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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Applications of Foundation Models | 28% | - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan - Recognize tools for building and deploying generative AI solutions - Describe use cases for text, image, video, and code generation - Explain integration of foundation models into applications |
| Topic 2: Security, Compliance, and Governance for AI Solutions | 14% | - Governance frameworks for AI lifecycle - Data protection and privacy in AI workflows - Compliance requirements and regulations - Security controls for AI data and models |
| Topic 3: Guidelines for Responsible AI | 14% | - Describe ethical and societal impacts of AI - Identify risks and mitigation strategies for AI systems - Define responsible AI principles: fairness, transparency, privacy, safety - Explain bias detection and reduction |
| Topic 4: Fundamentals of AI and ML | 20% | - Recognize key concepts: data, models, training, inference, evaluation - Identify types of ML: supervised, unsupervised, reinforcement learning - Define artificial intelligence (AI), machine learning (ML), and deep learning - Describe common ML workflows and lifecycle |
| Topic 5: Fundamentals of Generative AI | 24% | - Define generative AI and foundation models - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) - Explain capabilities and use cases of generative AI - Differentiate between generative AI and traditional ML |
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NEW QUESTION # 441
A company is using a pre-trained large language model (LLM). The LLM must perform multiple tasks that require specific domain knowledge. The LLM does not have information about several technical topics in the domain. The company has unlabeled data that the company can use to fine-tune the model.
Which fine-tuning method will meet these requirements?
Answer: D
Explanation:
The correct answer is C because Continued Pre-training (also known as domain-adaptive pre-training) involves training a pre-trained model further on unlabeled domain-specific data. This method helps adapt the LLM to a specific domain without needing labeled datasets, making it ideal for cases where the goal is to enhance the model's understanding of technical language or terminology.
From AWS documentation:
"Continued pre-training allows an LLM to ingest large volumes of domain-specific text without labels to improve contextual understanding in a particular area. This is effective when adapting a foundation model to new knowledge without altering the model architecture." Explanation of other options:
A). Full training refers to building a model from scratch, which is extremely resource-intensive and unnecessary if a strong base model already exists.
B). Supervised fine-tuning requires labeled data, which the scenario explicitly lacks.
D). RAG is a method to retrieve external information at inference time, not a training technique using unlabeled data.
Referenced AWS AI/ML Documents and Study Guides:
* AWS Bedrock Model Customization Documentation - Continued Pre-training
* Amazon SageMaker JumpStart - Domain Adaptation Techniques
* AWS Machine Learning Specialty Study Guide - Foundation Model Customization Section
NEW QUESTION # 442
What does inference refer to in the context of AI?
Answer: B
Explanation:
* Inference = applying a trained ML model to new, unseen data to make predictions, classifications, or generate outputs.
* A is algorithm research, C refers to ensemble learning, D is data collection.
# Reference:
AWS ML Glossary - Inference
NEW QUESTION # 443
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:
Explanation:
Use Case 1:
The company's chatbot needs predictions from the LLM to understand users' intent with minimal latency.
Chatbots require low-latency, immediate responses to user input. Real-time inference is ideal for these interactive use cases.
Use Case 2:
A data processing job needs to query the LLM to process gigabytes of text files on weekends.
Batch transform is designed for asynchronous, high-throughput jobs where latency is not critical. It's ideal for scheduled or large-scale processing like weekend batch jobs on large datasets.
Use Case 3:
The company's engineering team needs to create an API that can process small pieces of text content and provide low-latency predictions.
An API requiring fast response time for small content sizes is best served by real-time inference to meet latency requirements.
NEW QUESTION # 444
An AI practitioner is determining the appropriate data type for various use cases.
Select the correct data type from the following list for each use case. Select each data type one time.
Answer:
Explanation:
NEW QUESTION # 445
A company is developing a new foundation model (FM) for multilingual text generation. The model must work across multiple languages and dialects. The model must adhere to the principles of responsible AI.
Which model training process will meet these requirements?
Answer: D
Explanation:
Continued pre-training extends the training of an existing foundation model by exposing it to additional datasets that improve its knowledge and capabilities.
For multilingual text generation, a diverse dataset containing multiple languages and dialects helps the model learn different linguistic structures, expressions, and communication patterns.
Including representative language variations also supports responsible AI objectives by reducing the risk that particular linguistic communities are underrepresented.
Amazon Bedrock supports continued pre-training for compatible foundation models using additional unlabeled training data.
Prompt management alone does not improve the model ' s underlying multilingual knowledge. Training separate models and combining them introduces unnecessary complexity.
Model distillation from a single-language teacher may also restrict linguistic diversity.
Therefore, continued pre-training on a representative multilingual dataset is the most appropriate strategy, although fairness and performance must still be evaluated separately.
NEW QUESTION # 446
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