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選擇使用Testpdf提供的產品，你踏上了IT行業巔峰的第一步，離你的夢想更近了一步。Testpdf為你提供的測試資料不僅能幫你通過Amazon AIF-C01認證考試和鞏固你的專業知識，而且還能給你你提供一年的免費更新服務。
問題 #253
A company is building an ML model. The company collected new data and analyzed the data by creating a correlation matrix, calculating statistics, and visualizing the data.
Which stage of the ML pipeline is the company currently in?
答案：D
解題說明：
Exploratory data analysis (EDA) involves understanding the data by visualizing it, calculating statistics, and creating correlation matrices. This stage helps identify patterns, relationships, and anomalies in the data, which can guide further steps in the ML pipeline.
* Option C (Correct): "Exploratory data analysis": This is the correct answer as the tasks described (correlation matrix, calculating statistics, visualizing data) are all part of the EDA process.
* Option A: "Data pre-processing" is incorrect because it involves cleaning and transforming data, not initial analysis.
* Option B: "Feature engineering" is incorrect because it involves creating new features from raw data, not analyzing the data's existing structure.
* Option D: "Hyperparameter tuning" is incorrect because it refers to optimizing model parameters, not analyzing the data.
AWS AI Practitioner References:
* Stages of the Machine Learning Pipeline: AWS outlines EDA as the initial phase of understanding and exploring data before moving to more specific preprocessing, feature engineering, and model training stages.
問題 #254
A company is training ML models on datasets. The datasets contain some classes that have more examples than other classes. The company wants to measure how well the model balances detecting and labeling the classes.
Which metric should the company use?
答案：B
解題說明：
The correct answer is D - F1 score, which is the harmonic mean of precision and recall. AWS documentation states that the F1 score is the preferred metric when evaluating ML models trained on imbalanced datasets, where one class appears significantly more often than others. Accuracy becomes misleading in these situations because a model can achieve high accuracy simply by predicting the majority class. Precision alone measures correctness of positive predictions, while recall measures completeness-neither fully captures class balancing performance. The F1 score provides a combined measurement that reflects how well the model identifies minority classes while avoiding excessive false positives. AWS's ML Specialty Guide recommends using F1 for classification tasks where "class distribution is uneven or where both false negatives and false positives matter." This metric provides a fairer assessment of overall model performance across all class categories.
Referenced AWS Documentation:
AWS Certified Machine Learning Specialty Guide - Metrics for Imbalanced Classes Amazon SageMaker Developer Guide - Evaluating Classification Models
問題 #255
A publishing company built a Retrieval Augmented Generation (RAG) based solution to give its users the ability to interact with published content. New content is published daily. The company wants to provide a near real-time experience to users.
Which steps in the RAG pipeline should the company implement by using offline batch processing to meet these requirements? (Select TWO.)
答案：A,E
解題說明：
Comprehensive and Detailed Explanation From Exact Extract:
In a RAG (Retrieval Augmented Generation) architecture, there are steps that can be optimized using offline batch processing, particularly for operations that do not require real-time updates:
A . Generation of content embeddings:
When new content is published, it can be processed in batches to generate embeddings (vector representations) offline. These embeddings are then used at query time for similarity search. As new documents come in daily, batch processing is ideal for generating embeddings for all new content together.
"Content/document embeddings are typically generated offline, as this operation can be computationally expensive and does not need to happen in real-time." (Reference: AWS GenAI RAG Blog, Amazon Bedrock RAG Pattern) C . Creation of the search index:
After generating the content embeddings, these are indexed in a vector database or search service. This indexing is also typically performed in batch as part of the offline pipeline.
"Building or updating the vector index is often performed as a batch operation, reflecting the latest state of the content repository." (Reference: AWS RAG Pattern Whitepaper) B, D, and E are real-time steps. Embeddings for user queries (B), retrieval of relevant content (D), and response generation (E) must be processed in real-time to provide an interactive experience.
Reference:
Retrieval Augmented Generation (RAG) on AWS
Amazon Bedrock RAG Documentation
問題 #256
An ML research team develops custom ML models. The model artifacts are shared with other teams for integration into products and services. The ML team retains the model training code and data. The ML team wants to builk a mechanism that the ML team can use to audit models.
Which solution should the ML team use when publishing the custom ML models?
答案：D
解題說明：
The ML research team needs a mechanism to audit custom ML models while sharing model artifacts with other teams. Amazon SageMaker Model Cards provide a structured way todocument model details, including intended uses, training data, and inference performance, making them ideal for auditing and ensuring transparency when publishing models.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"Amazon SageMaker Model Cards enable you to document critical details about your machine learning models, such as intended uses, training data, evaluation metrics, and inference details. Model Cards support auditing by providing a centralized record that can be reviewed by teams to understand model behavior and limitations." (Source: Amazon SageMaker Developer Guide, SageMaker Model Cards) Detailed Explanation:
Option A: Create documents with the relevant information. Store the documents in Amazon S3.While storing documents in S3 is feasible, it lacks the structured format and integration with SageMaker that Model Cards provide, making it less suitable for auditing purposes.
Option B: Use AWS AI Service Cards for transparency and understanding models.AWS AI Service Cards are not a standard feature in AWS documentation. This option appears to be a distractor and is not a valid solution.
Option C: Create Amazon SageMaker Model Cards with Intended uses and training and inference details.This is the correct answer. SageMaker Model Cards are specifically designed to document model details for auditing, transparency, and collaboration, meeting the team's requirements.
Option D: Create model training scripts. Commit the model training scripts to a Git repository.Sharing training scripts in a Git repository provides access to code but does not offer a structured auditing mechanism for model details like intended uses or inference performance.
References:
Amazon SageMaker Developer Guide: SageMaker Model Cards (https://docs.aws.amazon.com/sagemaker
/latest/dg/model-cards.html)
AWS AI Practitioner Learning Path: Module on Model Governance and Auditing AWS Documentation: Responsible AI with SageMaker (https://aws.amazon.com/sagemaker/)
問題 #257
Which option is a benefit of ongoing pre-training when fine-tuning a foundation model (FM)?
答案：C
解題說明：
Ongoing pre-training when fine-tuning a foundation model (FM) improves model performance over time by continuously learning from new data.
* Ongoing Pre-Training:
* Involves continuously training a model with new data to adapt to changing patterns, enhance generalization, and improve performance on specific tasks.
* Helps the model stay updated with the latest data trends and minimize drift over time.
* Why Option B is Correct:
* Performance Enhancement: Continuously updating the model with new data improves its accuracy and relevance.
* Adaptability: Ensures the model adapts to new data distributions or domain-specific nuances.
* Why Other Options are Incorrect:
* A. Decrease model complexity: Ongoing pre-training typically enhances complexity by learning new patterns, not reducing it.
* C. Decreases training time requirement: Ongoing pre-training may increase the time needed for training.
* D. Optimizes inference time: Does not directly affect inference time; rather, it affects model performance.
問題 #258
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