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NEW QUESTION # 383
A company uses Amazon SageMaker for its ML pipeline in a production environment. The company has large input data sizes up to 1 GB and processing times up to 1 hour. The company needs near real-time latency.
Which SageMaker inference option meets these requirements?
Answer: B
Explanation:
Real-time inference is designed to provide immediate, low-latency predictions, which is necessary when the company requires near real-time latency for its ML models. This option is optimal when there is a need for fast responses, even with large input data sizes and substantial processing times.
Option A (Correct): "Real-time inference": This is the correct answer because it supports low-latency requirements, which are essential for real-time applications where quick response times are needed.
Option B: "Serverless inference" is incorrect because it is more suited for intermittent, small-scale inference workloads, not for continuous, large-scale, low-latency needs.
Option C: "Asynchronous inference" is incorrect because it is used for workloads that do not require immediate responses.
Option D: "Batch transform" is incorrect as it is intended for offline, large-batch processing where immediate response is not necessary.
AWS AI Practitioner Reference:
Amazon SageMaker Inference Options: AWS documentation describes real-time inference as the best solution for applications that require immediate prediction results with low latency.
NEW QUESTION # 384
A company wants to create a chatbot to answer employee questions about company policies. Company policies are updated frequently. The chatbot must reflect the changes in near real time. The company wants to choose a large language model (LLM).
Answer: C
Explanation:
The correct answer is C because Retrieval-Augmented Generation (RAG) allows a large language model to provide responses based on up-to-date content from external data sources without the need to fine-tune the model.
According to the AWS Bedrock Developer Guide:
"Amazon Bedrock Knowledge Bases enables developers to augment foundation models (FMs) with company- specific data that is updated in real time or near real time. By separating retrieval from the model itself, RAG- based approaches avoid the need for frequent retraining or fine-tuning." This means a company can use a knowledge base with Amazon Bedrock to dynamically fetch the latest company policy information and feed it to the LLM in the prompt. This approach is ideal for use cases where the content (like policies) changes frequently, and latency for updates must be minimal.
Explanation of other options:
A). Fine-tuning an LLM with SageMaker is not optimal for frequently updated data. Fine-tuning involves retraining and redeploying the model, which is time-consuming and not suited for real-time updates. As stated in the SageMaker documentation:
"Fine-tuning is best used for use cases where the data changes infrequently and where highly specific model behavior is required."
B). Selecting a foundation model alone does not fulfill the real-time requirement. The FM's base knowledge is static unless augmented through additional methods like RAG.
D). Amazon Q Business is intended for workplace productivity and enterprise use but is more opinionated in structure and doesn't provide the same flexibility as a custom RAG workflow for building a tailored chatbot application. While it supports some real-time data sync features, it's not purpose-built for LLM-based chat systems with dynamic data feeds like Knowledge Bases in Bedrock.
Therefore, the most appropriate and scalable solution aligned with AWS recommendations is C.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Developer Guide - Knowledge Bases and RAG (2024 Edition) AWS Certified Machine Learning Specialty Study Guide - Generative AI Section AWS Documentation: Choosing Between Fine-Tuning and RAG for LLM Applications Amazon SageMaker Documentation - Model Tuning and Deployment Best Practices (2024)
NEW QUESTION # 385
An AI practitioner is using an Amazon SageMaker notebook to train an ML prediction model for fraud detection. The company wants the model to be accurate for an unseen dataset.
Which two characteristics does the AI practitioner want the model to have?
Answer: A
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS machine learning fundamentals emphasize the bias-variance tradeoff as a core concept in building models that generalize well to unseen data. A model that is accurate on unseen (test or production) data must balance these two properties effectively.
* Low bias means the model can capture the underlying patterns in the data and is not overly simplistic.
* Low variance means the model's predictions are stable and not overly sensitive to fluctuations or noise in the training dataset.
A model with low bias and low variance is best positioned to generalize well to new, unseen fraud data, which is critical in fraud detection systems where patterns evolve and false positives or false negatives can be costly.
Why the other options are incorrect:
* High bias leads to underfitting and poor learning of fraud patterns.
* High variance leads to overfitting, where the model performs well on training data but poorly on unseen data.
AWS AI Study Guide References:
* AWS Machine Learning concepts: bias, variance, and generalization
* AWS best practices for model training and evaluation in Amazon SageMaker
NEW QUESTION # 386
A law firm wants to build an AI application by using large language models (LLMs). The application will read legal documents and extract key points from the documents.
Which solution meets these requirements?
Answer: A
NEW QUESTION # 387
Which functionality does Amazon SageMaker Clarify provide?
Answer: D
NEW QUESTION # 388
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