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NEW QUESTION # 117
A company's large language model (LLM) is experiencing hallucinations.
How can the company decrease hallucinations?
Answer: B
Explanation:
Hallucinations in large language models (LLMs) occur when the model generates outputs that are factually incorrect, irrelevant, or not grounded in the input data. To mitigate hallucinations, adjusting the model's inference parameters, particularly the temperature, is a well-documented approach in AWS AI Practitioner resources. The temperature parameter controls the randomness of the model's output. A lower temperature makes the model more deterministic, reducing the likelihood of generating creative but incorrect responses, which are often the cause of hallucinations.
Exact Extract from AWS AI Documents:
From the AWS documentation on Amazon Bedrock and LLMs:
"The temperature parameter controls the randomness of the generated text. Higher values (e.g., 0.8 or above) increase creativity but may lead to less coherent or factually incorrect outputs, while lower values (e.g., 0.2 or 0.3) make the output more focused and deterministic, reducing the likelihood of hallucinations." (Source: AWS Bedrock User Guide, Inference Parameters for Text Generation) Detailed Option A: Set up Agents for Amazon Bedrock to supervise the model training.Agents for Amazon Bedrock are used to automate tasks and integrate LLMs with external tools, not to supervise model training or directly address hallucinations. This option is incorrect as it does not align with the purpose of Agents in Bedrock.
Option B: Use data pre-processing and remove any data that causes hallucinations.While data pre-processing can improve model performance, identifying and removing specific data that causes hallucinations is impractical because hallucinations are often a result of the model's generative process rather than specific problematic data points. This approach is not directly supported by AWS documentation for addressing hallucinations.
Option C: Decrease the temperature inference parameter for the model.This is the correct approach. Lowering the temperature reduces the randomness in the model's output, making it more likely to stick to factual and contextually relevant responses. AWS documentation explicitly mentions adjusting inference parameters like temperature to control output quality and mitigate issues like hallucinations.
Option D: Use a foundation model (FM) that is trained to not hallucinate.No foundation model is explicitly trained to "not hallucinate," as hallucinations are an inherent challenge in LLMs. While some models may be fine-tuned for specific tasks to reduce hallucinations, this is not a standard feature of foundation models available on Amazon Bedrock.
Reference:
AWS Bedrock User Guide: Inference Parameters for Text Generation (https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html) AWS AI Practitioner Learning Path: Module on Large Language Models and Inference Configuration Amazon Bedrock Developer Guide: Managing Model Outputs (https://docs.aws.amazon.com/bedrock/latest/devguide/)
NEW QUESTION # 118
An education provider is building a question and answer application that uses a generative AI model to explain complex concepts. The education provider wants to automatically change the style of the model response depending on who is asking the question. The education provider will give the model the age range of the user who has asked the question.
Which solution meets these requirements with the LEAST implementation effort?
Answer: A
Explanation:
Adding a role description to the prompt context is a straightforward way to instruct the generative AI model to adjust its response style based on the user's age range. This method requires minimal implementation effort as it does not involve additional training or complex logic.
Option B (Correct): "Add a role description to the prompt context that instructs the model of the age range that the response should target": This is the correct answer because it involves the least implementation effort while effectively guiding the model to tailor responses according to the age range.
Option A: "Fine-tune the model by using additional training data" is incorrect because it requires significant effort in gathering data and retraining the model.
Option C: "Use chain-of-thought reasoning" is incorrect as it involves complex reasoning that may not directly address the need to adjust response style based on age.
Option D: "Summarize the response text depending on the age of the user" is incorrect because it involves additional processing steps after generating the initial response, increasing complexity.
AWS AI Practitioner Reference:
Prompt Engineering Techniques on AWS: AWS recommends using prompt context effectively to guide generative models in providing tailored responses based on specific user attributes.
NEW QUESTION # 119
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 # 120
A company deploys a foundation model (FM). The company notices that the FM is producing answers to user- submitted questions about politics. The company wants to ensure that the model does not send answers to political questions to users.
Which AWS solution will meet this requirement?
Answer: C
Explanation:
The verified answer is A. Amazon Bedrock Guardrails . The requirement is to prevent a deployed foundation model from returning answers about a specific unwanted topic: politics. AWS documentation states that Amazon Bedrock Guardrails provides configurable safeguards to help you build safe generative AI applications . It also explains that guardrails provide safety and privacy controls across foundation models and help detect and filter undesirable content in user inputs and model responses. Most importantly for this question, AWS specifically identifies denied topics as a guardrail capability. With denied topics, you can define a set of topics that are undesirable in the context of your application, and the filter helps block them if they are detected in user queries or model responses. That exactly matches the scenario because the company wants political questions blocked before answers are sent to users.
Amazon Bedrock Agents is incorrect because agents are used to build applications that can reason, orchestrate tasks, call APIs, and connect to knowledge sources or action groups. Agents do not primarily exist to block topic categories in model input or output. Amazon SageMaker Clarify is incorrect because AWS describes SageMaker Clarify as a service for fairness, model explainability, feature attribution, and bias detection. It is useful for understanding model behavior and detecting bias, but it is not the correct control for filtering political responses from a generative AI application.
Amazon SageMaker Model Monitor is also incorrect because it monitors deployed model quality, data quality, bias drift, and feature attribution drift. Monitoring can identify problems after deployment, but it does not directly enforce real-time content filtering for foundation model responses. The question asks for a solution that ensures political answers are not sent to users. AWS Bedrock Guardrails is purpose-built for that control by applying safeguards to FM inputs and outputs.
NEW QUESTION # 121
A company is making a chatbot. The chatbot uses Amazon Lex and Amazon OpenSearch Service. The chatbot uses the company's private data to answer questions. The company needs to convert the data into a vector representation before storing the data in a database.
Answer: D
NEW QUESTION # 122
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