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NEW QUESTION # 73
A company wants to fine-tune an ML model that is hosted on Amazon Bedrock. The company wants to use its own sensitive data that is stored in private databases in a VPC. The data needs to stay within the company's private network.
Which solution will meet these requirements?
Answer: D
Explanation:
The company wants to fine-tune an ML model on Amazon Bedrock using sensitive data stored in private databases within a VPC, ensuring the data remains within its private network. AWS PrivateLink provides a secure, private connection between a VPC and AWS services like Amazon Bedrock, allowing data to stay within the company's network without traversing the public internet. This meets the requirement for maintaining data privacy during fine-tuning.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"AWS PrivateLink enables you to securely connect your VPC to Amazon Bedrock without exposing data to the public internet. This is particularly useful for fine-tuning models with sensitive data, as it ensures that data remains within your private network." (Source: AWS Bedrock User Guide, Security and Networking) Detailed Explanation:
* Option A: Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) service role.While IAM service roles control access to Amazon Bedrock, they do not address the requirement of keeping data within the private network during data transfer. This option is insufficient.
* Option B: Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) resource policy.IAM resource policies define permissions for Bedrock resources but do not ensure that data stays within the private network. This option is incorrect.
* Option C: Use AWS PrivateLink to connect the VPC and Amazon Bedrock.This is the correct answer. AWS PrivateLink creates a secure, private connection between the VPC and Amazon Bedrock, ensuring that sensitive data does not leave the private network during fine-tuning, as required.
* Option D: Use AWS Key Management Service (AWS KMS) keys to encrypt the data.While AWS KMS can encrypt data, encryption alone does not guarantee that data remains within the private network during transfer. This option does not fully meet the requirement.
References:
AWS Bedrock User Guide: Security and Networking (https://docs.aws.amazon.com/bedrock/latest/userguide
/security.html)
AWS Documentation: AWS PrivateLink (https://aws.amazon.com/privatelink/) AWS AI Practitioner Learning Path: Module on Security and Networking for AI/ML Services
NEW QUESTION # 74
A retail company wants to build an ML model to recommend products to customers. The company wants to build the model based on responsible practices. Which practice should the company apply when collecting data to decrease model bias?
Answer: A
Explanation:
The retail company wants to build an ML model for product recommendations using responsible practices to decrease model bias. Collecting balanced and diverse data ensures the model does not favor specific groups, reducing bias and promoting fairness, a key responsible AI practice.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"To reduce model bias, it is critical to collect balanced and diverse data that represents various demographics and user groups. This practice ensures fairness and prevents the model from disproportionately favoring certain populations." (Source: AWS AI Practitioner Learning Path, Module on Responsible AI) Detailed Explanation:
Option A: Use data from only customers who match the demography of the company's overall customer base.
Limiting data to a specific demographic may reinforce existing biases, failing to address underrepresented groups and increasing bias.
Option B: Collect data from customers who have a past purchase history.Focusing only on customers with purchase history may exclude new users, potentially introducing bias, and does not address diversity.
Option C: Ensure that the data is balanced and collected from a diverse group.This is the correct answer. A balanced and diverse dataset reduces bias by ensuring the model learns from a representative sample, aligning with responsible AI practices.
Option D: Ensure that the data is from a publicly available dataset.Public datasets may not be diverse or representative of the company's customer base and could introduce unrelated biases, failing to address fairness.
References:
AWS AI Practitioner Learning Path: Module on Responsible AI
Amazon SageMaker Developer Guide: Bias and Fairness in ML (https://docs.aws.amazon.com/sagemaker
/latest/dg/clarify-bias.html)
AWS Documentation: Responsible AI Practices (https://aws.amazon.com/machine-learning/responsible-ai/)
NEW QUESTION # 75
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 # 76
A company wants to customize a foundation model (FM). The company wants to understand the customization methods and data types that are available.
Select the correct customization method from the following list for each description. Select each customization method one time. (Select THREE.) Customization methods:
* Continued pre-training
* Distillation
* Fine-tuning
Answer:
Explanation:
Explanation:
Provide labeled data to customize a model to improve performance on specific tasks.
answer: Fine-tuning
Comprehensive and Detailed Explanation (AWS AI documents):
AWS generative AI guidance defines fine-tuning as the process of adapting a pre-trained foundation model using labeled, task-specific data. Fine-tuning adjusts the model's parameters so it performs better on a particular task, such as classification, summarization, or domain-specific reasoning.
Fine-tuning is commonly used when:
* High-quality labeled data is available
* The goal is to improve accuracy on a specific task
* The base FM already has strong general capabilities
AWS AI Study Guide References:
* AWS foundation model customization methods
* AWS fine-tuning concepts for generative AI
Provide unlabeled data to customize a foundation model for a specific domain.
answer: Continued pre-training
Comprehensive and Detailed Explanation (AWS AI documents):
AWS documentation describes continued pre-training as extending the training of a foundation model using large volumes of unlabeled, domain-specific data. This method helps the model better understand domain vocabulary, structure, and context without requiring labeled datasets.
Continued pre-training is useful when:
* Large amounts of unlabeled domain data are available
* The goal is to improve domain understanding rather than a single task
* Labeling data would be expensive or impractical
AWS AI Study Guide References:
* AWS generative AI training lifecycle
* AWS guidance on domain adaptation using unlabeled data
Transfer knowledge from a larger and more intelligent model to a smaller model.
answer: Distillation
Comprehensive and Detailed Explanation (AWS AI documents):
AWS generative AI materials define distillation as a technique where a smaller model (student) learns to replicate the behavior of a larger, more capable model (teacher). The goal is to retain most of the performance while reducing model size, cost, and inference latency.
Distillation is commonly used to:
* Reduce operational costs
* Improve inference speed
* Deploy models to resource-constrained environments
AWS AI Study Guide References:
* AWS model optimization techniques
* AWS knowledge distillation concepts
NEW QUESTION # 77
A company is working on a large language model (LLM) and noticed that the LLM's outputs are not as diverse as expected. Which parameter should the company adjust?
Answer: A
Explanation:
The correct answer is A because temperature controls the randomness of a language model's output. A higher temperature increases diversity by making the model more likely to explore less probable tokens, while a lower temperature results in more deterministic and repetitive outputs.
From AWS documentation:
"The temperature parameter in LLMs adjusts the randomness of generated responses. Higher values (e.g., 0.8-
1.0) produce more creative and diverse output, while lower values (e.g., 0.1-0.3) make output more focused and repetitive." Explanation of other options:
B). Batch size is related to training efficiency, not output diversity.
C). Learning rate affects the training convergence rate, not inference-time output variety.
D). Optimizer type is a training configuration that influences how the model learns during training, not diversity during inference.
Referenced AWS AI/ML Documents and Study Guides:
* Amazon Bedrock - Parameter Tuning Guide
* AWS Machine Learning Specialty Guide - LLM Inference Parameters
NEW QUESTION # 78
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