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NEW QUESTION # 40
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:
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 # 41
A large retailer receives thousands of customer support inquiries about products every day. The customer support inquiries need to be processed and responded to quickly. The company wants to implement Agents for Amazon Bedrock.
What are the key benefits of using Amazon Bedrock agents that could help this retailer?
Answer: B
Explanation:
Amazon Bedrock Agents provide the capability to automate repetitive tasks and orchestrate complex workflows using generative AI models. This is particularly beneficial for customer support inquiries, where quick and efficient processing is crucial.
Option B (Correct): "Automation of repetitive tasks and orchestration of complex workflows": This is the correct answer because Bedrock Agents can automate common customer service tasks and streamline complex processes, improving response times and efficiency.
Option A: "Generation of custom foundation models (FMs) to predict customer needs" is incorrect as Bedrock agents do not create custom models.
Option C: "Automatically calling multiple foundation models (FMs) and consolidating the results" is incorrect because Bedrock agents focus on task automation rather than combining model outputs.
Option D: "Selecting the foundation model (FM) based on predefined criteria and metrics" is incorrect as Bedrock agents are not designed for selecting models.
AWS AI Practitioner References:
Amazon Bedrock Documentation: AWS explains that Bedrock Agents automate tasks and manage complex workflows, making them ideal for customer support automation.
NEW QUESTION # 42
Which scenario represents a practical use case for generative AI?
Answer: D
Explanation:
Generative AI is a type of AI that creates new content, such as text, images, or audio, often mimicking human- like outputs. A practical use case for generative AI is employing a chatbot to provide human-like responses to customer queries in real time, as it leverages the ability of large language models (LLMs) to generate natural language responses dynamically.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Generative AI enables applications like chatbots to produce human-like text responses in real time, enhancing customer support by providing natural and contextually relevant answers to user queries." (Source: AWS Bedrock User Guide, Introduction to Generative AI) Detailed Explanation:
* Option A: Using an ML model to forecast product demandForecasting product demand typically involves predictive analytics using supervised learning (e.g., regression models), not generative AI, which focuses on creating new content.
* Option B: Employing a chatbot to provide human-like responses to customer queries in real time This is the correct answer. Generative AI, particularly LLMs, is commonly used to power chatbots that generate human-like responses, making this a practical use case.
* Option C: Using an analytics dashboard to track website traffic and user behaviorAn analytics dashboard involves data visualization and analysis, not generative AI, which is about creating new content.
* Option D: Implementing a rule-based recommendation engine to suggest products to customersA rule-based recommendation engine relies on predefined rules, not generative AI. Generative AI could be used for more dynamic recommendations, but this scenario does not describe such a case.
References:
AWS Bedrock User Guide: Introduction to Generative AI (https://docs.aws.amazon.com/bedrock/latest
/userguide/what-is-bedrock.html)
AWS AI Practitioner Learning Path: Module on Generative AI Applications AWS Documentation: Generative AI Use Cases (https://aws.amazon.com/generative-ai/)
NEW QUESTION # 43
An AI practitioner performed continued pre-training on a foundation model (FM). After model deployment, the AI practitioner discovered that the model was exposing sensitive company information that was inadvertently included in the training data.
Which security risk does this scenario represent?
Answer: D
Explanation:
The verified answer is B. Data leakage. The model exposes sensitive company information because that information was inadvertently included in training data. AWS guidance warns that using sensitive data to train models can cause unintended disclosure of data from model output, including data leakage during inference. This is exactly the scenario: sensitive information entered the training process and later appeared in model output after deployment.
AWS security reference guidance for generative AI model customization identifies sensitive information disclosure as a threat where a model trained on datasets containing PII leaks sensitive information during inference. Although the question says company information rather than PII specifically, the same security principle applies: protected data included in training or continued pre-training can be memorized or reproduced by the model later.
Jailbreaking is incorrect because jailbreaking refers to attempts to bypass model safety controls through adversarial prompts. The problem here is not that a user bypassed safety controls; the sensitive information was already embedded through training data.
Contextual grounding is incorrect because grounding is a technique for improving answer accuracy by connecting model responses to trusted context, such as RAG sources. It is not a security risk.
Prompt injection is incorrect because prompt injection involves malicious input that attempts to override or manipulate model behavior at inference time. The question does not describe a malicious prompt. It describes accidental exposure from training data.
Therefore, the correct security risk is data leakage.
NEW QUESTION # 44
An animation company wants to provide subtitles for its content. Which AWS service meets this requirement?
Answer: B
Explanation:
Amazon Transcribe is the AWS service that converts speech to text, enabling the generation of subtitles (closed captions) for audio and video content automatically.
* C is correct:
"Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for developers to add speech-to-text capability to applications." This feature supports creating subtitles and transcripts for media files.
(Reference: Amazon Transcribe Overview, AWS AI Practitioner Official Study Guide)
* A (Comprehend) is for NLP/text analytics.
* B (Polly) is text-to-speech.
* D (Translate) translates text, but does not create subtitles from audio/video.
NEW QUESTION # 45
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