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>> Amazon AIF-C01 Valid Test Question <<
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NEW QUESTION # 54
A company wants to generate synthetic data responses for multiple prompts from a large volume of data. The company wants to use an API method to generate the responses. The company does not need to generate the responses immediately.
Answer: D
Explanation:
The correct answer is B - Use Amazon Bedrock batch inference, which allows asynchronous generation of large-scale model outputs through APIs without requiring low-latency performance. According to AWS Bedrock documentation, batch inference is ideal for high-volume workloads that can tolerate delay, such as bulk content generation or summarization jobs. Unlike real-time inference, it processes requests in bulk, reducing cost and operational load. AWS handles the queuing, processing, and scaling automatically. Bedrock Agents (option C) are for workflow orchestration, not large-scale generation. AWS Lambda (option D) can automate tasks but is not optimized for high-volume LLM calls. Batch inference provides cost efficiency, scalability, and simplicity for delayed, asynchronous generation needs.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Developer Guide - Batch Inference
AWS ML Specialty Study Guide - Scalable Inference Options
NEW QUESTION # 55
A company wants to label training datasets by using human feedback to fine-tune a foundation model (FM). The company does not want to develop labeling applications or manage a labeling workforce. Which AWS service or feature meets these requirements?
Answer: A
Explanation:
Amazon SageMaker Ground Truth Plus provides a fully managed data labeling service where AWS manages the workforce, tools, and processes.
Data Wrangler is for data preparation and transformation.
Transcribe is for speech-to-text.
Macie is for sensitive data discovery, not labeling.
Reference:
AWS Documentation - SageMaker Ground Truth Plus
NEW QUESTION # 56
Which option is a use case for generative AI models?
Answer: B
Explanation:
Generative AI models are used to create new content based on existing data. One common use case is generating photorealistic images from text descriptions, which is particularly useful in digital marketing, where visual content is key to engaging potential customers.
* Option B (Correct): "Creating photorealistic images from text descriptions for digital marketing"
* This is the correct answer because generative AI models, like those offered by Amazon Bedrock, can create images based on text descriptions, making them highly valuable for generating marketing materials.
* Option A: "Improving network security by using intrusion detection systems" is incorrect because this is a use case for traditional machine learning models, not generative AI.
* Option C: "Enhancing database performance by using optimized indexing" is incorrect as it is unrelated to generative AI.
* Option D: "Analyzing financial data to forecast stock market trends" is incorrect because it typically involves predictive modeling rather than generative AI.
AWS AI Practitioner References:
* Use Cases for Generative AI Models on AWS: AWS highlights the use of generative AI for creative content generation, including image creation, text generation, and more, which is suited for digital marketing applications.
NEW QUESTION # 57
A company uses an Amazon Bedrock foundation model (FM) to summarize documents for an internal use case. The company trained a custom model in Amazon Bedrock to improve the quality of the model's summarizations. The company needs a solution to use the customized model on Amazon Bedrock.
Which solution will meet this requirement?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
When a foundation model is customized directly in Amazon Bedrock, the correct way to use the customized model for inference is to purchase Provisioned Throughput.
Provisioned Throughput:
* Enables consistent performance and predictable latency
* Allows production use of customized Bedrock models
* Is the required deployment mechanism for custom FMs in Bedrock
Why the other options are incorrect:
* SageMaker endpoints (B) are not used for Bedrock-native custom models.
* Model Registry (C) applies to SageMaker models.
* Approval status (D) does not deploy or enable usage.
AWS AI document references:
* Amazon Bedrock Custom Model Deployment
* Provisioned Throughput for Foundation Models
* Using Customized Models in Amazon Bedrock
NEW QUESTION # 58
A software company wants to use a large language model (LLM) for workflow automation. The application will transform user messages into JSON files. The company will use the JSON files as inputs for data pipelines.
The company has a labeled dataset that contains user messages and output JSON files.
Which solution will train the LLM for workflow automation?
Answer: D
Explanation:
Fine-tuning is the process of training a pre-trained LLM with a labeled dataset specific to a desired task-in this case, mapping user messages to JSON outputs. Fine-tuning leverages supervised learning to specialize the model's outputs.
C is correct:
"Fine-tuning is a supervised learning approach in which a model is further trained on a custom, labeled dataset to adapt to a specific use case." (Reference: Amazon Bedrock Fine-Tuning, AWS Certified AI Practitioner Study Guide)
"Fine-tuning is a supervised learning approach in which a model is further trained on a custom, labeled dataset to adapt to a specific use case." (Reference: Amazon Bedrock Fine-Tuning, AWS Certified AI Practitioner Study Guide) A is incorrect-unsupervised learning does not use labeled data.
B (continued pre-training) uses unlabeled data.
D (RLHF) uses reward signals and human feedback, not direct labeled input/output pairs.
NEW QUESTION # 59
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