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NEW QUESTION # 131
A company needs to choose a model from Amazon Bedrock to use internally. The company must identify a model that generates responses in a style that the company's employees prefer.
What should the company do to meet these requirements?
Answer: D
Explanation:
To determine which model generates responses in a style that the company's employees prefer, the best approach is to use a human workforce to evaluate the models with custom prompt datasets. This method allows for subjective evaluation based on the specific stylistic preferences of the company's employees, which cannot be effectively assessed through automated methods or pre-built datasets.
Option B (Correct): "Evaluate the models by using a human workforce and custom prompt datasets": This is the correct answer as it directly involves human judgment to evaluate the style and quality of the responses, aligning with employee preferences.
Option A: "Evaluate the models by using built-in prompt datasets" is incorrect because built-in datasets may not capture the company's specific stylistic requirements.
Option C: "Use public model leaderboards to identify the model" is incorrect as leaderboards typically measure model performance on standard benchmarks, not on stylistic preferences.
Option D: "Use the model InvocationLatency runtime metrics in Amazon CloudWatch" is incorrect because latency metrics do not provide any information about the style of the model's responses.
AWS AI Practitioner Reference:
Model Evaluation Techniques on AWS: AWS suggests using human evaluators to assess qualitative aspects of model outputs, such as style and tone, to ensure alignment with organizational preferences
NEW QUESTION # 132
A medical company is customizing a foundation model (FM) for diagnostic purposes. The company needs the model to be transparent and explainable to meet regulatory requirements.
Which solution will meet these requirements?
Answer: C
Explanation:
Amazon SageMaker Clarify provides transparency and explainability for machine learning models by generating metrics, reports, and examples that help to understand model predictions. For a medical company that needs a foundation model to be transparent and explainable to meet regulatory requirements, SageMaker Clarify is the most suitable solution.
* Amazon SageMaker Clarify:
* It helps in identifying potential bias in the data and model, and also explains model behavior by generating feature attributions, providing insights into which features are most influential in the model's predictions.
* These capabilities are critical in medical applications where regulatory compliance often mandates transparency and explainability to ensure that decisions made by the model can be trusted and audited.
* Why Option B is Correct:
* Transparency and Explainability: SageMaker Clarify is explicitly designed to provide insights into machine learning models' decision-making processes, helping meet regulatory requirements by explaining why a model made a particular prediction.
* Compliance with Regulations: The tool is suitable for use in sensitive domains, such as healthcare, where there is a need for explainable AI.
* Why Other Options are Incorrect:
* A. Amazon Inspector: Focuses on security assessments, not on explainability or model transparency.
* C. Amazon Macie: Provides data security by identifying and protecting sensitive data, but does not help in making models explainable.
* D. Amazon Rekognition: Used for image and video analysis, not relevant to making models explainable.
Thus, B is the correct answer for meeting transparency and explainability requirements for the foundation model
NEW QUESTION # 133
A company has built a chatbot that can respond to natural language questions with images. The company wants to ensure that the chatbot does not return inappropriate or unwanted images.
Which solution will meet these requirements?
Answer: D
Explanation:
Moderation APIs, such as Amazon Rekognition's Content Moderation API, can help filter and block inappropriate or unwanted images from being returned by a chatbot. These APIs are specifically designed to detect and manage undesirable content in images.
* Option A (Correct): "Implement moderation APIs": This is the correct answer because moderation APIs are designed to identify and filter inappropriate content, ensuring the chatbot does not return unwanted images.
* Option B: "Retrain the model with a general public dataset" is incorrect because retraining does not directly prevent inappropriate content from being returned.
* Option C: "Perform model validation" is incorrect as it ensures model correctness, not content moderation.
* Option D: "Automate user feedback integration" is incorrect because user feedback does not prevent inappropriate images in real-time.
AWS AI Practitioner References:
* AWS Content Moderation Services: AWS provides moderation APIs for filtering unwanted content from applications.
NEW QUESTION # 134
A company wants to use AI to protect its application from threats. The AI solution needs to check if an IP address is from a suspicious source.
Which solution meets these requirements?
Answer: D
Explanation:
An anomaly detection system is suitable for identifying unusual patterns or behaviors, such as suspicious IP addresses, which might indicate a potential threat.
* Anomaly Detection:
* Anomaly detection uses machine learning algorithms to identify deviations from normal behavior, such as unexpected traffic from a suspicious IP address.
* This is a common approach for identifying potential threats or malicious activity in cybersecurity applications.
* Why Option C is Correct:
* Detects Suspicious Behavior: An anomaly detection system can monitor and detect IP addresses that exhibit unusual or suspicious patterns.
* Real-time Monitoring: Provides continuous analysis of network traffic to identify potential security threats.
* Why Other Options are Incorrect:
* A. Speech recognition system: Is unrelated to detecting suspicious IP addresses.
* B. NLP named entity recognition: Focuses on identifying entities in text, not IP address analysis.
* D. Fraud forecasting system: Generally used for predicting fraud, not directly applicable to identifying suspicious IPs.
Thus, C is the correct answer for detecting suspicious IP addresses.
NEW QUESTION # 135
A company has developed a large language model (LLM) and wants to make the LLM available to multiple internal teams. The company needs to select the appropriate inference mode for each team.
Select the correct inference mode from the following list for each use case. Each inference mode should be selected one or more times. (Select THREE.)
* Batch transform
* Real-time inference
Answer:
Explanation:
NEW QUESTION # 136
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