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NEW QUESTION # 255
A social media company wants to use a large language model (LLM) to summarize messages. The company has chosen a few LLMs that are available on Amazon SageMaker JumpStart. The company wants to compare the generated output toxicity of these models.
Which strategy gives the company the ability to evaluate the LLMs with the LEAST operational overhead?
Answer: A
Explanation:
The least operational overhead comes from automated tools that can scan and evaluate LLM outputs for toxicity. AWS and SageMaker JumpStart support integrations with automatic evaluation tools and APIs (such as Amazon Comprehend or third-party toxicity classifiers).
* B is correct: Automated evaluation provides quick, scalable, and repeatable analysis, requiring minimal human intervention.
* A and C require manual effort, increasing operational overhead.
* D (RLHF) is resource-intensive and not designed for rapid, automated model comparison.
"Automated evaluation can quickly assess generated text for specific attributes like toxicity, sentiment, or compliance using pre-trained classifiers, reducing human involvement and operational complexity." (Reference: AWS SageMaker JumpStart Evaluation, AWS AI Practitioner Guide)
NEW QUESTION # 256
A company needs to scan its Amazon EC2-based ML infrastructure for security vulnerabilities before deploying generative AI (GenAI) models.
Which AWS service provides automated vulnerability assessment?
Answer: C
Explanation:
The verified answer is C. Amazon Inspector. The company needs automated vulnerability assessment for Amazon EC2-based infrastructure. AWS documentation states that Amazon Inspector scans EC2 instances for package vulnerabilities and network reachability issues and produces findings. It compares instance metadata against vulnerability intelligence and security advisory rules. This is exactly the service used to assess EC2 infrastructure before deploying sensitive workloads such as generative AI models.
AWS also documents that Amazon Inspector scans EC2 instances for common vulnerabilities and exposures, operating system package vulnerabilities, programming language package vulnerabilities, network exposure issues, and network reachability issues. This confirms that Inspector is the correct service for automated vulnerability assessment of EC2-based ML infrastructure.
AWS CloudFormation is incorrect because it provisions infrastructure as code. It can create EC2 instances, security groups, and related resources, but it does not perform vulnerability assessment. Amazon Comprehend is incorrect because it is a natural language processing service used to extract insights from text. It does not scan infrastructure. AWS Trusted Advisor is incorrect because it provides best-practice recommendations across categories such as cost optimization, security, performance, and service limits, but it is not the dedicated automated vulnerability assessment service for EC2 instances.
NEW QUESTION # 257
Which AWS service or feature can help an AI development team quickly deploy and consume a foundation model (FM) within the team's VPC?
Answer: C
Explanation:
Amazon SageMaker JumpStart is the correct service for quickly deploying and consuming a foundation model (FM) within a team's VPC.
* Amazon SageMaker JumpStart:
* Provides access to a wide range of pre-trained models and solutions that can be easily deployed and consumed within a VPC.
* Designed to simplify and accelerate the deployment of machine learning models, including foundation models.
* Why Option B is Correct:
* Rapid Deployment: JumpStart allows for quick deployment of models with minimal configuration, directly within a secure VPC environment.
* Ease of Use: Provides a user-friendly interface to select and deploy models, reducing the time to value.
* Why Other Options are Incorrect:
* A. Amazon Personalize: Focuses on creating personalized recommendations, not deploying foundation models.
* C. PartyRock: Not a recognized AWS service.
* D. Amazon SageMaker endpoints: Endpoints are for deploying specific models, not a feature for quickly starting with pre-trained foundation models.
NEW QUESTION # 258
A company is introducing a new feature for its application. The feature will refine the style of output messages. The company will fine-tune a large language model (LLM) on Amazon Bedrock to implement the feature. Which type of data does the company need to meet these requirements?
Answer: C
Explanation:
* Fine-tuning requires paired input-output examples to teach the model how to respond to inputs with desired styled outputs.
* Single inputs (A) or outputs (B) are insufficient.
* Separate, unpaired samples (D) don't establish the input-output mapping.
# Reference:
AWS Documentation - Preparing data for fine-tuning FMs
NEW QUESTION # 259
An AI practitioner is developing a new ML model. After training the model, the AI practitioner evaluates the accuracy of the model's predictions. The model's accuracy is low when the model uses both the training dataset and the test dataset.
Which scenario is the MOST likely cause of this problem?
Answer: A
Explanation:
Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in the training data. AWS documentation explains that an underfit model performs poorly on both training and test datasets, which directly matches the scenario described.
In this case, the model shows low accuracy during training and evaluation, indicating that it has not learned sufficient relationships from the data. AWS identifies common causes of underfitting as insufficient model complexity, inadequate feature representation, overly aggressive regularization, or insufficient training time.
Underfitting is different from overfitting. Overfitting occurs when a model performs well on training data but poorly on test data, which is not the situation here. Hallucination applies to generative AI outputs, not supervised ML model accuracy. Cross-validation is a model evaluation technique, not a cause of poor performance.
AWS emphasizes the importance of diagnosing underfitting early in the model development lifecycle. Remedies include increasing model complexity, adding relevant features, reducing regularization, or selecting a more expressive algorithm. These steps allow the model to better learn from the data and improve accuracy across both training and test sets.
AWS machine learning best practices clearly associate low performance on both datasets with underfitting, making this the most likely cause of the problem described.
NEW QUESTION # 260
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