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| Section | Objectives |
|---|---|
| Applications of Foundation Models | - Use cases for generative AI in business - AI-powered assistants and automation - Content generation and summarization |
| Fundamentals of Generative AI | - Foundation models and prompt engineering basics - Large language models (LLMs) concepts - AWS generative AI services overview (e.g., Amazon Bedrock) |
| Responsible AI and Security | - Bias, fairness, and explainability - AI ethics and responsible use - Security, privacy, and governance in AI systems |
| Fundamentals of Artificial Intelligence and Machine Learning | - Core AI and ML concepts - Common ML workflows and lifecycle - Data fundamentals for AI/ML |
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NEW QUESTION # 344
A company is using supervised learning to train an AI model on a small labeled dataset that is specific to a target task. Which step of the foundation model (FM) lifecycle does this describe?
Answer: C
Explanation:
Comprehensive and Detailed
Fine-tuning involves training an already pre-trained FM on a smaller, labeled dataset for task specialization.
Data selection is about curating training data.
Pre-training is the initial training phase on massive datasets.
Evaluation happens after training, not during.
Reference:
AWS Documentation - Fine-tuning in Amazon Bedrock
NEW QUESTION # 345
A company is building a contact center application and wants to gain insights from customer conversations. The company wants to analyze and extract key information from the audio of the customer calls.
Which solution meets these requirements?
Answer: C
NEW QUESTION # 346
A company uses Amazon SageMaker for its ML pipeline in a production environment. The company has large input data sizes up to 1 GB and processing times up to 1 hour. The company needs near real-time latency.
Which SageMaker inference option meets these requirements?
Answer: A
Explanation:
Real-time inference is designed to provide immediate, low-latency predictions, which is necessary when the company requires near real-time latency for its ML models. This option is optimal when there is a need for fast responses, even with large input data sizes and substantial processing times.
* Option A (Correct): "Real-time inference": This is the correct answer because it supports low- latency requirements, which are essential for real-time applications where quick response times are needed.
* Option B: "Serverless inference" is incorrect because it is more suited for intermittent, small-scale inference workloads, not for continuous, large-scale, low-latency needs.
* Option C: "Asynchronous inference" is incorrect because it is used for workloads that do not require immediate responses.
* Option D: "Batch transform" is incorrect as it is intended for offline, large-batch processing where immediate response is not necessary.
AWS AI Practitioner References:
* Amazon SageMaker Inference Options: AWS documentation describes real-time inference as the best solution for applications that require immediate prediction results with low latency.
NEW QUESTION # 347
A company that uses multiple ML models wants to identify changes in original model quality so that the company can resolve any issues.
Which AWS service or feature meets these requirements?
Answer: C
Explanation:
Amazon SageMaker Model Monitor is specifically designed to automatically detect and alert on changes in model quality, such as data drift, prediction drift, or other anomalies in model performance once deployed.
* D is correct:
"Amazon SageMaker Model Monitor continuously monitors the quality of machine learning models in production. It automatically detects concept drift, data drift, and other quality issues, enabling teams to take corrective actions." (Reference: Amazon SageMaker Model Monitor Documentation, AWS Certified AI Practitioner Study Guide)
* A (JumpStart) provides prebuilt solutions and models, not monitoring.
* B (HyperPod) is for large-scale training, not model monitoring.
* C (Data Wrangler) is for data preparation, not ongoing model quality monitoring.
NEW QUESTION # 348
Which term describes the numerical representations of real-world objects and concepts that AI and natural language processing (NLP) models use to improve understanding of textual information?
Answer: A
Explanation:
Embeddings are numerical representations of objects (such as words, sentences, or documents) that capture the objects' semantic meanings in a form that AI and NLP models can easily understand. These representations help models improve their understanding of textual information by representing concepts in a continuous vector space.
* Option A (Correct): "Embeddings": This is the correct term, as embeddings provide a way for models to learn relationships between different objects in their input space, improving their understanding and processing capabilities.
* Option B: "Tokens" are pieces of text used in processing, but they do not capture semantic meanings like embeddings do.
* Option C: "Models" are the algorithms that use embeddings and other inputs, not the representations themselves.
* Option D: "Binaries" refer to data represented in binary form, which is unrelated to the concept of embeddings.
AWS AI Practitioner References:
* Understanding Embeddings in AI and NLP: AWS provides resources and tools, like Amazon SageMaker, that utilize embeddings to represent data in formats suitable for machine learning models.
NEW QUESTION # 349
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