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NEW QUESTION # 318
A financial company is developing a generative AI application for loan approval decisions. The company needs the application output to be responsible and fair.
Which solution meets these requirements?
Answer: A
Explanation:
The correct answer is A because ensuring responsibility and fairness in ML begins with bias detection in the training data. Including a balanced representation of all demographics ensures the model learns fairly across different groups, which is critical in regulated industries like finance.
From AWS documentation:
"A key principle of responsible AI is building models that do not propagate or amplify bias. Fairness begins with training data. Reviewing and augmenting data for representation is essential." Explanation of other options:
B . The number of hidden layers doesn't inherently improve fairness or responsibility.
C . Keeping decisions opaque violates explainability principles in responsible AI.
D . A static dataset can become outdated and may not reflect real-world shifts, which limits fairness assessment over time.
Referenced AWS AI/ML Documents and Study Guides:
Amazon SageMaker Clarify Documentation - Bias Detection and Explainability AWS Responsible AI Guidelines AWS ML Specialty Study Guide - Fairness and Governance
NEW QUESTION # 319
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to classify the sentiment of text passages as positive or negative.
Which prompt engineering strategy meets these requirements?
Answer: D
NEW QUESTION # 320
A company is building an AI application to automate business processes. The company uses a foundation model (FM) to support the application.
The company needs to select datasets to assess the quality of the AI model ' s behavior.
Which type of datasets will meet these requirements?
Answer: B
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS Responsible AI and generative AI evaluation guidance emphasizes that assessing the quality and behavior of a foundation model requires representative and diverse evaluation datasets. These datasets should reflect real-world usage patterns, edge cases, and multiple business scenarios to properly evaluate how the model behaves in production.
Using diverse datasets that cover various use cases and usage scenarios allows organizations to:
Evaluate robustness and generalization of the FM
Identify failure modes, bias, and unsafe behavior across different inputs Validate that the model performs consistently across business workflows Why the other options are incorrect:
A may remove meaningful real-world patterns and does not reflect realistic usage.
B risks reinforcing model biases and does not provide independent evaluation.
D does not reflect real or meaningful business scenarios and can distort evaluation results.
AWS AI Study Guide References:
AWS Responsible AI evaluation practices
AWS guidance on foundation model testing and validation
NEW QUESTION # 321
Which technique breaks a complex task into smaller subtasks that are sent sequentially to a large language model (LLM)?
Answer: A
Explanation:
Prompt chaining is a technique where a complex task is broken into smaller subtasks, and the outputs of one subtask are used as inputs for the next, sequentially guiding a large language model (LLM) to solve the problem step-by-step. This method is particularly useful for complex tasks that require multiple reasoning steps.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Prompt chaining involves breaking a complex task into smaller subtasks and sequentially passing the output of one subtask as input to the next, enabling large language models to handle intricate problems by solving them step-by-step." (Source: AWS Bedrock User Guide, Prompt Engineering Techniques) Detailed Option A: One-shot promptingOne-shot prompting provides a single example to guide the LLM, but it does not break tasks into smaller subtasks or handle sequential processing.
Option B: Prompt chainingThis is the correct answer. Prompt chaining divides a complex task into smaller, manageable subtasks, solving them sequentially with the LLM, as described.
Option C: Tree of thoughtsTree of thoughts involves exploring multiple reasoning paths simultaneously, not breaking tasks into sequential subtasks.
Option D: Retrieval Augmented Generation (RAG)RAG retrieves external information to augment LLM responses but does not specifically break tasks into sequential subtasks.
Reference:
AWS Bedrock User Guide: Prompt Engineering Techniques (https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-engineering.html) AWS AI Practitioner Learning Path: Module on Generative AI Prompting Amazon Bedrock Developer Guide: Advanced Prompting Strategies (https://aws.amazon.com/bedrock/)
NEW QUESTION # 322
A software company wants to use a large language model (LLM) for workflow automation. The application will transform user messages into JSON files. The company will use the JSON files as inputs for data pipelines.
The company has a labeled dataset that contains user messages and output JSON files.
Which solution will train the LLM for workflow automation?
Answer: B
Explanation:
Fine-tuning is the process of training a pre-trained LLM with a labeled dataset specific to a desired task-in this case, mapping user messages to JSON outputs. Fine-tuning leverages supervised learning to specialize the model's outputs.
C is correct:
"Fine-tuning is a supervised learning approach in which a model is further trained on a custom, labeled dataset to adapt to a specific use case." (Reference: Amazon Bedrock Fine-Tuning, AWS Certified AI Practitioner Study Guide) A is incorrect-unsupervised learning does not use labeled data.
B (continued pre-training) uses unlabeled data.
D (RLHF) uses reward signals and human feedback, not direct labeled input/output pairs.
NEW QUESTION # 323
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