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| Section | Weight | Objectives |
|---|---|---|
| Security, Compliance, and Governance for AI Solutions | 14% | - Governance frameworks for AI lifecycle - Security controls for AI data and models - Compliance requirements and regulations - Data protection and privacy in AI workflows |
| Guidelines for Responsible AI | 14% | - Explain bias detection and reduction - Identify risks and mitigation strategies for AI systems - Describe ethical and societal impacts of AI - Define responsible AI principles: fairness, transparency, privacy, safety |
| Applications of Foundation Models | 28% | - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan - Describe use cases for text, image, video, and code generation - Explain integration of foundation models into applications - Recognize tools for building and deploying generative AI solutions |
| Fundamentals of Generative AI | 24% | - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) - Differentiate between generative AI and traditional ML - Explain capabilities and use cases of generative AI - Define generative AI and foundation models |
| 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 |
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質問 # 226
A food service company wants to collect a dataset to predict customer food preferences. The company wants to ensure that the food preferences of all demographics are included in the data.
正解：B
解説：
* Diversity in datasets ensures representation of all demographics, reducing bias and improving fairness.
* Accuracy is model performance.
* Recency bias skews towards recent data.
* Reliability measures consistency of results, not representation.
# Reference:
AWS Responsible AI Guidelines - Data Diversity
質問 # 227
An AI practitioner is developing a prompt for an Amazon Titan model. The model is hosted on Amazon Bedrock. The AI practitioner is using the model to solve numerical reasoning challenges. The AI practitioner adds the following phrase to the end of the prompt: "Ask the model to show its work by explaining its reasoning step by step." Which prompt engineering technique is the AI practitioner using?
正解：B
解説：
Chain-of-thought prompting is a prompt engineering technique where you instruct the model to explain its reasoning step by step, which is particularly useful for tasks involving logic, math, or reasoning.
A is correct: Asking the model to "explain its reasoning step by step" directly invokes chain-of-thought prompting, as documented in AWS and generative AI literature.
B is unrelated (prompt injection is a security concern).
C (few-shot) provides examples, but doesn't specifically require step-by-step reasoning.
D (templating) is about structuring the prompt format.
"Chain-of-thought prompting elicits step-by-step explanations from LLMs, which improves performance on complex reasoning tasks." (Reference: Amazon Bedrock Prompt Engineering Guide, AWS Certified AI Practitioner Study Guide)
"Chain-of-thought prompting elicits step-by-step explanations from LLMs, which improves performance on complex reasoning tasks." (Reference: Amazon Bedrock Prompt Engineering Guide, AWS Certified AI Practitioner Study Guide)
質問 # 228
A company wants more customized responses to its generative AI models' prompts.
Select the correct customization methodology from the following list for each use case. Each use case should be selected one time. (Select THREE.)
* Continued pre-training
* Data augmentation
* Model fine-tuning
正解：
解説：
Model fine-tuning adapts a pre-trained model to a specific domain or task using labeled data. This is the preferred approach when you want highly customized behavior for a particular application or subject area.
(Reference: Amazon Bedrock Fine-Tuning)
Data augmentation is used to artificially increase the size of a labeled dataset, usually by transforming or generating variations of the original data. This helps improve model generalization when labeled data is limited.
(Reference: AWS Data Preparation Techniques, AWS AI Practitioner Guide) Continued pre-training (also called domain-adaptive pre-training) further trains a foundation model on large amounts of unlabeled data from a specific domain, improving the model's language understanding or generation for that domain.
(Reference: Amazon Bedrock Customization Options)
質問 # 229
Sated and order the steps from the following bat to correctly describe the ML Lifecycle for a new custom modal Select each step one time. (Select and order FOUR.)
* Define the business objective.
* Deploy the modal.
* Develop and tram the model.
* Process the data.
正解：
解説：
Step 1: Define the business objective.
Step 2: Process the data.
Step 3: Develop and train the model.
Step 4: Deploy the model.
The correct order represents the machine learning lifecycle as defined by AWS in the Amazon SageMaker documentation and AWS Certified Machine Learning Specialty Study Guide. The lifecycle describes the sequence of tasks required to build, train, and deploy a custom ML model effectively.
From AWS documentation:
"The machine learning process begins with defining the business problem, followed by collecting and processing data, developing and training models, and finally deploying them into production for inference." Step 1 - Define the business objective:
This step involves clearly identifying the business problem to be solved and determining the measurable outcomes expected from the ML model. This ensures alignment between business goals and ML outputs.
Step 2 - Process the data:
Data is collected, cleaned, transformed, and prepared for training. This includes handling missing values, normalizing data, and performing feature engineering - a crucial phase that influences model performance.
Step 3 - Develop and train the model:
The model is built and trained on the processed data using algorithms appropriate to the problem (e.g., regression, classification, clustering). Hyperparameters are tuned to optimize model accuracy.
Step 4 - Deploy the model:
Once validated, the model is deployed to a production environment (e.g., Amazon SageMaker endpoint) to make predictions on new data. Continuous monitoring and retraining ensure the model remains effective.
Referenced AWS AI/ML Documents and Study Guides:
Amazon SageMaker Developer Guide - Machine Learning Lifecycle
AWS Certified Machine Learning Specialty Study Guide - Model Development Lifecycle AWS ML Best Practices Whitepaper - End-to-End ML Workflow
質問 # 230
A healthcare company wants to create a model to improve disease diagnostics by analyzing patient voices. The company has recorded hundreds of patient voices for this project. The company is currently filtering voice recordings according to duration and language.
正解：C
解説：
The correct answer is B - Data preprocessing, which involves cleaning, filtering, and transforming raw data before model training. AWS defines preprocessing as the step where irrelevant or poor-quality data (e.g., excessively short or misrecorded audio) is removed or normalized. The described task-filtering recordings by duration and language-is a textbook example of preprocessing since it prepares the dataset for downstream feature extraction. Feature engineering would occur afterward when converting audio signals into numerical representations (like Mel-frequency coefficients). Model training and data collection come after and before preprocessing respectively. Proper preprocessing ensures data consistency, reduces noise, and improves model accuracy, especially in audio-based healthcare models where clarity and consistency are critical.
Referenced AWS AI/ML Documents and Study Guides:
AWS Machine Learning Specialty Guide - Data Preprocessing and Cleaning
Amazon SageMaker Data Wrangler Documentation - Data Preparation Best Practices
質問 # 231
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