To make a Large Language Model (LLM) produce more consistent, deterministic, and less random responses, the inference process must be constrained. This is achieved by adjusting key sampling parameters. Lowering the temperature value makes the model's output less random by increasing the probability of the most likely next tokens. Additionally, reducing the pool of potential tokens by either lowering the topk (selecting from the K most likely tokens) or topp (selecting from a nucleus of tokens with a cumulative probability of P) further reduces variability. Therefore, lowering the temperature in combination with lowering either topk or topp are both correct approaches to meet the requirements.

A. Increasing the topp sampling threshold expands the pool of potential tokens, which increases randomness and directly contradicts the goal of consistency.

C. Increasing both temperature and topk significantly increases the randomness and creativity of the output, which is the opposite of the stated goal.

1. Amazon Bedrock User Guide, "Inference parameters for foundation models": This official documentation specifies the function of key parameters.

Temperature: "Use a lower value to decrease randomness in the response."

Top K: "A lower value limits the choices to more probable tokens."

Top P: "A lower value limits the choices to more probable tokens."

This confirms that lowering these values reduces randomness and promotes deterministic output.

2. Holtzman, A., et al. (2020). "The Curious Case of Neural Text Degeneration." International Conference on Learning Representations (ICLR). This peer-reviewed paper discusses various decoding strategies.

Section 2, "Decoding Strategies": The paper explains that low-temperature sampling is closer to greedy decoding (most deterministic), and that Nucleus Sampling (topp) and topk sampling are methods to truncate the vocabulary to high-probability tokens, thus controlling randomness. Lowering k or p makes the output less random.

3. Stanford University, CS224N: NLP with Deep Learning, Winter 2023 Lecture Slides:

Lecture 10, "Language Models and Recurrent Neural Networks", Slides 73-76: These slides detail decoding algorithms. They explain that a lower temperature makes generation more conservative (less random) and that top-k and top-p (Nucleus) sampling restrict the choices to the most probable tokens, thereby controlling the output.

This approach directly addresses the core requirement of creating semantic embeddings in a fully managed and scalable manner. Amazon Titan Text Embeddings, available through the serverless Amazon Bedrock API, is specifically designed to convert textual data into high-quality vector representations. These embeddings capture the contextual and semantic meaning of the text. Storing these vectors in Amazon OpenSearch Service (with its k-NN search capability) creates a scalable vector store. This architecture is the foundational pattern for Retrieval-Augmented Generation (RAG) and integrates natively with agents for Amazon Bedrock, which can query this vector store to retrieve relevant context for downstream tasks.

A. Amazon Kendra is a high-level intelligent search service. While it performs semantic search internally and can serve as a knowledge base for Bedrock agents, it abstracts away the direct creation and management of embeddings.

B. Amazon SageMaker Data Wrangler is a service for data preparation and feature engineering for traditional machine learning models, not for generating the semantic embeddings required for RAG workflows.

C. While SageMaker JumpStart can deploy embedding models, it requires managing a dedicated endpoint. Using the serverless Amazon Bedrock API for Titan Embeddings is a more "fully managed" solution that aligns better with the requirements.

1. Amazon Bedrock User Guide - Amazon Titan models: "Amazon Titan Text Embeddings G1 - The Titan Text Embeddings model translates text inputs (words, phrases, or large units of text) into a numerical representation (known as embeddings) that can be used to power use cases like search, personalization, and clustering based on semantic similarity." (Source: AWS Documentation, Amazon Bedrock User Guide, Section: "Amazon Titan models").

2. Amazon Bedrock User Guide - Knowledge bases for Amazon Bedrock: "When you create a knowledge base, Amazon Bedrock converts your documents into embeddings... and stores the embeddings in your vector database... You can choose from a variety of vector databases to store the vectors for your knowledge base, including Amazon OpenSearch Service Serverless..." This document explicitly outlines the architecture described in the correct answer. (Source: AWS Documentation, Amazon Bedrock User Guide, Section: "Knowledge bases for Amazon Bedrock").

3. Amazon Kendra Developer Guide - Integrating Amazon Bedrock with an Amazon Kendra index: This guide shows how Kendra can be used as a data source for RAG, confirming it's a valid but higher-level solution. It states, "You can use an Amazon Kendra index as a data source for your knowledge base to build a solution with the retrieval augmented generation (RAG) model." (Source: AWS Documentation, Amazon Kendra Developer Guide, Section: "Integrating Amazon Bedrock with an Amazon Kendra index").

4. Amazon SageMaker Developer Guide - Prepare ML Data with Amazon SageMaker Data Wrangler: "You can use SageMaker Data Wrangler to simplify the process of data preparation and feature engineering..." This confirms its purpose is distinct from generating semantic embeddings for RAG. (Source: AWS Documentation, Amazon SageMaker Developer Guide, Section: "Prepare ML Data with Amazon SageMaker Data Wrangler").

SageMaker JumpStart provides a low-code/no-code (LCNC) solution for fine-tuning pre-trained foundation models, including LLMs for tasks like text summarization. It streamlines the process by offering a graphical user interface within SageMaker Studio to select a model, provide a training dataset, and launch a fine-tuning job. This approach automates the complex setup of training infrastructure and minimizes the need for manual coding or intervention, directly aligning with the team's requirements for an automated LCNC solution.

A. SageMaker Training Jobs are a powerful tool but require users to write and provide their own training scripts and container configurations, which is not a low-code/no-code approach.

B. Fine-tuning on raw Amazon EC2 instances, even when launched from SageMaker Studio, necessitates manual environment setup, library management, and coding, contradicting the LCNC requirement.

C. SageMaker Script Mode is specifically designed for users to bring their own custom training scripts, making it a code-intensive method that offers flexibility at the cost of simplicity.

1. Amazon SageMaker Developer Guide: Under the "Amazon SageMaker JumpStart" section, it states, "You can fine-tune models with no code through SageMaker JumpStart's user interface in Studio." This confirms that JumpStart is the designated LCNC solution for this task. (See the subsection: "Fine-tune a model with SageMaker JumpStart").

2. Amazon SageMaker Developer Guide: The section "Create a training job with the Amazon SageMaker Python SDK" describes how to use SageMaker Training Jobs. The process involves creating an estimator object and providing an entry-point script, demonstrating it is a code-first approach.

3. Amazon SageMaker Developer Guide: The "Use Script Mode" page details how to package custom Python code for use with SageMaker, highlighting that it is fundamentally a code-based solution for users who need custom logic.

4. Amazon SageMaker Developer Guide: The "Automate model development with Amazon SageMaker Autopilot" section describes Autopilot's function for tabular data (classification and regression), not for fine-tuning LLMs. However, the combination with JumpStart in the option points to the automated, UI-driven workflow that JumpStart provides for foundation models.

The scenario describes a classic machine learning problem where the model shows high performance on the majority class ("legitimate claim") but fails to correctly identify the minority class ("fraudulent claim"). The key piece of evidence is the SageMaker Clarify pretraining bias analysis, which revealed a "significant skew." This skew directly indicates a disproportionate representation of classes in the dataset, a condition known as Class Imbalance (CI). Models trained on such data are inherently biased toward the majority class to minimize overall error, leading to poor predictive power for the underrepresented minority class.

A. Insufficient text extraction would likely cause poor performance across all classes by creating incomplete feature sets, not a specific bias against the minority class.

B. Overfitting results in poor generalization on all new, unseen data. The model's good performance on the majority class contradicts a general overfitting problem.

C. A high learning rate typically leads to unstable training and poor convergence across all classes, rather than a systematic failure on only the minority class.

1. Amazon SageMaker Developer Guide: Under the section "Detect pre-training bias in datasets," the guide defines the Class Imbalance (CI) metric. It states, "A value of 1 indicates that the dataset is balanced... Values greater than 1 indicate that the dataset is imbalanced and that there is a bias towards the majority class." This directly links the "significant skew" found by Clarify to Class Imbalance.

Source: Amazon SageMaker Developer Guide, Section: "Pre-training bias metrics", Subsection: "Class Imbalance (CI)".

2. AWS Machine Learning Blog: In the article "Handling imbalanced data with Amazon SageMaker," it is explained that "In imbalanced classification problems, the model tends to be biased towards the majority class, causing poor classification of the minority class." This perfectly matches the behavior described in the question's scenario.

Source: AWS Machine Learning Blog, "Handling imbalanced data with Amazon SageMaker," Nov 12, 2021.

3. He, H., & Garcia, E. A. (2009). Learning from Imbalanced Data. This foundational academic paper explains that with imbalanced datasets, "the learning algorithm is not motivated to resolve the classification problem for the minority class examples since they contribute very little to the overall accuracy."

Source: IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 9, pp. 1263-1284, Sept. 2009, Section I (Introduction). DOI: https://doi.org/10.1109/TKDE.2008.239

The primary issue is a severe class imbalance, where the minority class (fraud) is underrepresented, leading to poor model performance (high false negatives). The Synthetic Minority Oversampling Technique (SMOTE) is a data-level method specifically designed to address this. By generating new, synthetic examples of the minority class in the feature space, SMOTE rebalances the class distribution of the training dataset. This forces the model to learn the characteristics of the fraudulent transactions more effectively, thereby improving its ability to detect them and reducing the number of false negatives.

A. Early stopping is a regularization technique to prevent overfitting; it does not correct the underlying class imbalance that causes the model to ignore the minority class.

B. Oversampling should be applied to the minority (fraudulent) class to increase its representation. Oversampling the majority (non-fraudulent) class would exacerbate the imbalance.

C. While hyperparameter tuning can improve model performance, it is an indirect approach that does not fundamentally alter the imbalanced data distribution, which is the root cause of the problem.

1. AWS Machine Learning Blog. (2021). Handling imbalanced data with Amazon SageMaker. In the section "Data-level approach," the blog states, "The data-level approach focuses on rebalancing the data distribution so that the minority class is well-represented... A popular oversampling technique is the Synthetic Minority Oversampling Technique (SMOTE)." This directly supports using SMOTE as a preprocessing step.

2. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321-357. Section 2, "SMOTE: Synthetic Minority Over-sampling Technique," describes the algorithm as a method to over-sample the minority class by creating “synthetic” examples rather than by over-sampling with replacement, which directly addresses the scenario's need. DOI: https://doi.org/10.1613/jair.953

3. Amazon SageMaker Developer Guide. XGBoost Algorithm. The guide discusses the scaleposweight hyperparameter, a common technique to handle class imbalance by weighting the loss function. This highlights that while model-level solutions exist (related to option C), a direct data correction method like SMOTE (option D) is a distinct and valid preprocessing approach as requested by the question.