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NEW QUESTION # 127
A fitness company has an application that uses LLMs to create new personalized exercise routines for users. The company generates the routines every week for all users in the company's database.
The company wants to reduce costs for this repetitive workload. The workload processes large volumes of requests and does not require immediate responses.
Which solution will meet these requirements?
Answer: D
Explanation:
The verified answer is C. Use batch inference with Amazon Bedrock. The workload has three decisive characteristics: it is repetitive, it processes a large volume of requests, and it does not require immediate responses. AWS documentation states that with Amazon Bedrock batch inference, users can submit multiple prompts and generate responses asynchronously. It also states that batch inference helps process a large number of requests efficiently by sending a single request and generating responses in an Amazon S3 bucket. That directly matches the weekly generation of exercise routines for all users.
AWS also explains that after the batch job is complete, the output files can be retrieved from Amazon S3, and batch inference can improve model inference performance on large datasets. This is the right architecture for offline or scheduled workloads where immediate response latency is not required.
Amazon Bedrock AgentCore is incorrect because AgentCore is for building, running, and managing agents, tools, memory, gateways, and agent workflows. The problem is not agent orchestration; it is cost-efficient high-volume offline inference.
Real-time inference with Amazon Bedrock on-demand endpoints is incorrect because real-time inference is better for interactive applications where users need immediate responses. The question explicitly says immediate responses are not required.
Real-time inference with SageMaker AI hosted endpoints is also incorrect for the same reason. Hosted endpoints are appropriate for low-latency inference requests, but this workload is scheduled and large-volume.
Therefore, Amazon Bedrock batch inference is the best solution.
NEW QUESTION # 128
A company plans to use a generative AI model to provide real-time service quotes to users.
Which criteria should the company use to select the correct model for this use case?
Answer: C
Explanation:
The correct answer is D because low latency and optimized inference speed are critical for real-time applications. For delivering real-time service quotes, the system must respond in milliseconds or a few seconds at most, making latency a primary concern when choosing the model.
From AWS Bedrock documentation:
"When selecting a foundation model for real-time applications, inference speed and latency are key evaluation metrics to ensure responsive user experiences." Explanation of other options:
A). Model size affects performance and cost but doesn't directly guarantee low latency.
B). Training data quality is important for accuracy, but it doesn't address real-time performance requirements.
C). GPU availability matters in infrastructure planning, not in model selection for latency optimization.
Referenced AWS AI/ML Documents and Study Guides:
* Amazon Bedrock Model Selection Guide - Real-time Use Case Considerations
* AWS ML Specialty Guide - Foundation Model Performance Criteria
NEW QUESTION # 129
A company is using a pre-trained large language model (LLM) to build a chatbot for product recommendations. The company needs the LLM outputs to be short and written in a specific language.
Which solution will align the LLM response quality with the company's expectations?
Answer: B
Explanation:
Adjusting the prompt is the correct solution to align the LLM outputs with the company's expectations for short, specific language responses.
Adjust the Prompt:
Modifying the prompt can guide the LLM to produce outputs that are shorter and tailored to the desired language.
A well-crafted prompt can provide specific instructions to the model, such as "Answer in a short sentence in Spanish." Why Option A is Correct:
Control Over Output: Adjusting the prompt allows for direct control over the style, length, and language of the LLM outputs.
Flexibility: Prompt engineering is a flexible approach to refining the model's behavior without modifying the model itself.
Why Other Options are Incorrect:
B: Choose an LLM of a different size: The model size does not directly impact the response length or language.
C: Increase the temperature: Increases randomness in responses but does not ensure brevity or specific language.
D: Increase the Top K value: Affects diversity in model output but does not align directly with response length or language specificity.
NEW QUESTION # 130
A manufacturing company wants to create product descriptions in multiple languages.
Which AWS service will automate this task?
Answer: A
Explanation:
The manufacturing company needs to create product descriptions in multiple languages, which requires automated language translation. Amazon Translate is a fully managed service that uses machine learning to provide high-quality translation between languages, making it the ideal solution for this task.
Exact Extract from AWS AI Documents:
From the Amazon Translate Developer Guide:
"Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. It can be used to automatically translate text, such as product descriptions, into multiple languages to reach a global audience." (Source: Amazon Translate Developer Guide, Introduction to Amazon Translate) Detailed Option A: Amazon TranslateThis is the correct answer. Amazon Translate automates the translation of text into multiple languages, directly addressing the company's need to create product descriptions in different languages.
Option B: Amazon TranscribeAmazon Transcribe converts speech to text, which is unrelated to translating text into multiple languages. This option is incorrect.
Option C: Amazon KendraAmazon Kendra is an intelligent search service that uses machine learning to provide answers from documents, not for translating text. This option is irrelevant.
Option D: Amazon PollyAmazon Polly is a text-to-speech service that generates spoken audio from text, not for translating text into other languages. This option does not meet the requirements.
Reference:
Amazon Translate Developer Guide: Introduction to Amazon Translate (https://docs.aws.amazon.com/translate/latest/dg/what-is.html) AWS AI Practitioner Learning Path: Module on Natural Language Processing Services AWS Documentation: Language Translation with Amazon Translate (https://aws.amazon.com/translate/)
NEW QUESTION # 131
What is tokenization used for in natural language processing (NLP)?
Answer: B
Explanation:
The correct answer is C because tokenization is the NLP process of breaking down text into smaller units, such as words, subwords, or characters, that can be processed by ML models.
From AWS documentation:
"Tokenization is the process of splitting text into meaningful units, such as words or subwords, that are used as input tokens in NLP tasks. Tokenization is an essential step in preparing text data for models." This is a foundational concept used in language models, including those on Amazon Bedrock and SageMaker.
Explanation of other options:
A). Encryption is not related to NLP and is used for data security.
B). Compression reduces file size but is unrelated to language processing.
D). Translation is a separate NLP task that uses tokenization as a preprocessing step but is not the definition of tokenization itself.
Referenced AWS AI/ML Documents and Study Guides:
* AWS NLP on Amazon SageMaker Documentation
* AWS Machine Learning Specialty Guide - NLP Fundamentals
* Amazon Bedrock Foundation Model Documentation - Tokenization and Input Formatting
NEW QUESTION # 132
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