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NEW QUESTION # 202
A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality.
Which action must the company take to use the custom model through Amazon Bedrock?
Answer: C
Explanation:
To use a custom model that has been trained to improve summarization quality, the company must deploy the model on an Amazon SageMaker endpoint. This allows the model to be used for real-time inference through Amazon Bedrock or other AWS services. By deploying the model in SageMaker, the custom model can be accessed programmatically via API calls, enabling integration with Amazon Bedrock.
* Option B (Correct): "Deploy the custom model in an Amazon SageMaker endpoint for real-time inference": This is the correct answer because deploying the model on SageMaker enables it to serve real-time predictions and be integrated with Amazon Bedrock.
* Option A: "Purchase Provisioned Throughput for the custom model" is incorrect because provisioned throughput is related to database or storage services, not model deployment.
* Option C: "Register the model with the Amazon SageMaker Model Registry" is incorrect because while the model registry helps with model management, it does not make the model accessible for real- time inference.
* Option D: "Grant access to the custom model in Amazon Bedrock" is incorrect because Bedrock does not directly manage custom model access; it relies on deployed endpoints like those in SageMaker.
AWS AI Practitioner References:
* Amazon SageMaker Endpoints: AWS recommends deploying models to SageMaker endpoints to use them for real-time inference in various applications.
NEW QUESTION # 203
A media company wants to analyze viewer behavior and demographics to recommend personalized content.
The company wants to deploy a customized ML model in its production environment. The company also wants to observe if the model quality drifts over time.
Which AWS service or feature meets these requirements?
Answer: C
Explanation:
The requirement is to deploy a customized machine learning (ML) model and monitor its quality for potential drift over time in a production environment. Let's evaluate each option:
A). Amazon Rekognition: This service is designed for image and video analysis, such as object detection, facial recognition, and text extraction. It is not suited for deploying custom ML models or monitoring model quality drift.
B). Amazon SageMaker Clarify: This feature helps detect bias in ML models and explains model predictions.
While it addresses fairness and interpretability, it does not specifically focus on monitoring model quality drift over time in production.
C). Amazon Comprehend: This is a natural language processing (NLP) service for extracting insights from text, such as sentiment analysis or entity recognition. It does not support deploying custom ML models or monitoring model performance drift.
D). Amazon SageMaker Model Monitor: This feature is part of Amazon SageMaker and is specifically designed to monitor ML models in production. It tracks metrics such as data drift, model drift, and performance degradation over time, alerting users when issues are detected.
Exact Extract Reference: According to the AWS documentation on Amazon SageMaker, "Amazon SageMaker Model Monitor allows you to detect and remediate data and model quality issues in production. It continuously monitors the performance of deployed models, capturing data and model predictions to detect deviations from expected behavior, such as data drift or model performance degradation." (Source: AWS SageMaker Documentation - Model Monitoring, https://docs.aws.amazon.com/sagemaker/latest/dg/model- monitor.html).
This directly aligns with the requirement to observe model quality drift, making Amazon SageMaker Model Monitor the correct choice.
References:
AWS SageMaker Documentation: Model Monitoring (https://docs.aws.amazon.com/sagemaker/latest/dg
/model-monitor.html)
AWS AI Practitioner Study Guide (conceptual alignment with monitoring deployed ML models)
NEW QUESTION # 204
A company is using an Amazon Nova Canvas model to generate images. The model generates images successfully. The company needs to prevent the model from including specific items in the generated images.
Which solution will meet this requirement?
Answer: C
Explanation:
The correct answer is C - Use a negative prompt. Negative prompts instruct a generative image model to avoid certain features, objects, or styles in the output. This technique is fully supported by models like Amazon Nova Canvas on Bedrock, which are based on diffusion or image generation architectures.
According to AWS documentation, negative prompts refine output control by telling the model what not to include, thereby improving brand alignment, compliance, or creative direction. A higher temperature increases randomness, not control. A detailed prompt helps, but without exclusion instructions, the model may still include unwanted elements. Changing the model may yield better output but doesn't directly solve this control requirement. Negative prompts are purpose-built for this scenario.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Documentation - Prompt Engineering for Image Models
AWS Generative AI Guide - Controlled Generation with Negative Prompts
NEW QUESTION # 205
A company uses an open-source pre-trained model to analyze user sentiment for a newly released product.
Which action must the company perform, according to MLOps best practices?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
According to MLOps best practices, once an ML model is deployed to production-especially an open-source pre-trained model-the organization must continuously monitor model outputs to ensure:
* Model predictions remain accurate over time
* No performance degradation due to data drift or concept drift
* Outputs remain aligned with business and ethical expectations
AWS MLOps guidance emphasizes monitoring in production as a mandatory step for maintaining model reliability and governance.
Why the other options are incorrect:
* A (Hyperparameter tuning) is optional and model-dependent.
* B (Labeling data) is required only when training or fine-tuning, not when using a pre-trained model directly.
* D (Feature engineering) is less relevant for modern pre-trained NLP models.
AWS AI document references:
* MLOps Best Practices on AWS
* Amazon SageMaker Model Monitoring
* Operationalizing Machine Learning on AWS
NEW QUESTION # 206
A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality.
Which action must the company take to use the custom model through Amazon Bedrock?
Answer: C
Explanation:
To use a custom model that has been trained to improve summarization quality, the company must deploy the model on an Amazon SageMaker endpoint. This allows the model to be used for real-time inference through Amazon Bedrock or other AWS services. By deploying the model in SageMaker, the custom model can be accessed programmatically via API calls, enabling integration with Amazon Bedrock.
Option B (Correct): "Deploy the custom model in an Amazon SageMaker endpoint for real-time inference":
This is the correct answer because deploying the model on SageMaker enables it to serve real-time predictions and be integrated with Amazon Bedrock.
Option A: "Purchase Provisioned Throughput for the custom model" is incorrect because provisioned throughput is related to database or storage services, not model deployment.
Option C: "Register the model with the Amazon SageMaker Model Registry" is incorrect because while the model registry helps with model management, it does not make the model accessible for real-time inference.
Option D: "Grant access to the custom model in Amazon Bedrock" is incorrect because Bedrock does not directly manage custom model access; it relies on deployed endpoints like those in SageMaker.
AWS AI Practitioner References:
Amazon SageMaker Endpoints: AWS recommends deploying models to SageMaker endpoints to use them for real-time inference in various applications.
NEW QUESTION # 207
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