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NEW QUESTION # 342
Which term is the speed at which a pre-trained foundation model (FM) processes requests and delivers output?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Inference latency measures the time it takes for a model to:
* Receive an input request
* Process the request
* Return an output
AWS performance guidance emphasizes inference latency as a critical metric for real-time and user-facing AI applications.
Why the other options are incorrect:
* Model size (A) refers to number of parameters.
* Context window (C) defines input length capacity.
* Fine-tuning (D) is a customization process.
AWS AI document references:
* Foundation Model Performance Metrics
* Latency Considerations for AI Applications
* Optimizing Inference on AWS
NEW QUESTION # 343
Which type of AI model makes numeric predictions?
Answer: D
Explanation:
The correct answer is regression. In machine learning, regression models are designed to predict continuous numerical values based on input features. Common use cases include predicting house prices, sales forecasting, temperature trends, or medical risk scores. According to AWS SageMaker documentation, regression tasks fall under supervised learning where the output is a real-valued number rather than a class label. For instance, linear regression is one of the most commonly used models for predicting a single continuous output. By contrast, diffusion models are typically used in generative image tasks, transformers are architectures (not specific to numeric output), and multi-modal models process various data types like text, images, and audio. Only regression models are purpose-built for making precise numeric predictions, which aligns with AWS best practices when the output is a quantity, not a category.
Referenced AWS AI/ML Documents and Study Guides:
AWS Machine Learning Specialty Guide - Regression Models
Amazon SageMaker Built-in Algorithms - Linear Learner (Regression and Classification)
NEW QUESTION # 344
A company is building a generative Al application and is reviewing foundation models (FMs). The company needs to consider multiple FM characteristics.
Select the correct FM characteristic from the following list for each definition. Each FM characteristic should be selected one time. (Select THREE.) Concurrency Context windows Latency
Answer:
Explanation:
Explanation:
AWS References:
Amazon Bedrock - Model parameters and context window
AWS ML Inference - Latency and Throughput
AWS Scalability - Concurrency
NEW QUESTION # 345
A company needs to automate recurring compliance assessments for its AI workloads. The assessments must include documented evidence mapped to regulatory frameworks.
Which AWS service meets these requirements?
Answer: A
Explanation:
The verified answer is A. AWS Audit Manager. The requirement is recurring compliance assessments with documented evidence mapped to regulatory frameworks. AWS documentation states that AWS Audit Manager helps customers continually audit AWS usage to simplify risk and compliance management. It automates evidence collection so customers can assess whether policies, procedures, and controls are operating effectively. AWS also states that Audit Manager provides prebuilt frameworks that structure and automate assessments for compliance standards and regulations.
The key phrase is "documented evidence mapped to regulatory frameworks." AWS Audit Manager creates assessments from frameworks, automatically runs resource assessments, collects data from in-scope AWS accounts, transforms that data into audit-friendly evidence, and attaches it to relevant controls. That is the exact service function being tested. AWS also explains that Audit Manager can generate reports for auditors and supports prebuilt frameworks for standards such as PCI DSS, GDPR, HIPAA, SOC 2, and AWS operational best practices.
AWS Trusted Advisor is incorrect because Trusted Advisor provides recommendations for cost optimization, performance, security, fault tolerance, service quotas, and operational excellence. It is not a compliance- evidence automation service.
AWS Secrets Manager is incorrect because it stores, rotates, and manages secrets such as database credentials and API keys. It does not automate compliance assessments.
Amazon Inspector is incorrect because it scans workloads for software vulnerabilities and unintended network exposure. It is security-focused, but it does not create recurring evidence-mapped compliance assessments.
Therefore, the correct service is AWS Audit Manager.
NEW QUESTION # 346
A company deploys a custom ML model on Amazon SageMaker AI. The company uses the model to build a generative AI application for a healthcare recommendation system.
The company tests the application and finds a potential bias issue. The application consistently recommends different treatment approaches for patients who have identical medical conditions based on patient demographic information.
The company needs a solution to ensure that the application does not generate biased recommendations.
Which solution will meet this requirement?
Answer: C
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS Responsible AI best practices emphasize that bias should be detected, measured, mitigated, and monitored throughout the ML lifecycle, especially for sensitive domains such as healthcare. When biased outcomes are observed, AWS guidance recommends addressing bias at the data and model level, not only at the output level.
Using Amazon SageMaker Clarify aligns directly with AWS Responsible AI principles because it is designed to:
* Detect and quantify bias in datasets and model predictions across sensitive attributes such as demographic groups
* Provide pre-training and post-training bias metrics, allowing practitioners to identify where bias originates
* Support data-centric mitigation, including improving dataset balance and representativeness After identifying bias with SageMaker Clarify, collecting additional balanced training data and retraining the model helps ensure that:
* The model learns from a more representative dataset
* Disparate treatment recommendations based on demographics are reduced
* Fairness is improved while maintaining clinical accuracy
Why the other options are not sufficient or aligned with AWS best practices:
* B. Prompt engineering can influence outputs but does not address underlying data or model bias and is not sufficient for regulated, high-risk domains like healthcare.
* C. Content filtering removes outputs after generation but does not prevent biased decision-making by the model itself.
* D. Separate FM endpoints by demographic group increases the risk of reinforcing bias and violates fairness principles rather than mitigating them.
AWS AI Study Guide References:
* AWS Responsible AI principles: Fairness and Governance
* Amazon SageMaker Clarify: bias detection and mitigation
* AWS best practices for ML in high-risk domains such as healthcare
NEW QUESTION # 347
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