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NEW QUESTION # 149
A company is using Amazon Bedrock to answer questions. The Amazon Bedrock foundation model (FM) uses the company's internal documentation to respond to the questions. The documentation is updated frequently. The company needs a solution that avoids recurring costs for model training while keeping responses current.
Which solution will meet these requirements?
Answer: C
Explanation:
Amazon Bedrock Knowledge Bases with Retrieval Augmented Generation (RAG) is the appropriate architecture because it separates frequently changing enterprise information from the foundation model's learned parameters. Instead of retraining or fine-tuning the FM every time documentation changes, the application retrieves relevant information from the current knowledge source at query time and supplies that information as context to the model.
AWS specifically states that adding a knowledge base improves cost-effectiveness by "removing the need to continually train your model to be able to use your private data." This directly satisfies the requirement to avoid recurring model-training costs.
With Amazon Bedrock Knowledge Bases, the organization connects supported data sources, ingests and indexes the documents, and retrieves relevant chunks when a user asks a question. RAG then combines that retrieved content with the user's query so that the FM can generate an answer grounded in company information. AWS describes RAG as using information from data sources to improve the relevance and accuracy of generated responses.
When source documents change, the knowledge base can be synchronized or re-ingested so the retrieval layer reflects the updated content. This is substantially more appropriate than fine-tuning the model after every documentation change.
Option B would unnecessarily introduce repeated customization jobs, cost, operational overhead, and model-version management. Option C would become stale because static examples would continue referencing earlier documentation. Option D is especially unsuitable because cached answers can become outdated and would not verify them against newly updated source material.
RAG is specifically designed for scenarios where models require access to current, proprietary, or rapidly changing information without embedding that knowledge permanently into model weights.
Therefore, A. Amazon Bedrock Knowledge Bases with RAG is the correct solution.
NEW QUESTION # 150
A company is introducing a new feature for its application. The feature will refine the style of output messages. The company will fine-tune a large language model (LLM) on Amazon Bedrock to implement the feature. Which type of data does the company need to meet these requirements?
Answer: D
Explanation:
* Fine-tuning requires paired input-output examples to teach the model how to respond to inputs with desired styled outputs.
* Single inputs (A) or outputs (B) are insufficient.
* Separate, unpaired samples (D) don't establish the input-output mapping.
# Reference:
AWS Documentation - Preparing data for fine-tuning FMs
NEW QUESTION # 151
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company needs the LLM to produce more consistent responses to the same input prompt.
Which adjustment to an inference parameter should the company make to meet these requirements?
Answer: B
NEW QUESTION # 152
A company is using a pre-trained large language model (LLM) to build a chatbot for product recommendations. The company needs the LLM outputs to be short and written in a specific language.
Which solution will align the LLM response quality with the company's expectations?
Answer: A
Explanation:
Adjusting the prompt is the correct solution to align the LLM outputs with the company's expectations for short, specific language responses.
Adjust the Prompt:
Modifying the prompt can guide the LLM to produce outputs that are shorter and tailored to the desired language.
A well-crafted prompt can provide specific instructions to the model, such as "Answer in a short sentence in Spanish." Why Option A is Correct:
Control Over Output: Adjusting the prompt allows for direct control over the style, length, and language of the LLM outputs.
Flexibility: Prompt engineering is a flexible approach to refining the model's behavior without modifying the model itself.
Why Other Options are Incorrect:
B: Choose an LLM of a different size: The model size does not directly impact the response length or language.
C: Increase the temperature: Increases randomness in responses but does not ensure brevity or specific language.
D: Increase the Top K value: Affects diversity in model output but does not align directly with response length or language specificity.
NEW QUESTION # 153
Which type of AI model makes numeric predictions?
Answer: B
Explanation:
The regression model is a fundamental type of supervised machine learning algorithm that is specifically designed to make numeric predictions. In regression tasks, the goal is to predict a continuous numerical value based on input features. This contrasts with classification, which predicts discrete labels.
According to AWS documentation:
"Regression models are used for predicting a continuous value. Examples include predicting house prices, stock market prices, or customer credit limits." (Reference: AWS Machine Learning Foundations: Regression, AWS AI Practitioner Study Guide) Option A (Diffusion) relates to generative models and is not primarily used for numeric prediction.
Option C (Transformer) is a neural network architecture, often used for sequence modeling tasks (e.g., NLP).
Option D (Multi-modal) describes a model handling multiple data types, not specifically numeric prediction.
Reference:
AWS AI/ML Learning Path - Regression Models
AWS Certified AI Practitioner Study Guide (Pearson)
NEW QUESTION # 154
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