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NEW QUESTION # 85
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: C
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 # 86
A company wants more customized responses to its generative AI models' prompts.
Select the correct customization methodology from the following list for each use case. Each use case should be selected one time. (Select THREE.)
* Continued pre-training
* Data augmentation
* Model fine-tuning
Answer:
Explanation:
Model fine-tuning adapts a pre-trained model to a specific domain or task using labeled data. This is the preferred approach when you want highly customized behavior for a particular application or subject area.
(Reference: Amazon Bedrock Fine-Tuning)
Data augmentation is used to artificially increase the size of a labeled dataset, usually by transforming or generating variations of the original data. This helps improve model generalization when labeled data is limited.
(Reference: AWS Data Preparation Techniques, AWS AI Practitioner Guide) Continued pre-training (also called domain-adaptive pre-training) further trains a foundation model on large amounts of unlabeled data from a specific domain, improving the model's language understanding or generation for that domain.
(Reference: Amazon Bedrock Customization Options)
NEW QUESTION # 87
A company wants to develop a solution that uses generative AI to create content for product advertisements, Including sample images and slogans.
Select the correct model type from the following list for each action. Each model type should be selected one time. (Select THREE.)
* Diffusion model
* Object detection model
* Transformer-based model
Answer:
Explanation:
Explanation:
Diffusion models are state-of-the-art generative models for creating high-quality, realistic images from textual prompts or other forms of conditioning. These are the foundational technology behind tools like Amazon Bedrock Titan Image Generator and other generative image models.
Reference: AWS Generative AI Overview, Diffusion Models Explained - AWS Blog Transformer-based models (such as GPT or Amazon Titan Text) are designed for generating and understanding natural language. These models can generate coherent, contextually relevant slogans based on product information.
Reference: AWS Generative AI on Bedrock, Transformers Explained - AWS
Object detection models are designed to identify and locate objects within images, which makes them suitable for verifying that specific brand elements (like logos or products) are correctly positioned in the generated content.
Reference: AWS Rekognition Object Detection, Object Detection Overview - AWS
NEW QUESTION # 88
What is tokenization used for in natural language processing (NLP)?
Answer: D
Explanation:
The verified answer is C. To break text into smaller units for processing. AWS defines tokenization as the process of breaking input text into smaller units called tokens. These tokens can be words, subwords, or characters, and tokenization is used as a preprocessing step for natural language processing tasks. This exactly matches option C.
Tokenization is fundamental because machine learning and generative AI systems do not process raw human language in the same way humans read sentences. Text must first be split into units the model can represent, count, embed, and process mathematically. For example, a sentence might be split into words, subword pieces, punctuation, or characters depending on the tokenizer and model architecture. These tokens can then be converted into numerical representations for downstream tasks such as classification, summarization, question answering, translation, or text generation.
Option A is incorrect because encryption protects data confidentiality by transforming readable text into unreadable ciphertext. Tokenization in NLP is not encryption. The original purpose is linguistic and computational processing, not data protection.
Option B is incorrect because compression reduces file size. Tokenization may reduce text into units, but its purpose is not storage compression. It prepares text for analysis by NLP systems.
Option D is incorrect because translation is a downstream NLP task that converts text from one language to another. Tokenization may be used before translation, but it is not itself translation.
The wording "break text into smaller units" is the defining phrase. Therefore, the correct answer is C.
NEW QUESTION # 89
A company wants to use a large language model (LLM) to generate product descriptions. The company wants to give the model example descriptions that follow a format.
Which prompt engineering technique will generate descriptions that match the format?
Answer: D
Explanation:
The correct answer is D because Few-shot prompting involves providing the LLM with a few examples of the expected input-output format. This helps the model learn and mimic the pattern or structure required in the response - such as generating product descriptions that follow a specific template.
From AWS documentation:
"Few-shot prompting helps guide the model to produce structured and domain-specific outputs by supplying a small number of example inputs and corresponding outputs." Explanation of other options:
A). Zero-shot prompting provides no examples, which may lead to inconsistent formatting.
B). Chain-of-thought prompting is used to guide reasoning steps, not formatting.
C). One-shot prompting uses a single example, but few-shot typically yields better structure adherence.
Referenced AWS AI/ML Documents and Study Guides:
* AWS Prompt Engineering Guide
* Amazon Bedrock Developer Documentation - Prompting Techniques
* AWS ML Specialty Study Guide - LLM Prompting Patterns
NEW QUESTION # 90
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