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NEW QUESTION # 29
A company wants to deploy a conversational chatbot to answer customer questions. The chatbot is based on a fine-tuned Amazon SageMaker JumpStart model. The application must comply with multiple regulatory frameworks.
Which capabilities can the company show compliance for? (Select TWO.)
Answer: B,D
Explanation:
To comply with multiple regulatory frameworks, the company must ensure data protection and threat detection. Data protection involves safeguarding sensitive customer information, while threat detection identifies and mitigates security threats to the application.
Option C (Correct): "Data protection": This is correct because data protection is critical for compliance with privacy and security regulations.
Option B (Correct): "Threat detection": This is correct because detecting and mitigating threats is essential to maintaining the security posture required for regulatory compliance.
Option A: "Auto scaling inference endpoints" is incorrect because auto-scaling does not directly relate to regulatory compliance.
Option D: "Cost optimization" is incorrect because it is focused on managing expenses, not compliance.
Option E: "Loosely coupled microservices" is incorrect because this architectural approach does not directly address compliance requirements.
AWS AI Practitioner Reference:
AWS Compliance Capabilities: AWS offers services and tools, such as data protection and threat detection, to help companies meet regulatory requirements for security and privacy.
NEW QUESTION # 30
A financial company is building an ML model to classify fraudulent transactions based on customer data and transaction patterns. The company wants to evaluate the model's performance. The company must ensure that the model makes correct predictions and minimizes false positives.
Which evaluation metric will meet these requirements?
Answer: D
Explanation:
The verified answer is C. F1 score. This is a classification problem: the model classifies transactions as fraudulent or non-fraudulent. AWS machine learning documentation identifies accuracy, precision, recall, and F1 score as classification evaluation metrics. F1 score is especially appropriate when the evaluation must consider both correct positive predictions and the cost of incorrect positive predictions. AWS defines F1 as the harmonic mean of precision and recall, which means it balances how many positive predictions are correct with how many actual positive cases are found.
The key issue in the question is minimizing false positives. In fraud detection, a false positive means a legitimate transaction is incorrectly classified as fraudulent. AWS documentation explains that precision evaluates how well the model avoids false positives by measuring the percentage of positive predictions that were actually correct. Because F1 includes precision, it penalizes models that generate too many false positives while also considering recall.
Accuracy is not the best answer because it only measures the overall fraction of correct predictions. In fraud detection, the dataset is often imbalanced, so a model can appear accurate while still performing poorly on fraud-related predictions. R-squared and RMSE are regression metrics, not classification metrics. R-squared measures how well a regression model explains variance, and RMSE measures prediction error magnitude for continuous numeric outputs.
Because the company needs a classification metric that reflects correct predictions and penalizes false- positive behavior, F1 score is the strongest option among the choices.
NEW QUESTION # 31
A company uses Amazon Comprehend to analyze customer feedback. A customer has several unique trained models. The company uses Comprehend to assign each model an endpoint. The company wants to automate a report on each endpoint that is not used for more than 15 days.
Answer: D
Explanation:
The correct answer is B because Amazon CloudWatch provides monitoring capabilities that include tracking usage metrics for Amazon Comprehend endpoints, such as invocation counts. You can configure CloudWatch to collect these metrics and create custom dashboards or alarms to report when an endpoint has zero usage over a period (e.g., 15 days).
From AWS documentation:
"Amazon CloudWatch enables you to collect and track metrics for Comprehend endpoints, create alarms, and automatically react to changes in your AWS resources." This allows automated reporting and alerting for underused or idle endpoints.
Explanation of other options:
A . AWS Trusted Advisor focuses on general AWS resource optimization, security, and limits but does not track endpoint usage.
C . AWS CloudTrail tracks API calls but does not provide time-based monitoring or usage analysis over time.
D . AWS Config is used to track configuration changes and compliance, not endpoint usage metrics.
Referenced AWS AI/ML Documents and Study Guides:
Amazon CloudWatch Metrics for Amazon Comprehend
AWS Certified Machine Learning Specialty Guide - Monitoring and Logging Section AWS Cloud Operations Guide - Resource Utilization Monitoring
NEW QUESTION # 32
What is the purpose of vector embeddings in a large language model (LLM)?
Answer: A
NEW QUESTION # 33
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:
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 # 34
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