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질문 # 223
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.
정답：
설명：
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
질문 # 224
A company wants to keep its foundation model (FM) relevant by using the most recent data. The company wants to implement a model training strategy that includes regular updates to the FM.
Which solution meets these requirements?
정답：A
설명：
To keep a foundation model (FM) relevant with the most recent data, the company needs a training strategy that supports regular updates. Continuous pre-training involves periodically updating a pre-trained model with new data to improve its performance and relevance over time, making it the best fit for this requirement.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Continuous pre-training is a strategy where a pre-trained model is periodically updated with new data to keep it relevant and improve its performance. This approach is commonly used for foundation models to ensure they adapt to new trends and information." (Source: AWS AI Practitioner Learning Path, Module on Model Training Strategies) Detailed Explanation:
* Option A: Batch learningBatch learning involves training a model on a fixed dataset in batches, but it does not inherently support regular updates with new data to keep the model relevant over time.
* Option B: Continuous pre-trainingThis is the correct answer. Continuous pre-training updates the FM with recent data, ensuring it stays relevant by adapting to new trends and information.
* Option C: Static trainingStatic training implies training a model once on a fixed dataset without updates, which does not meet the requirement for regular updates.
* Option D: Latent trainingLatent training is not a standard term in AWS or ML contexts. It may refer to latent space in models like VAEs, but it is not a strategy for regular model updates.
References:
AWS AI Practitioner Learning Path: Module on Model Training Strategies
Amazon Bedrock User Guide: Model Customization and Updates (https://docs.aws.amazon.com/bedrock
/latest/userguide/custom-models.html)
AWS Documentation: Machine Learning Training Strategies (https://aws.amazon.com/machine-learning/)
질문 # 225
An AI practitioner is building a model to generate images of humans in various professions. The AI practitioner discovered that the input data is biased and that specific attributes affect the image generation and create bias in the model.
Which technique will solve the problem?
정답：D
설명：
Data augmentation for imbalanced classes is the correct technique to address bias in input data affecting image generation.
* Data Augmentation for Imbalanced Classes:
* Involves generating new data samples by modifying existing ones, such as flipping, rotating, or cropping images, to balance the representation of different classes.
* Helps mitigate bias by ensuring that the training data is more representative of diverse characteristics and scenarios.
* Why Option A is Correct:
* Balances Data Distribution: Addresses class imbalance by augmenting underrepresented classes, which reduces bias in the model.
* Improves Model Fairness: Ensures that the model is exposed to a more diverse set of training examples, promoting fairness in image generation.
* Why Other Options are Incorrect:
* B. Model monitoring for class distribution: Helps identify bias but does not actively correct it.
* C. Retrieval Augmented Generation (RAG): Involves combining retrieval and generation but is unrelated to mitigating bias in image generation.
* D. Watermark detection for images: Detects watermarks in images, not a technique for addressing bias.
질문 # 226
A company has implemented a generative AI solution to create personalized exercise routines for premium subscription users. The company offers free basic subscriptions and paid premium subscriptions.
The company wants to evaluate the AI solution's return on investment over time.
정답：A
질문 # 227
A company that uses multiple ML models wants to identify changes in original model quality so that the company can resolve any issues.
Which AWS service or feature meets these requirements?
정답：A
설명：
Amazon SageMaker Model Monitor is specifically designed to automatically detect and alert on changes in model quality, such as data drift, prediction drift, or other anomalies in model performance once deployed.
* D is correct:
"Amazon SageMaker Model Monitor continuously monitors the quality of machine learning models in production. It automatically detects concept drift, data drift, and other quality issues, enabling teams to take corrective actions." (Reference: Amazon SageMaker Model Monitor Documentation, AWS Certified AI Practitioner Study Guide)
* A (JumpStart) provides prebuilt solutions and models, not monitoring.
* B (HyperPod) is for large-scale training, not model monitoring.
* C (Data Wrangler) is for data preparation, not ongoing model quality monitoring.
질문 # 228
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