knowledge base": This is the correct answer as it enables the company to incorporate private data into the FM to improve its effectiveness.
Option A: "Use a different FM" is incorrect because it does not address the need to supplement the current model with private data.
Option B: "Choose a lower temperature value" is incorrect as it affects output randomness, not the integration of private data.
Option D: "Enable model invocation logging" is incorrect because logging does not help in supplementing the model with additional data.
AWS AI Practitioner Reference:
Amazon Bedrock and Knowledge Integration: AWS explains how creating a knowledge base allows Amazon Bedrock to use external data sources to improve the FM's relevance and accuracy.
問題 #219
A company stores customer personally identifiable information (PII) data. The company must store the PII data within the company's AWS Region.
Which aspect of governance does this describe?
答案：A
解題說明：
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Data residency refers to requirements that dictate where data is physically stored and processed, often to meet regulatory, legal, or organizational policies.
AWS governance guidance emphasizes that:
* PII data may be legally required to remain within a specific geographic region
* Customers can control data location by selecting AWS Regions
* Data residency is a core component of data governance and compliance
Why the other options are incorrect:
* Data mining (A) refers to extracting insights from data.
* Pre-training bias (C) relates to model training data, not storage location.
* Geolocation routing (D) concerns request routing, not data governance.
AWS AI document references:
* AWS Data Governance and Residency
* Responsible AI and Data Management on AWS
* Security and Compliance in AWS Regions
問題 #220
A company wants to use large language models (LLMs) with Amazon Bedrock to develop a chat interface for the company's product manuals. The manuals are stored as PDF files.
Which solution meets these requirements MOST cost-effectively?
答案：A
解題說明：
Using Amazon Bedrock with large language models (LLMs) allows for efficient utilization of AI to answer queries based on context provided in product manuals. To achieve this cost-effectively, the company should avoid unnecessary use of resources.
* Option A (Correct): "Use prompt engineering to add one PDF file as context to the user prompt when the prompt is submitted to Amazon Bedrock": This is the most cost-effective solution. By using prompt engineering, only the relevant content from one PDF file is added as context to each query. This approach minimizes the amount of data processed, which helps in reducing costs associated with LLMs' computational requirements.
* Option B: "Use prompt engineering to add all the PDF files as context to the user prompt when the prompt is submitted to Amazon Bedrock" is incorrect. Including all PDF files would increase costs significantly due to the large context size processed by the model.
* Option C: "Use all the PDF documents to fine-tune a model with Amazon Bedrock" is incorrect. Fine- tuning a model is more expensive than using prompt engineering, especially if done for multiple documents.
* Option D: "Upload PDF documents to an Amazon Bedrock knowledge base" is incorrect because Amazon Bedrock does not have a built-in knowledge base feature for directly managing and querying PDF documents.
AWS AI Practitioner References:
* Prompt Engineering for Cost-Effective AI: AWS emphasizes the importance of using prompt engineering to minimize costs when interacting with LLMs. By carefully selecting relevant context, users can reduce the amount of data processed and save on expenses.
問題 #221
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從Google Drive中免費下載最新的KaoGuTi AIF-C01 PDF版考試題庫：https://drive.google.com/open?id=1slkSIGGcaBODTNguATGEdIE2O-R_0rRa